From 188cf58f982761b4bc97255bfc12c9d10845caf3 Mon Sep 17 00:00:00 2001 From: Eugene Vinitsky Date: Mon, 30 Mar 2026 20:55:04 -0400 Subject: [PATCH 01/24] Merge turbostream (vcha/turbostream) into 2.0_v2 Brings in turbostream features: traffic lights, stop signs, route/waypoint system, updated observation space, transition-level PPO, symlog rewards, new Carla map binaries, MLOps tooling, notebooks, eval scripts, and submit_cluster.py from 3.0. Maps will need rebuilding. Co-Authored-By: Claude Opus 4.6 (1M context) --- .github/workflows/perf-ci.yml | 2 +- .github/workflows/pre-commit.yml | 28 + .github/workflows/render-ci.yml | 59 + .github/workflows/train-ci.yml | 4 +- .github/workflows/training-test.yml | 37 + .github/workflows/utest.yml | 4 +- .gitignore | 18 +- .pre-commit-config.yaml | 1 - README.md | 309 +- data_utils/carla/carla/Town01_0.json | 107229 +++++++++++++++ data_utils/carla/process_carla_roads.py | 338 + data_utils/carla/process_carla_xodr.py | 897 + mlops/README.md | 131 + mlops/configs/experiments.yaml | 92 + mlops/configs/smoke-tests.yaml | 174 + mlops/dockerfile/.dockerignore | 83 + mlops/dockerfile/build.dockerfile | 86 + mlops/dockerfile/launch.dockerfile | 33 + mlops/mlops.py | 719 + mlops/run_training.sh | 182 + notebooks/01_observations.ipynb | 534 + notebooks/02_rewards.ipynb | 405 + notebooks/03_metrics.ipynb | 400 + notebooks/04_training.ipynb | 625 + notebooks/05_inference.ipynb | 1682 + notebooks/06_architecture.ipynb | 912 + pufferlib/__init__.py | 7 - pufferlib/config/default.ini | 12 +- pufferlib/config/ocean/drive.ini | 444 +- pufferlib/ocean/benchmark/README.md | 67 + pufferlib/ocean/benchmark/evaluator.py | 532 +- pufferlib/ocean/benchmark/metrics.py | 6 +- .../ocean/benchmark/metrics_sanity_check.py | 9 +- .../ocean/benchmark/visual_sanity_check.py | 7 +- pufferlib/ocean/benchmark/wosac.ini | 9 +- pufferlib/ocean/drive/README.md | 108 + pufferlib/ocean/drive/binding.c | 1862 +- pufferlib/ocean/drive/datatypes.h | 351 + pufferlib/ocean/drive/drive.c | 324 +- pufferlib/ocean/drive/drive.h | 6368 +- pufferlib/ocean/drive/drive.py | 691 +- pufferlib/ocean/drive/drivenet.h | 129 +- pufferlib/ocean/drive/render.h | 791 + pufferlib/ocean/env_binding.h | 305 +- pufferlib/ocean/env_config.h | 169 +- pufferlib/ocean/environment.py | 186 +- pufferlib/ocean/torch.py | 364 +- pufferlib/pufferl.py | 1744 +- pufferlib/pytorch.py | 13 +- .../binaries/carla/opendrive__Town01.bin | Bin 0 -> 106288 bytes .../binaries/carla/opendrive__Town02.bin | Bin 0 -> 55916 bytes .../binaries/carla/opendrive__Town03.bin | Bin 0 -> 827000 bytes .../binaries/carla/opendrive__Town04.bin | Bin 0 -> 1108712 bytes .../binaries/carla/opendrive__Town05.bin | Bin 0 -> 1515196 bytes .../binaries/carla/opendrive__Town06.bin | Bin 0 -> 1041276 bytes .../binaries/carla/opendrive__Town07.bin | Bin 0 -> 395336 bytes .../binaries/carla/opendrive__Town10HD.bin | Bin 0 -> 266096 bytes .../resources/drive/puffer_drive_weights.bin | Bin 2456688 -> 2456688 bytes pufferlib/spaces.py | 13 +- pufferlib/sweep.py | 30 + pufferlib/utils.py | 255 +- pufferlib/vector.py | 41 +- pufferlib/viz.py | 2174 + scripts/build_ocean.sh | 83 +- scripts/eval/benchmark_comparison.py | 57 + scripts/eval/run_all_eval.sh | 72 + scripts/eval/run_all_latest_eval.py | 113 + scripts/eval/run_failure_scenarios.py | 101 + scripts/submit_cluster.py | 450 + setup.py | 215 +- tests/test_drive_config.py | 17 +- tests/test_drive_render.py | 116 + tests/test_drive_scenario_length.py | 6 +- tests/test_drive_train.py | 16 +- tests/test_simulator_perf.py | 2 +- 75 files changed, 128244 insertions(+), 4999 deletions(-) create mode 100644 .github/workflows/pre-commit.yml create mode 100644 .github/workflows/render-ci.yml create mode 100644 .github/workflows/training-test.yml create mode 100644 data_utils/carla/carla/Town01_0.json create mode 100644 data_utils/carla/process_carla_roads.py create mode 100644 data_utils/carla/process_carla_xodr.py create mode 100644 mlops/README.md create mode 100644 mlops/configs/experiments.yaml create mode 100644 mlops/configs/smoke-tests.yaml create mode 100644 mlops/dockerfile/.dockerignore create mode 100644 mlops/dockerfile/build.dockerfile create mode 100644 mlops/dockerfile/launch.dockerfile create mode 100644 mlops/mlops.py create mode 100755 mlops/run_training.sh create mode 100644 notebooks/01_observations.ipynb create mode 100644 notebooks/02_rewards.ipynb create mode 100644 notebooks/03_metrics.ipynb create mode 100644 notebooks/04_training.ipynb create mode 100644 notebooks/05_inference.ipynb create mode 100644 notebooks/06_architecture.ipynb create mode 100644 pufferlib/ocean/benchmark/README.md create mode 100644 pufferlib/ocean/drive/README.md create mode 100644 pufferlib/ocean/drive/datatypes.h create mode 100644 pufferlib/ocean/drive/render.h create mode 100644 pufferlib/resources/drive/binaries/carla/opendrive__Town01.bin create mode 100644 pufferlib/resources/drive/binaries/carla/opendrive__Town02.bin create mode 100644 pufferlib/resources/drive/binaries/carla/opendrive__Town03.bin create mode 100644 pufferlib/resources/drive/binaries/carla/opendrive__Town04.bin create mode 100644 pufferlib/resources/drive/binaries/carla/opendrive__Town05.bin create mode 100644 pufferlib/resources/drive/binaries/carla/opendrive__Town06.bin create mode 100644 pufferlib/resources/drive/binaries/carla/opendrive__Town07.bin create mode 100644 pufferlib/resources/drive/binaries/carla/opendrive__Town10HD.bin create mode 100644 pufferlib/viz.py create mode 100644 scripts/eval/benchmark_comparison.py create mode 100755 scripts/eval/run_all_eval.sh create mode 100644 scripts/eval/run_all_latest_eval.py create mode 100644 scripts/eval/run_failure_scenarios.py create mode 100644 scripts/submit_cluster.py create mode 100644 tests/test_drive_render.py diff --git a/.github/workflows/perf-ci.yml b/.github/workflows/perf-ci.yml index 083a97a827..fd6f76553a 100644 --- a/.github/workflows/perf-ci.yml +++ b/.github/workflows/perf-ci.yml @@ -2,7 +2,7 @@ name: perf-ci on: pull_request: - branches: [ 2.0 ] + branches: [ main ] jobs: test-performance: diff --git a/.github/workflows/pre-commit.yml b/.github/workflows/pre-commit.yml new file mode 100644 index 0000000000..cb7b20e1bc --- /dev/null +++ b/.github/workflows/pre-commit.yml @@ -0,0 +1,28 @@ +name: Pre-commit + +on: + pull_request: + types: + - opened + - reopened + - synchronize + - ready_for_review + +jobs: + pre-commit: + name: pre-commit + runs-on: ubuntu-latest + steps: + - name: Checkout code + uses: actions/checkout@v4 + + - name: Setup Python + uses: actions/setup-python@v5 + with: + python-version: "3.11" + + - name: Install pre-commit + run: python -m pip install -U pip pre-commit + + - name: Run pre-commit + run: pre-commit run --all-files --show-diff-on-failure diff --git a/.github/workflows/render-ci.yml b/.github/workflows/render-ci.yml new file mode 100644 index 0000000000..9634af71d2 --- /dev/null +++ b/.github/workflows/render-ci.yml @@ -0,0 +1,59 @@ +name: render-ci + +on: + pull_request: + branches: [ main ] + push: + branches: [ main ] + +jobs: + test-render: + name: test-render - ubuntu-latest + runs-on: ubuntu-latest + + steps: + - name: Checkout code + uses: actions/checkout@v3 + + - name: Setup Python + uses: actions/setup-python@v4 + with: + python-version: "3.11" + + - name: Check disk space (before cleanup) + run: df -h + + - name: Free up disk space + run: | + echo "Before cleanup:" + df -h + sudo rm -rf /usr/share/dotnet /usr/local/lib/android /opt/ghc + sudo apt-get clean + sudo rm -rf ~/.cache/pip /tmp/* /var/tmp/* + echo "After cleanup:" + df -h + + - name: Install system dependencies for headless rendering + run: | + sudo apt update + sudo apt install -y ffmpeg xvfb build-essential + + - name: Upgrade pip + run: python -m pip install -U pip + + - name: Install pufferlib + run: | + pip install -e .[cpu] --no-cache-dir + env: + TMPDIR: ${{ runner.temp }}/build + PIP_NO_CACHE_DIR: 1 + + - name: Compile C extensions + run: python setup.py build_ext --inplace --force + + - name: Check disk space (after install) + run: df -h + + - name: Run render test + run: python tests/test_drive_render.py + timeout-minutes: 15 diff --git a/.github/workflows/train-ci.yml b/.github/workflows/train-ci.yml index cad2613d93..bd5b377766 100644 --- a/.github/workflows/train-ci.yml +++ b/.github/workflows/train-ci.yml @@ -2,9 +2,9 @@ name: train-ci on: pull_request: - branches: [ 2.0 ] + branches: [ main ] push: - branches: [ 2.0 ] + branches: [ main ] jobs: test-training: diff --git a/.github/workflows/training-test.yml b/.github/workflows/training-test.yml new file mode 100644 index 0000000000..deb4ad3882 --- /dev/null +++ b/.github/workflows/training-test.yml @@ -0,0 +1,37 @@ +name: Training Test + +on: + pull_request: + types: + - closed + branches: [ develop ] + +jobs: + run-training-test: + name: Run Training Test + if: github.event.pull_request.merged == true + runs-on: ubuntu-latest + + steps: + - name: Checkout code + uses: actions/checkout@v3 + + - name: Authenticate to Google Cloud + uses: 'google-github-actions/auth@v2' + with: + credentials_json: '${{ secrets.GCP_CREDENTIALS }}' + + - name: Configure Docker for GCP Artifact Registry + run: gcloud auth configure-docker europe-west4-docker.pkg.dev + + - name: Setup Python + uses: actions/setup-python@v4 + with: + python-version: "3.11" + + - name: Install dependencies for mlops.py + run: pip install pyyaml google-cloud-aiplatform + + - name: Run training test + run: python mlops/mlops.py train-test --verbose + timeout-minutes: 40 diff --git a/.github/workflows/utest.yml b/.github/workflows/utest.yml index 5a0626a051..cfcbdda4b7 100644 --- a/.github/workflows/utest.yml +++ b/.github/workflows/utest.yml @@ -2,9 +2,9 @@ name: Unit tests on: pull_request: - branches: [ 2.0 ] + branches: [ main ] push: - branches: [ 2.0 ] + branches: [ main ] jobs: test: diff --git a/.gitignore b/.gitignore index 2bd9405687..aa3da57485 100644 --- a/.gitignore +++ b/.gitignore @@ -141,13 +141,13 @@ dmypy.json # Pyre type checker .pyre/ .pylintrc -.vscode/ # ROMs *.gb checkpoints/ experiments/ +benchmark*/ wandb/ .neptune/ raylib*/ @@ -163,14 +163,9 @@ pufferlib/ocean/impulse_wars/benchmark/ # Ignore data files data/ -pufferlib/resources/drive/binaries/ -pufferlib/resources/drive/binaries/training/ -pufferlib/resources/drive/binaries/validation/ - -# But keep map_000.bin for the training test -!pufferlib/resources/drive/binaries/map_000.bin -!pufferlib/resources/drive/binaries/training/map_000.bin -pufferlib/resources/drive/sanity/sanity_binaries/ +pufferlib/resources/drive/binaries/*/ +!pufferlib/resources/drive/binaries/carla/ +!pufferlib/resources/drive/binaries/carla/** # Compiled drive binary in root /drive @@ -187,6 +182,5 @@ pufferlib/resources/drive/output_agent.gif pufferlib/resources/drive/output.gif artifacts/ # Local drive renders pufferlib/resources/drive/output*.gif -emsdk/ -docs/book/* -!docs/book/assets/ +*.mp4 +.vscode/ diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 74e29d4620..d286db0404 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -26,4 +26,3 @@ repos: rev: 'v21.1.5' hooks: - id: clang-format - exclude: '\.(js|html)$|^build_web/|^docs/assets/' diff --git a/README.md b/README.md index 0b431e7169..fdb7786288 100644 --- a/README.md +++ b/README.md @@ -1,218 +1,139 @@ # PufferDrive -[![Unit Tests](https://github.com/Emerge-Lab/PufferDrive/actions/workflows/utest.yml/badge.svg)](https://github.com/Emerge-Lab/PufferDrive/actions/workflows/utest.yml) +MARL autonomous driving RL environment. C simulation engine (GigaFlow/Waymo replay) + Python/PyTorch PPO training loop with V-trace and priority sampling. - +## Install -**PufferDrive is a fast and friendly driving simulator to train and test RL-based models.** - -
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- ---- - -**Docs**: https://emerge-lab.github.io/PufferDrive - ---- - -### See our 2.0 release video - - - PufferDrive 2.0 - - -## Installation - -Clone the repo ```bash -https://github.com/Emerge-Lab/PufferDrive.git -``` - -Make a venv (`uv venv`), activate the venv -``` -source .venv/bin/activate -``` - -Inside the venv, install the dependencies -``` +# Python env +uv venv && source .venv/bin/activate uv pip install -e . -``` -Compile the C code -``` +# Build C extensions (required after any .h/.c change) python setup.py build_ext --inplace --force ``` -Run this while your virtual environment is active so the extension is built against the right interpreter. - -To test your setup, you can run -``` -puffer train puffer_drive -``` -See also the [puffer docs](https://puffer.ai/docs.html). - - -## Quick start - -Start a training run -``` -puffer train puffer_drive -``` - -## Dataset - -
-Downloading and using data - -### Data preparation - -To train with PufferDrive, you need to convert JSON files to map binaries. Run the following command with the path to your data folder: - -```bash -python pufferlib/ocean/drive/drive.py -``` - -### Downloading Waymo Data - -You can download the WOMD data from Hugging Face in two versions: - -- **Mini dataset**: [GPUDrive_mini](https://huggingface.co/datasets/EMERGE-lab/GPUDrive_mini) contains 1,000 training files and 300 test/validation files -- **Medium dataset**: [10,000 files from the training dataset](https://huggingface.co/datasets/daphne-cornelisse/pufferdrive_train) -- **Large dataset**: [GPUDrive](https://huggingface.co/datasets/EMERGE-lab/GPUDrive) contains 100,000 unique scenes - -**Note**: Replace 'GPUDrive_mini' with 'GPUDrive' in your download commands if you want to use the full dataset. - -### Additional Data Sources - -For more training data compatible with PufferDrive, see [ScenarioMax](https://github.com/valeoai/ScenarioMax). The GPUDrive data format is fully compatible with PufferDrive. -
- - -## Visualizer - -
-Dependencies and usage - -## Local rendering - -To launch an interactive renderer, first build: -``` -bash scripts/build_ocean.sh drive local -``` -then launch: -```bash -./drive -``` -this will run `demo()` with an existing model checkpoint. +## Data -## Headless server setup +Place binaries under `pufferlib/resources/drive/binaries/`. -Run the Raylib visualizer on a headless server and export as .mp4. This will rollout the pre-trained policy in the env. - -### Install dependencies +## Train ```bash -sudo apt update -sudo apt install ffmpeg xvfb -``` - -For HPC (There are no root privileges), so install into the conda environment -```bash -conda install -c conda-forge xorg-x11-server-xvfb-cos6-x86_64 -conda install -c conda-forge ffmpeg -``` - -- `ffmpeg`: Video processing and conversion -- `xvfb`: Virtual display for headless environments - -### Build and run +# Single node +puffer train puffer_drive -1. Build the application: -```bash -bash scripts/build_ocean.sh visualize local -``` +# Override config on the fly +puffer train puffer_drive --train.learning_rate 0.001 --env.num_agents 512 -2. Run with virtual display: -```bash -xvfb-run -s "-screen 0 1280x720x24" ./visualize +# Multi-GPU +torchrun --standalone --nnodes=1 --nproc-per-node=6 -m pufferlib.pufferl train puffer_drive ``` -The `-s` flag sets up a virtual screen at 1280x720 resolution with 24-bit color depth. - ---- - -> To force a rebuild, you can delete the cached compiled executable binary using `rm ./visualize`. - ---- - -
+## Eval - -## Benchmarks - -### Distributional realism - -We provide a PufferDrive implementation of the [Waymo Open Sim Agents Challenge (WOSAC)](https://waymo.com/open/challenges/2025/sim-agents/) for fast, easy evaluation of how well your trained agent matches distributional properties of human behavior. See documentation [here](https://emerge-lab.github.io/PufferDrive/wosac/). - -WOSAC evaluation with random policy: ```bash -puffer eval puffer_drive --eval.wosac-realism-eval True -``` - -WOSAC evaluation with your checkpoint (must be .pt file): +# Multi-scenario eval (replay mode) +puffer eval_multi_scenarios puffer_drive \ + --load-model-path experiments/puffer_drive_177193887946/models/model_puffer_drive_000001.pt \ + --num_scenarios 250 --eval_simulation replay + +# Multi-scenario eval (gigaflow mode) +puffer eval_multi_scenarios puffer_drive \ + --load-model-path experiments/puffer_drive_177193887946/models/model_puffer_drive_000001.pt \ + --num_scenarios 10 --eval_simulation gigaflow + +# Multi-scenario eval with rendering +puffer eval_multi_scenarios_render puffer_drive \ + --load-model-path experiments/puffer_drive_177193887946/models/model_puffer_drive_000001.pt \ + --num_scenarios 10 --eval_simulation gigaflow --render 1 --render_obs 0 + +# Save eval as GIF +puffer eval_multi_scenarios_render puffer_drive \ + --load-model-path experiments/puffer_drive_177193887946/models/model_puffer_drive_000001.pt \ + --num_scenarios 5 --eval_simulation gigaflow --save-frames 1 --gif-path eval.gif --fps 15 +``` + +## Key Configuration (`pufferlib/config/ocean/drive.ini`) + +### `[env]` — Simulation + +| Parameter | Default | Notes | +|-----------|---------|-------| +| `simulation_mode` | `"gigaflow"` | `"gigaflow"` (procedural) or `"replay"` (Data-driven) | +| `num_agents` | `1024` | Total agents across all workers | +| `min/max_agents_per_env` | `10 / 80` | Per-env agent count range | +| `action_type` | `"discrete"` | `"discrete"`, `"continuous"`, `"trajectory"`, `"trajectory_frenet"`, `"trajectory_jerk"` | +| `dynamics_model` | `"jerk"` | `"classic"` or `"jerk"` | +| `scenario_length` | `128` | Steps per episode (128 GF, 91 replay) | +| `collision_behavior` | `1` | `0` ignore, `1` stop, `2` remove | +| `offroad_behavior` | `1` | Same options | +| `traffic_light_behavior` | `1` | Same options | +| `control_mode` | `"control_vehicles"` | `"control_vehicles"`, `"control_agents"`, `"control_sdc_only"` | +| `reward_conditioning` | `True` | Condition policy on reward weights | +| `reward_randomization` | `True` | Randomize reward weights each episode | + +### `[env]` — Reward Shaping + +| Parameter | Default | Effect | +|-----------|---------|--------| +| `reward_goal` | `1.0` | Goal reaching (set 0 without reward conditioning) | +| `reward_vehicle_collision` | `1.0` | Collision penalty | +| `reward_comfort` | `0.05` | Smooth driving | +| `reward_lane_align` | `0.025` | Lane heading alignment | +| `reward_vel_align` | `1.0` | Speed matching road limit | +| `reward_lane_center` | `0.0038` | Lane centering | +| `reward_traffic_light_violation` | `1.0` | Red light penalty | +| `reward_overspeed` | `0.05` | Speeding penalty | + +### `[train]` — PPO + +| Parameter | Default | Notes | +|-----------|---------|-------| +| `learning_rate` | `0.001` | Adam LR | +| `bptt_horizon` | `64` | BPTT window (64 GF, 91 replay) | +| `minibatch_size` | `4096` | | +| `gamma` | `0.98` | Discount | +| `gae_lambda` | `0.95` | GAE lambda | +| `vf_coef` | `2` | Value loss weight | +| `ent_coef` | `0.001` | Entropy bonus | +| `vtrace_rho_clip` / `vtrace_c_clip` | `1` | V-trace IS ratio clipping | +| `prio_alpha` / `prio_beta0` | `0.85` | Priority sampling exponents | + +### `[policy]` — Network + +| Parameter | Default | Notes | +|-----------|---------|-------| +| `backbone_hidden_size` | `512` | | +| `split_network` | `False` | GigaFlow network vs LSTM | +| `encoder_gigaflow` | `False` | | +| `dropout` | `0.0` | | + +## Notebooks + +| Notebook | Purpose | +|----------|---------| +| `01_observations.ipynb` | Verify obs vector packing, normalization, interpretability | +| `02_rewards.ipynb` | Reward magnitudes, component breakdown, correlation with behavior | +| `03_metrics.ipynb` | Episode metrics, `vec_log` aggregation, episode boundary handling | +| `04_training.ipynb` | End-to-end data flow: env → policy → loss; encoding, sampling, advantages, gradients | +| `05_inference.ipynb` | Config loading, policy forward pass, rollouts (det. vs stochastic), value accuracy, LSTM state | +| `06_architecture.ipynb` | Model summary, per-encoder breakdown, forward pass shape tracing, weight distributions | + + +## Debug + +**C build issues** ```bash -puffer eval puffer_drive --eval.wosac-realism-eval True --load-model-path .pt -``` - -### Human-compatibility - -You may be interested in how compatible your agent is with human partners. For this purpose, we support an eval where your policy only controls the self-driving car (SDC). The rest of the agents in the scene are stepped using the logs. While it is not a perfect eval since the human partners here are static, it will still give you a sense of how closely aligned your agent's behavior is to how people drive. You can run it like this: -```bash -puffer eval puffer_drive --eval.human-replay-eval True --load-model-path .pt -``` - -## Development - -
Documentation and browser demo - -**Docs** - -A browsable documentation site now lives under `docs/` and is configured with mkbooks. To preview locally: -``` -brew install mdbook -mdbook serve --open docs -``` -Open the served URL to see a local version of the docs. - -**Interactive demo** - -To edit the browser demo, follow these steps: -- Download [emscripten](https://github.com/emscripten-core/emscripten) -- emscripten install latest -- Activate: `source emsdk/emsdk_env.sh` -- Run `bash scripts/build_ocean.sh drive web` -- This generates a number of `game*` files, move them to `assets/` to include them on the webpage - -
- +# Verbose build with AddressSanitizer +DEBUG=1 python setup.py build_ext --inplace --force -## Citation +# Skip unrelated extensions +NO_OCEAN=1 python setup.py build_ext --inplace --force +NO_TRAIN=1 python setup.py build_ext --inplace --force -If you use PufferDrive in your research, please cite: -```bibtex -@software{pufferdrive2025github, - author = {Daphne Cornelisse* and Spencer Cheng* and Pragnay Mandavilli and Julian Hunt and Kevin Joseph and Waël Doulazmi and Valentin Charraut and Aditya Gupta and Joseph Suarez and Eugene Vinitsky}, - title = {{PufferDrive}: A Fast and Friendly Driving Simulator for Training and Evaluating {RL} Agents}, - url = {https://github.com/Emerge-Lab/PufferDrive}, - version = {2.0.0}, - year = {2025}, -} +# Debug train +CUDA_VISIBLE_DEVICES=None LD_PRELOAD=$(gcc -print-file-name=libasan.so) python -m pufferlib.pufferl train puffer_drive --train.device cpu --vec.backend Serial +# or +gdb --args python -m pufferlib.pufferl train puffer_drive --train.device cpu --vec.backend Serial ``` diff --git a/data_utils/carla/carla/Town01_0.json b/data_utils/carla/carla/Town01_0.json new file mode 100644 index 0000000000..21b0fbec47 --- /dev/null +++ b/data_utils/carla/carla/Town01_0.json @@ -0,0 +1,107229 @@ +{ + "name": "Town01", + "scenario_id": "N0KHWCZZgZhX", + "objects": [ + { + "position": [ + { + "x": 92.37733991874609, + "y": -48.26687711506169, + "z": 1.0 + }, + { + "x": 92.37727862621773, + "y": -47.863670458684396, + "z": 1.0 + }, + { + "x": 92.37720201055727, + "y": -47.359662138212784, + "z": 1.0 + }, + { + "x": 92.37711007176472, + "y": -46.75485215364684, + "z": 1.0 + }, + { + "x": 92.37704877923635, + "y": -46.35164549726926, + "z": 1.0 + }, + { + "x": 92.37701813297217, + "y": -46.15004216908089, + "z": 1.0 + }, + { + "x": 92.37694183367158, + "y": -45.648114989838035, + "z": 1.0 + }, + { + "x": 92.37688083639642, + "y": -45.24685062665783, + "z": 1.0 + }, + { + "x": 92.3767893404837, + "y": -44.644954081888244, + 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LANE_UNDEFINED = 0 + LANE_FREEWAY = 1 + LANE_SURFACE_STREET = 2 + LANE_BIKE_LANE = 3 + # Original definition skips 4 + ROAD_LINE_UNKNOWN = 5 + ROAD_LINE_BROKEN_SINGLE_WHITE = 6 + ROAD_LINE_SOLID_SINGLE_WHITE = 7 + ROAD_LINE_SOLID_DOUBLE_WHITE = 8 + ROAD_LINE_BROKEN_SINGLE_YELLOW = 9 + ROAD_LINE_BROKEN_DOUBLE_YELLOW = 10 + ROAD_LINE_SOLID_SINGLE_YELLOW = 11 + ROAD_LINE_SOLID_DOUBLE_YELLOW = 12 + ROAD_LINE_PASSING_DOUBLE_YELLOW = 13 + ROAD_EDGE_UNKNOWN = 14 + ROAD_EDGE_BOUNDARY = 15 + ROAD_EDGE_MEDIAN = 16 + STOP_SIGN = 17 + CROSSWALK = 18 + SPEED_BUMP = 19 + DRIVEWAY = 20 # New womd datatype in v1.2.0: Driveway entrances + UNKNOWN = -1 + NUM_TYPES = 21 + + +def save_lane_section_to_json(xodr_json, id, road_edges, road_lines, lanes, sidewalks=[]): + roads = xodr_json.get("roads", []) + for road_edge in road_edges: + # edge_polygon = Polygon(road_edge) + edge_data = { + "id": id, + "map_element_id": int(MapType.ROAD_EDGE_BOUNDARY), + "type": "road_edge", + "geometry": [{"x": float(pt[0]), "y": float(pt[1]), "z": 0.0} for pt in road_edge], + } + roads.append(edge_data) + id += 1 + for road_line in road_lines: + line_data = { + "id": id, + "map_element_id": int(MapType.ROAD_LINE_BROKEN_SINGLE_WHITE), + "type": "road_line", + "geometry": [{"x": float(pt[0]), "y": float(pt[1]), "z": 0.0} for pt in road_line], + } + roads.append(line_data) + id += 1 + for lane in lanes: + lane_data = { + "id": id, + "map_element_id": int(MapType.LANE_SURFACE_STREET), + "type": "lane", + "geometry": [{"x": float(pt[0]), "y": float(pt[1]), "z": 0.0} for pt in lane], + } + roads.append(lane_data) + id += 1 + # for sidewalk in sidewalks: + # sidewalk_data = { + # "id": id, + # "map_element_id": int(MapType.LANE_BIKE_LANE), + # "type": "sidewalk", + # "geometry": [{"x": float(pt[0]), "y": float(pt[1]), "z": 0.0} for pt in sidewalk] + # } + # roads.append(sidewalk_data) + # id += 1 + xodr_json["roads"] = roads + return id + + +def get_lane_data(lane, type="BOUNDARY", check_dir=True): + if type == "BOUNDARY": + points = lane.boundary_line + # print(f'Number of boundary pts: {len(points)}') + elif type == "CENTERLINE": + points = lane.centre_line + # print(f'Number of centerline pts: {len(points)}') + else: + raise ValueError(f"Unknown lane data type: {type}") + + if not check_dir: + return points + + # Check traffic direction + travel_dir = None + vector_lane = lane.lane_xml.find(".//userData/vectorLane") + if vector_lane is not None: + travel_dir = vector_lane.get("travelDir") + + if travel_dir == "backward": + # Reverse points for backward travel + points = points[::-1] + + return points + + +def sum_pts(road_elts): + road_geometries = [len(elt) for elt in road_elts] + return sum(road_geometries) + + +def create_empty_json(town_name): + def random_string(length=8): + return "".join(random.choices(string.ascii_letters + string.digits, k=length)) + + json_data = { + "name": town_name, + "scenario_id": random_string(12), + "objects": [], + "roads": [], + "tl_states": {}, + "metadata": {"sdc_track_index": 0, "tracks_to_predict": [], "objects_of_interest": []}, + } + return json_data + + +def generate_carla_road( + town_name, source_dir, carla_map_dir, resolution, dest_dir, max_samples, print_number_of_sample_truncations +): + src_file_path = os.path.join(source_dir, f"{town_name}.json") + dst_file_path = os.path.join(dest_dir, f"{town_name}.json") + if not os.path.isfile(src_file_path): + print(f"Warning: {src_file_path} does not exist, creating empty file.") + empty_json = create_empty_json(town_name) + with open(src_file_path, "w") as f: + json.dump(empty_json, f, indent=2) + + with open(src_file_path, "r") as f: + xodr_json = json.load(f) + xodr_json["roads"] = [] + + with open(dst_file_path, "w") as f: + json.dump(xodr_json, f, indent=2) + + odr_file = os.path.join(carla_map_dir, town_name + ".xodr") + + road_network = RoadNetwork(xodr_file_path=odr_file, resolution=resolution, max_samples=max_samples) + roads = road_network.get_roads() + print(f"Number of roads in the network: {len(roads)}") + # print(f"Type: {type(roads[0])}\nRoads: {roads}") + print(f"Number of lanes in the network: {sum([sum([len(ls.lanes) for ls in r.lane_sections]) for r in roads])}") + print( + f"Road 0 lane 1 boundary_pts: {len(roads[0].lane_sections[0].lanes[1].boundary_line)}" + ) # Sanity check to see if resolution is working + + # Go only till last "driving" lane("parking" NTD) + # "median" lane means a road edge(add after all of them appear) + # Add "sidewalk" lane as well + + id = 0 + roads_json_cnt = [[], [], []] + print(f"Network has {len(roads)} roads.") + for road_obj in roads: + # print(f"Road ID: {road_obj.id}") + lane_sections = road_obj.lane_sections + # print(f"Lane Sections: {lane_sections}") + for lane_section in lane_sections: + # print(f"Lane Section ID: {lane_section.lane_section_ordinal}") + # print(f"Number of Left Lanes: {len(lane_section.left_lanes)}") + # print(f"Number of Right Lanes: {len(lane_section.right_lanes)}") + road_edges = [] + road_lines = [] + lanes = [] + # sidwalks = [] + + left_immediate_driveable = False + right_immediate_driveable = False + + # Left Lanes + add_lane_data = False + add_edge_data = False + previous_lane = None + for i, left_lane in enumerate(lane_section.left_lanes): + if left_lane.type == "driving" or left_lane.type == "parking": + if i == 0: + left_immediate_driveable = True + + if add_lane_data: + road_line_data = get_lane_data(previous_lane, "BOUNDARY") + road_line_data = road_line_data + road_lines.append(road_line_data) + lanes.append(get_lane_data(previous_lane, "CENTERLINE")) + # Add outer edge as road edge + elif add_edge_data: + road_edges.append(get_lane_data(previous_lane, "BOUNDARY")) + add_lane_data = True + add_edge_data = False + else: + # Add inner lane as road edge + if add_lane_data and i != 0: + lanes.append(get_lane_data(previous_lane, "CENTERLINE")) + road_edges.append(get_lane_data(previous_lane, "BOUNDARY")) + add_edge_data = True + add_lane_data = False + previous_lane = left_lane + + if add_lane_data: + lanes.append(get_lane_data(previous_lane, "CENTERLINE")) + road_edges.append(get_lane_data(previous_lane, "BOUNDARY")) + # elif add_edge_data: + # if previous_lane.type == 'sidewalk': + # sidwalks.append(get_lane_data(previous_lane, "BOUNDARY")) + + # print("LEFT STATS") + # print(f"Number of Road edges: {len(road_edges)}") + # print(f"Road lines: {len(road_lines)}") + # print(f"Lanes: {len(lanes)}") + # print(f"Sidewalks: {len(sidwalks)}") + + # Right Lanes + add_lane_data = False + add_edge_data = False + previous_lane = None + for i, right_lane in enumerate(lane_section.right_lanes): + if right_lane.type == "driving" or right_lane.type == "parking": + if i == 0: + right_immediate_driveable = True + + if add_lane_data: + road_line_data = get_lane_data(previous_lane, "BOUNDARY") + road_line_data = road_line_data + road_lines.append(road_line_data) + lanes.append(get_lane_data(previous_lane, "CENTERLINE")) + # Add outer edge as road edge + elif add_edge_data: + road_edges.append(get_lane_data(previous_lane, "BOUNDARY")) + add_lane_data = True + add_edge_data = False + else: + # Add inner lane as road edge + if add_lane_data and i != 0: + lanes.append(get_lane_data(previous_lane, "CENTERLINE")) + road_edges.append(get_lane_data(previous_lane, "BOUNDARY")) + add_edge_data = True + add_lane_data = False + previous_lane = right_lane + + if add_lane_data: + lanes.append(get_lane_data(previous_lane, "CENTERLINE")) + road_edges.append(get_lane_data(previous_lane, "BOUNDARY")) + # elif add_edge_data: + # if previous_lane.type == 'sidewalk': + # sidwalks.append(get_lane_data(previous_lane, "BOUNDARY")) + + # print(f"Number of Road edges in {road_obj.id}: {len(road_edges)}") + # print(f"Road lines in {road_obj.id}: {len(road_lines)}") + # print(f"Lanes in {road_obj.id}: {len(lanes)}") + # print(f"Sidewalks in {road_obj.id}: {len(sidwalks)}") + + # If atleast one side has no immediate driveable lane add center as road edge + if not left_immediate_driveable or not right_immediate_driveable: + road_edges.append(lane_section.lane_section_reference_line) + else: + road_line_data = get_lane_data(previous_lane, "BOUNDARY") + road_lines.append(road_line_data) + + if len(road_lines) == 0 and len(lanes) == 0: + road_edges = [] + id = save_lane_section_to_json(xodr_json, id, road_edges, road_lines, lanes) + roads_json_cnt[0].append(len(road_edges)) + roads_json_cnt[1].append(len(road_lines)) + roads_json_cnt[2].append(len(lanes)) + # if len(lanes) == 0 and len(road_lines) != 0: + # print(f"Road: {road_obj.id}, Lane Section: {lane_section.lane_section_ordinal}") + # print(f"Road edges: {len(road_edges)}, Road lines: {len(road_lines)}, Lanes: {len(lanes)}") + # break + # break + print(f"Total roads JSON count: {sum(roads_json_cnt[0]) + sum(roads_json_cnt[1]) + sum(roads_json_cnt[2])}") + + # Save to file + with open(dst_file_path, "w") as f: + json.dump(xodr_json, f, indent=2) + + # Print logs + if print_number_of_sample_truncations: + road_network.print_logs_max_samples_hit() + + +def generate_carla_roads( + town_names, source_dir, carla_map_dir, resolution, dest_dir, max_samples, print_number_of_sample_truncations +): + if type(resolution) == float: + resolution = [resolution] * len(town_names) + elif type(resolution) != list: + raise ValueError("Resolution must be a float or a list type") + elif len(resolution) != len(town_names): + raise ValueError("Resolution must be of the same length as town_names.") + for i, town in enumerate(town_names): + print(f"Processing town: {town}") + generate_carla_road( + town, + source_dir, + carla_map_dir, + resolution[i], + dest_dir, + max_samples, + print_number_of_sample_truncations=print_number_of_sample_truncations, + ) + + +if __name__ == "__main__": + town_names = ["Town01", "Town02", "Town03", "Town04", "Town05", "Town06", "Town07", "Town10HD"] + source_dir = "data_utils/carla" + dest_dir = "data_utils/carla" + carla_map_dir = "/scratch/pm3881/Carla-0.10.0-Linux-Shipping/CarlaUnreal/Content/Carla/Maps/OpenDrive" + resolution = 1.0 # [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5] # Meters + max_samples = int(1e5) # Max points to sample per reference line + print_number_of_sample_truncations = True # Enable to see the number of data points lost + generate_carla_roads( + town_names, source_dir, carla_map_dir, resolution, dest_dir, max_samples, print_number_of_sample_truncations + ) + # resolution = 1.0 + # town_name = 'town06' + # generate_carla_road( + # town_name, + # source_dir, + # carla_map_dir, + # resolution, + # dest_dir, + # max_samples, + # print_number_of_sample_truncations + # ) diff --git a/data_utils/carla/process_carla_xodr.py b/data_utils/carla/process_carla_xodr.py new file mode 100644 index 0000000000..0c11b8c1b4 --- /dev/null +++ b/data_utils/carla/process_carla_xodr.py @@ -0,0 +1,897 @@ +import sys +import os +import json +import numpy as np +import random +from lxml import etree +import pyxodr +from pyxodr.road_objects.road import Road +from pyxodr.road_objects.lane import Lane, ConnectionPosition, LaneOrientation, TrafficOrientation +from pyxodr.road_objects.junction import Junction +from pyxodr.road_objects.lane_section import LaneSection +from pyxodr.road_objects.network import RoadNetwork +from shapely.geometry import Polygon +from enum import IntEnum + + +class MapType(IntEnum): + LANE_UNDEFINED = 0 + LANE_FREEWAY = 1 + LANE_SURFACE_STREET = 2 + LANE_BIKE_LANE = 3 + # Original definition skips 4 + ROAD_LINE_UNKNOWN = 5 + ROAD_LINE_BROKEN_SINGLE_WHITE = 6 + ROAD_LINE_SOLID_SINGLE_WHITE = 7 + ROAD_LINE_SOLID_DOUBLE_WHITE = 8 + ROAD_LINE_BROKEN_SINGLE_YELLOW = 9 + ROAD_LINE_BROKEN_DOUBLE_YELLOW = 10 + ROAD_LINE_SOLID_SINGLE_YELLOW = 11 + ROAD_LINE_SOLID_DOUBLE_YELLOW = 12 + ROAD_LINE_PASSING_DOUBLE_YELLOW = 13 + ROAD_EDGE_UNKNOWN = 14 + ROAD_EDGE_BOUNDARY = 15 + ROAD_EDGE_MEDIAN = 16 + STOP_SIGN = 17 + CROSSWALK = 18 + SPEED_BUMP = 19 + DRIVEWAY = 20 # New womd datatype in v1.2.0: Driveway entrances + UNKNOWN = -1 + NUM_TYPES = 21 + + +def save_lane_section_to_json(xodr_json, id, road_edges, road_lines, lanes, sidewalks=[]): + roads = xodr_json.get("roads", []) + for road_edge in road_edges: + # edge_polygon = Polygon(road_edge) + edge_data = { + "id": id, + "map_element_id": int(MapType.ROAD_EDGE_BOUNDARY), + "type": "road_edge", + "geometry": [{"x": float(pt[0]), "y": float(pt[1]), "z": 0.0} for pt in road_edge], + } + roads.append(edge_data) + id += 1 + for road_line in road_lines: + line_data = { + "id": id, + "map_element_id": int(MapType.ROAD_LINE_BROKEN_SINGLE_WHITE), + "type": "road_line", + "geometry": [{"x": float(pt[0]), "y": float(pt[1]), "z": 0.0} for pt in road_line], + } + roads.append(line_data) + id += 1 + for lane in lanes: + lane_data = { + "id": id, + "map_element_id": int(MapType.LANE_SURFACE_STREET), + "type": "lane", + "geometry": [{"x": float(pt[0]), "y": float(pt[1]), "z": 0.0} for pt in lane], + } + roads.append(lane_data) + id += 1 + # for sidewalk in sidewalks: + # sidewalk_data = { + # "id": id, + # "map_element_id": int(MapType.LANE_BIKE_LANE), + # "type": "sidewalk", + # "geometry": [{"x": float(pt[0]), "y": float(pt[1]), "z": 0.0} for pt in sidewalk] + # } + # roads.append(sidewalk_data) + # id += 1 + xodr_json["roads"] = roads + return id + + +def get_lane_data(lane, type="BOUNDARY", check_dir=True): + if type == "BOUNDARY": + points = lane.boundary_line + elif type == "CENTERLINE": + points = lane.centre_line + else: + raise ValueError(f"Unknown lane data type: {type}") + + if not check_dir: + return points + + # Check traffic direction + travel_dir = None + vector_lane = lane.lane_xml.find(".//userData/vectorLane") + if vector_lane is not None: + travel_dir = vector_lane.get("travelDir") + + if travel_dir == "backward": + # Reverse points for backward travel + points = points[::-1] + + return points + + +class RoadLinkObject: + def __init__(self, road_id, pred_lane_section_ids, succ_lane_section_ids): + self.road_id = road_id + self.pred_lane_section_ids = pred_lane_section_ids + self.succ_lane_section_ids = succ_lane_section_ids + self.lane_links_map = {} # map from (lane_id, lane_section_index) to LaneLinkObject + self.predecessor_roads = [] + self.successor_roads = [] + + +class LaneLinkObject: + def __init__(self, lane_id, lane_section_index, road_id, lane, lane_centerpoints, forward_dir, is_junction): + self.lane_id = lane_id + self.lane_section_index = lane_section_index + self.road_id = road_id + self.lane = lane + self.lane_centerpoints = lane_centerpoints + self.forward_dir = forward_dir + self.predecessor_lanes = [] + self.successor_lanes = [] + self.outgoing_edges = [] + self.incoming_edges = [] + self.is_sampled = False + self.is_junction = is_junction + + +def get_junction(road_network, junction_id): + for junction in road_network.get_junctions(): + if junction.id == junction_id: + return junction + return None + + +def get_road(road_network, road_id): + for road in road_network.get_roads(): + if road.id == road_id: + return road + return None + + +def is_forward_dir(lane): + # Check traffic direction + travel_dir = None + vector_lane = lane.lane_xml.find(".//userData/vectorLane") + if vector_lane is not None: + travel_dir = vector_lane.get("travelDir") + + if travel_dir == "forward": + return True + return False + + +def create_lane_link_elements(road_network, roads, road_link_map): + roads_json_cnt = [[], [], []] + print(f"Network has {len(roads)} roads.") + for road_obj in roads: + # print(f"Road ID: {road_obj.id}") + lane_sections = road_obj.lane_sections + + is_road_junction = False if road_obj.road_xml.attrib["junction"] == "-1" else True + pred_lane_section_ids = {} + for predecessor_xml in road_obj.road_xml.find("link").findall("predecessor"): + if predecessor_xml.attrib["elementType"] == "road": + pred_road_id = predecessor_xml.attrib["elementId"] + if predecessor_xml.attrib["contactPoint"] == "start": + pred_lane_section_ids[pred_road_id] = 0 + else: + pred_road = get_road(road_network, pred_road_id) + pred_lane_section_ids[pred_road_id] = len(pred_road.lane_sections) - 1 + + succ_lane_section_ids = {} + for successor_xml in road_obj.road_xml.find("link").findall("successor"): + if successor_xml.attrib["elementType"] == "road": + succ_road_id = successor_xml.attrib["elementId"] + if successor_xml.attrib["contactPoint"] == "start": + succ_lane_section_ids[succ_road_id] = 0 + else: + succ_road = get_road(road_network, succ_road_id) + succ_lane_section_ids[succ_road_id] = len(succ_road.lane_sections) - 1 + + road_link_object = RoadLinkObject( + road_id=road_obj.id, + pred_lane_section_ids=pred_lane_section_ids, + succ_lane_section_ids=succ_lane_section_ids, + ) + + for lane_section in lane_sections: + # print(f"Lane Section ID: {lane_section.lane_section_ordinal}") + # print(f"Number of Left Lanes: {len(lane_section.left_lanes)}") + # print(f"Number of Right Lanes: {len(lane_section.right_lanes)}") + road_edges = [] + road_lines = [] + lanes = [] + # sidwalks = [] + + left_immediate_driveable = False + right_immediate_driveable = False + + # Left Lanes + add_lane_data = False + add_edge_data = False + previous_lane = None + for i, left_lane in enumerate(lane_section.left_lanes): + if left_lane.type == "driving": # We only deal with driving lanes + if i == 0: + left_immediate_driveable = True + + if add_lane_data: + road_line_data = get_lane_data(previous_lane, "BOUNDARY") + road_line_data = road_line_data[::40] + road_lines.append(road_line_data) + waypoints = get_lane_data(previous_lane, "CENTERLINE") + lanes.append(waypoints) + road_link_object.lane_links_map[(str(previous_lane.id), lane_section.lane_section_ordinal)] = ( + LaneLinkObject( + lane_id=previous_lane.id, + lane_section_index=lane_section.lane_section_ordinal, + road_id=road_obj.id, + lane=previous_lane, + lane_centerpoints=waypoints, + forward_dir=is_forward_dir(previous_lane), + is_junction=is_road_junction, + ) + ) + # Add outer edge as road edge + elif add_edge_data: + road_edges.append(get_lane_data(previous_lane, "BOUNDARY")) + add_lane_data = True + add_edge_data = False + else: + # Add inner lane as road edge + if add_lane_data and i != 0: + waypoints = get_lane_data(previous_lane, "CENTERLINE") + lanes.append(waypoints) + road_link_object.lane_links_map[(str(previous_lane.id), lane_section.lane_section_ordinal)] = ( + LaneLinkObject( + lane_id=previous_lane.id, + lane_section_index=lane_section.lane_section_ordinal, + road_id=road_obj.id, + lane=previous_lane, + lane_centerpoints=waypoints, + forward_dir=is_forward_dir(previous_lane), + is_junction=is_road_junction, + ) + ) + road_edges.append(get_lane_data(previous_lane, "BOUNDARY")) + add_edge_data = True + add_lane_data = False + previous_lane = left_lane + + if add_lane_data: + waypoints = get_lane_data(previous_lane, "CENTERLINE") + lanes.append(waypoints) + road_link_object.lane_links_map[(str(previous_lane.id), lane_section.lane_section_ordinal)] = ( + LaneLinkObject( + lane_id=previous_lane.id, + lane_section_index=lane_section.lane_section_ordinal, + road_id=road_obj.id, + lane=previous_lane, + lane_centerpoints=waypoints, + forward_dir=is_forward_dir(previous_lane), + is_junction=is_road_junction, + ) + ) + road_edges.append(get_lane_data(previous_lane, "BOUNDARY")) + # elif add_edge_data: + # if previous_lane.type == 'sidewalk': + # sidwalks.append(get_lane_data(previous_lane, "BOUNDARY")) + + # print("LEFT STATS") + # print(f"Number of Road edges: {len(road_edges)}") + # print(f"Road lines: {len(road_lines)}") + # print(f"Lanes: {len(lanes)}") + # print(f"Sidewalks: {len(sidwalks)}") + + # Right Lanes + add_lane_data = False + add_edge_data = False + previous_lane = None + for i, right_lane in enumerate(lane_section.right_lanes): + if right_lane.type == "driving": + if i == 0: + right_immediate_driveable = True + + if add_lane_data: + road_line_data = get_lane_data(previous_lane, "BOUNDARY") + road_line_data = road_line_data[::40] + road_lines.append(road_line_data) + waypoints = get_lane_data(previous_lane, "CENTERLINE") + lanes.append(waypoints) + road_link_object.lane_links_map[(str(previous_lane.id), lane_section.lane_section_ordinal)] = ( + LaneLinkObject( + lane_id=previous_lane.id, + lane_section_index=lane_section.lane_section_ordinal, + road_id=road_obj.id, + lane=previous_lane, + lane_centerpoints=waypoints, + forward_dir=is_forward_dir(previous_lane), + is_junction=is_road_junction, + ) + ) + # Add outer edge as road edge + elif add_edge_data: + road_edges.append(get_lane_data(previous_lane, "BOUNDARY")) + add_lane_data = True + add_edge_data = False + else: + # Add inner lane as road edge + if add_lane_data and i != 0: + waypoints = get_lane_data(previous_lane, "CENTERLINE") + lanes.append(waypoints) + road_link_object.lane_links_map[(str(previous_lane.id), lane_section.lane_section_ordinal)] = ( + LaneLinkObject( + lane_id=previous_lane.id, + lane_section_index=lane_section.lane_section_ordinal, + road_id=road_obj.id, + lane=previous_lane, + lane_centerpoints=waypoints, + forward_dir=is_forward_dir(previous_lane), + is_junction=is_road_junction, + ) + ) + road_edges.append(get_lane_data(previous_lane, "BOUNDARY")) + add_edge_data = True + add_lane_data = False + previous_lane = right_lane + + if add_lane_data: + waypoints = get_lane_data(previous_lane, "CENTERLINE") + lanes.append(waypoints) + road_link_object.lane_links_map[(str(previous_lane.id), lane_section.lane_section_ordinal)] = ( + LaneLinkObject( + lane_id=previous_lane.id, + lane_section_index=lane_section.lane_section_ordinal, + road_id=road_obj.id, + lane=previous_lane, + lane_centerpoints=waypoints, + forward_dir=is_forward_dir(previous_lane), + is_junction=is_road_junction, + ) + ) + road_edges.append(get_lane_data(previous_lane, "BOUNDARY")) + # elif add_edge_data: + # if previous_lane.type == 'sidewalk': + # sidwalks.append(get_lane_data(previous_lane, "BOUNDARY")) + + road_link_map[road_obj.id] = road_link_object + + roads_json_cnt[0].append(len(road_edges)) + roads_json_cnt[1].append(len(road_lines)) + roads_json_cnt[2].append(len(lanes)) + # if len(lanes) == 0 and len(road_lines) != 0: + # print(f"Road: {road_obj.id}, Lane Section: {lane_section.lane_section_ordinal}") + # print(f"Road edges: {len(road_edges)}, Road lines: {len(road_lines)}, Lanes: {len(lanes)}") + # break + # break + print(f"Total roads JSON count: {sum(roads_json_cnt[0]) + sum(roads_json_cnt[1]) + sum(roads_json_cnt[2])}") + # print(f"Road edges count: {roads_json_cnt[0]}") + # print(f"Road lines count: {roads_json_cnt[1]}") + print(f"Lanes count: {sum(roads_json_cnt[2])}") + total_lane_links = sum(len(obj.lane_links_map) for obj in road_link_map.values()) + assert sum(roads_json_cnt[2]) == total_lane_links + + +def create_successor_predecessor_elements(road_network, roads, road_link_map): + stopping_points = 0 + + for road_obj in roads: + # print(f"Road ID: {road_obj.id}") + road_link_object = road_link_map[road_obj.id] + lane_sections = road_obj.lane_sections + # print(f"Lane Sections: {lane_sections}") + + for lane_link_obj in road_link_object.lane_links_map.values(): + lane_link_obj.predecessor_lanes = [] + lane_link_obj.successor_lanes = [] + + lane = lane_link_obj.lane + + link_xml = lane.lane_xml.find("link") + + successor_is_junction = True + if link_xml is not None and link_xml.findall("successor") != []: + successor_is_junction = False + # if link_xml.findall("successor") == []: + # print(f"Successor for road: {road_obj.id}, lane_section: {lane_link_obj.lane_section_index}, lane: {lane_link_obj.lane.id}: {link_xml.findall('successor')}") + # Process Successor Links + for successor in link_xml.findall("successor"): + successor_id = successor.get("id") + + if lane_link_obj.lane_section_index + 1 < len(lane_sections): + # Lane link in next lane section + lane_link_obj.successor_lanes.append( + road_link_object.lane_links_map[(successor_id, lane_link_obj.lane_section_index + 1)] + ) + else: + # Lane link in successor roads + for road_succ in road_obj.road_xml.find("link").findall("successor"): + if road_succ.attrib["elementType"] == "road": + succ_road_id = road_succ.attrib["elementId"] + succ_road = road_link_map[succ_road_id] + succ_lane_section_index = road_link_object.succ_lane_section_ids[succ_road_id] + # if (lane_link_obj.lane_id, succ_lane_section_index) not in succ_road.lane_links_map: + # print(f"Key:{(successor_id, succ_lane_section_index)} not found in lane_links_map - road_id: {succ_road_id}, lane_section_index: {succ_lane_section_index}, lane_id: {successor_id}") + lane_link_obj.successor_lanes.append( + succ_road.lane_links_map[(successor_id, succ_lane_section_index)] + ) + else: + # Junction case + successor_is_junction = True + elif successor_is_junction: + # if road_obj.id == "0" and str(lane.id) == "-1": + # print(f"Road: {road_obj.id}, lane: {lane.id}, successor is a junction") + # break + # Handle junction case + for successor in road_obj.road_xml.find("link").findall("successor"): + if successor.attrib["elementType"] == "junction": + junction_id = successor.attrib["elementId"] + # print(f"Road: {road_obj.id}, lane: {lane.id}, successor is a junction: {junction_id}") + junction = get_junction(road_network, junction_id) + # print(f"Retrieved Junction: {junction.id} from road_network") + connected_lanes = junction.get_lane_junction_lanes(str(lane.id), road_id=road_obj.id) + if len(connected_lanes) == 0 and lane_link_obj.forward_dir: + stopping_points += 1 + print( + f"Non junction road: {road_obj.id}, lane_section: {lane_link_obj.lane_section_index}, lane: {lane_link_obj.lane.id} has no junction or road links, it is an ending lane" + ) + for conn_lane in connected_lanes: + succ_road_obj = get_road(road_network, conn_lane["road_id"]) + succ_road = road_link_map[conn_lane["road_id"]] + succ_lane_section_index = conn_lane["lane_section_index"] + if succ_lane_section_index == -1: + succ_lane_section_index = len(succ_road_obj.lane_sections) - 1 + succ_lane_id = conn_lane["lane_id"] + lane_link_obj.successor_lanes.append( + succ_road.lane_links_map[(str(succ_lane_id), succ_lane_section_index)] + ) + else: + if lane_link_obj.forward_dir: + # Stopping point + stopping_points += 1 + print( + f"Non junction road: {road_obj.id}, lane_section: {lane_link_obj.lane_section_index}, lane: {lane_link_obj.lane.id} has no junction or road links, it is an ending lane" + ) + + predecessor_is_junction = True + if link_xml is not None and link_xml.findall("predecessor") != []: + predecessor_is_junction = False + # Process Predecessor Links + for predecessor in link_xml.findall("predecessor"): + predecessor_id = predecessor.get("id") + + if lane_link_obj.lane_section_index - 1 >= 0: + # Lane link in previous lane section + lane_link_obj.predecessor_lanes.append( + road_link_object.lane_links_map[(predecessor_id, lane_link_obj.lane_section_index - 1)] + ) + else: + # Lane link in predecessor roads + for road_pred in road_obj.road_xml.find("link").findall("predecessor"): + if road_pred.attrib["elementType"] == "road": + pred_id = road_pred.attrib["elementId"] + pred_road = road_link_map[pred_id] + pred_lane_section_index = road_link_object.pred_lane_section_ids[pred_id] + # print(f"Curr lane: {lane_link_obj.lane.id}, section: {lane_link_obj.lane_section_index}, road: {road_obj.id}; Pred lane: {predecessor_id}, section: {road_link_object.pred_lane_section_ids[pred_id]}, road: {pred_id}") + lane_link_obj.predecessor_lanes.append( + pred_road.lane_links_map[(predecessor_id, pred_lane_section_index)] + ) + else: + # Junction case + predecessor_is_junction = True + elif predecessor_is_junction: + # Handle junction case + for predecessor in road_obj.road_xml.find("link").findall("predecessor"): + if predecessor.attrib["elementType"] == "junction": + junction_id = predecessor.attrib["elementId"] + junction = get_junction(road_network, junction_id) + connected_lanes = junction.get_lane_junction_lanes(lane_id=str(lane.id), road_id=road_obj.id) + if len(connected_lanes) == 0 and not lane_link_obj.forward_dir: + stopping_points += 1 + print( + f"Non junction road: {road_obj.id}, lane_section: {lane_link_obj.lane_section_index}, lane: {lane_link_obj.lane.id} has no junction or road links, it is a starting lane" + ) + for conn_lane in connected_lanes: + pred_road_obj = get_road(road_network, conn_lane["road_id"]) + pred_road = road_link_map[conn_lane["road_id"]] + pred_lane_section_index = conn_lane["lane_section_index"] + if pred_lane_section_index == -1: + pred_lane_section_index = len(pred_road_obj.lane_sections) - 1 + pred_lane_id = conn_lane["lane_id"] + lane_link_obj.predecessor_lanes.append( + pred_road.lane_links_map[(str(pred_lane_id), pred_lane_section_index)] + ) + else: + if not lane_link_obj.forward_dir: + # Stopping case + stopping_points += 1 + print( + f"Non junction road: {road_obj.id}, lane_section: {lane_link_obj.lane_section_index}, lane: {lane_link_obj.lane.id} has no junction or road links, it is a starting lane" + ) + + print(f"Road network has {stopping_points} stopping points (lanes with no predecessors or successors).") + + +def add_incoming_outgoing_edges(road_network, roads, road_link_map): + for road_obj in roads: + # print(f"Road ID: {road_obj.id}") + road_link_object = road_link_map[road_obj.id] + lane_sections = road_obj.lane_sections + # print(f"Lane Sections: {lane_sections}") + + for lane_link_obj in road_link_object.lane_links_map.values(): + lane = lane_link_obj.lane + link_xml = lane.lane_xml.find("link") + + if lane_link_obj.forward_dir: + # Forward direction, predecessor lanes are incoming edges, successor lanes are outgoing edges + lane_link_obj.incoming_edges = lane_link_obj.predecessor_lanes + lane_link_obj.outgoing_edges = lane_link_obj.successor_lanes + else: + # Backward direction, predecessor lanes are outgoing edges, successor lanes are incoming edges + lane_link_obj.outgoing_edges = lane_link_obj.predecessor_lanes + lane_link_obj.incoming_edges = lane_link_obj.successor_lanes + + +def test_linkage(road_link_map): + # Testing linkage + + start_road_id = "0" + road_link_object = road_link_map[start_road_id] + + lane_link_keys = list(road_link_object.lane_links_map.keys()) + random_lane_link_key = None + if random_lane_link_key is None: + random_lane_link_key = random.choice(lane_link_keys) + lane_link_obj = road_link_object.lane_links_map[random_lane_link_key] + + print(f"Starting at road id: {start_road_id}") + print(f"Lane link key: {random_lane_link_key}") + print(f"Lane id: {lane_link_obj.lane_id}, Lane section index: {lane_link_obj.lane_section_index}") + + # Traverse outgoing edges for 10 steps + current = lane_link_obj + for step in range(10): + print(f"\nStep {step}:") + print( + f" Road id: {current.road_id}, Lane id: {current.lane_id}, Lane section index: {current.lane_section_index}" + ) + if current.outgoing_edges: + current = random.choice(current.outgoing_edges) + else: + print(" No outgoing edges. Stopping traversal.") + print("Debugging") + print( + f"Outgoing Edges: {[(edge.road_id, str(edge.lane.id), edge.lane_section_index) for edge in current.outgoing_edges]}" + ) + print( + f"Incoming Edges: {[(edge.road_id, str(edge.lane.id), edge.lane_section_index) for edge in current.incoming_edges]}" + ) + print( + f"Successors: {[(edge.road_id, str(edge.lane.id), edge.lane_section_index) for edge in current.successor_lanes]}" + ) + print( + f"Predecessors: {[(edge.road_id, str(edge.lane.id), edge.lane_section_index) for edge in current.predecessor_lanes]}" + ) + break + + +def print_traj_stats(waypoints_list, num_timestamps, episode_length): + # Extract positions + positions = [wp["position"] for wp in waypoints_list if "position" in wp] + positions = np.array(positions) + + # Compute consecutive distances + if positions.shape[0] < 2: + print("Not enough waypoints to compute statistics.") + return + + diffs = positions[1:] - positions[:-1] + distances = np.linalg.norm(diffs, axis=1) + distance_traversed = np.sum(distances) + + # Compute max speed along trajectory + timestamp_dur = episode_length / num_timestamps + speeds = distances / timestamp_dur + max_speed_traj = np.max(speeds) + + print(f"Distance traversed: {distance_traversed} meters") + print(f"Max speed in trajectory: {max_speed_traj} m/s") + + +def check_geometry(point, all_geometries): + for pt in all_geometries: + dist = np.linalg.norm(np.array(pt) - np.array(point)) + if dist < 1e-9: # threshold, adjust as needed + return False + return True + + +def generate_traj_data( + road_link_map, + num_timestamps=90, + resolution=0.1, + episode_length=9, + max_speed=10, + random_sampling_variation=1, + resample=True, + all_geometries=[], +): + # Calculate average speed (70% of max_speed) + avg_speed = 0.7 * max_speed + avg_cons_pts_dist = resolution * (1 + np.sqrt(2)) / 2 + time_step_dur = episode_length / num_timestamps + sampling_length = int((avg_speed * time_step_dur) / avg_cons_pts_dist) + + # # Calculate junction speed (20% of max_speed) + # junction_speed = 0.2 * max_speed + + # Pick a random lane_link_key + while True: + road_link_keys = list(road_link_map.keys()) + start_key = random.choice(road_link_keys) + lane_link_keys = list(road_link_map[start_key].lane_links_map.keys()) + if lane_link_keys != []: + if resample: + start_lane_key = random.choice(lane_link_keys) + lane_link_obj = road_link_map[start_key].lane_links_map[start_lane_key] + if not lane_link_obj.is_sampled: + # print(f"Starting: RoadLink key: {start_key}\n LaneLink key: {start_lane_key}") + break + else: + start_lane_key = random.choice(lane_link_keys) + lane_link_obj = road_link_map[start_key].lane_links_map[start_lane_key] + + waypoints_list = [] + current_lane_link = lane_link_obj + current_lane_link.is_sampled = True + idx = random.randint(0, len(current_lane_link.lane_centerpoints) - 1) + # if check_geometry(current_lane_link.lane_centerpoints[idx], all_geometries) == False: + # print(f"Waypoint {current_lane_link.lane_centerpoints[idx]}, lane: {current_lane_link.lane_id}, Lane_Section: {current_lane_link.lane_section_index} Road: {current_lane_link.road_id} not in all_geometries") + waypoints_list.append( + { + "timestamp": 0, + "position": current_lane_link.lane_centerpoints[idx].tolist() + if hasattr(current_lane_link.lane_centerpoints[idx], "tolist") + else list(current_lane_link.lane_centerpoints[idx]), + "lane_id": current_lane_link.lane_id, + "lane_section_index": current_lane_link.lane_section_index, + "road_id": current_lane_link.road_id, + } + ) + change_lane = False + + for t in range(num_timestamps): + if change_lane: + # Pick a random outgoing edge + if current_lane_link.outgoing_edges: + if resample: + # Filter outgoing_edges with is_sampled == False + unsampled_edges = [ + edge for edge in current_lane_link.outgoing_edges if not getattr(edge, "is_sampled", False) + ] + if unsampled_edges != []: + current_lane_link = random.choice(unsampled_edges) + else: + current_lane_link = random.choice(current_lane_link.outgoing_edges) + current_lane_link.is_sampled = True + idx = 0 # Lane connection width is zero so reset at start + else: + current_lane_link = random.choice(current_lane_link.outgoing_edges) + else: + # No outgoing edge, stop trajectory + print("No outgoing edge, stopping trajectory") + while len(waypoints_list) < num_timestamps + 1: + waypoints_list.append( + { + "position": waypoint.tolist() if hasattr(waypoint, "tolist") else list(waypoint), + "velocity": {"x": 0.0, "y": 0.0}, + "heading": 0.0 if len(waypoints_list) == 0 else waypoints_list[-1]["heading"], + "lane_id": current_lane_link.lane_id, + "lane_section_index": current_lane_link.lane_section_index, + "road_id": current_lane_link.road_id, + "change_lane": change_lane, + } + ) + break + + # Randomize sampling length + curr_sampling_length = sampling_length + random.randint(-random_sampling_variation, random_sampling_variation) + next_idx = idx + curr_sampling_length + + if next_idx < len(current_lane_link.lane_centerpoints): + waypoint = current_lane_link.lane_centerpoints[next_idx] + idx = next_idx + change_lane = False + else: + waypoint = current_lane_link.lane_centerpoints[-1] + change_lane = True + + # Velocity and heading calculation + v_x = (waypoint[0] - waypoints_list[t - 1]["position"][0]) / time_step_dur + v_y = (waypoint[1] - waypoints_list[t - 1]["position"][1]) / time_step_dur + heading = np.arctan2(v_y, v_x) + + # if check_geometry(waypoint, all_geometries) == False: + # print(f"Waypoint {waypoint}, lane: {current_lane_link.lane_id}, Lane_Section: {current_lane_link.lane_section_index} Road: {current_lane_link.road_id} not in all_geometries") + + waypoints_list.append( + { + "position": waypoint.tolist() if hasattr(waypoint, "tolist") else list(waypoint), + "velocity": {"x": v_x, "y": v_y}, + "heading": heading, + "lane_id": current_lane_link.lane_id, + "lane_section_index": current_lane_link.lane_section_index, + "road_id": current_lane_link.road_id, + } + ) + + # Change first waypoint with average velocity and avg heading keeping everything else same + waypoints_list[0] = { + "position": waypoints_list[0]["position"], + "velocity": { + "x": np.mean([wp["velocity"]["x"] for wp in waypoints_list[1:]]), + "y": np.mean([wp["velocity"]["y"] for wp in waypoints_list[1:]]), + }, + "heading": np.mean([wp["heading"] for wp in waypoints_list[1:]]), + "lane_id": waypoints_list[0]["lane_id"], + "lane_section_index": waypoints_list[0]["lane_section_index"], + "road_id": waypoints_list[0]["road_id"], + } + + return waypoints_list + + +def save_object_to_json( + xodr_json, + road_link_map, + id, + resolution=0.1, + object_type="vehicle", + all_geometries=[], + start_with_zero_velocity=True, +): + traj_data = generate_traj_data(road_link_map=road_link_map, resolution=resolution, all_geometries=all_geometries) + + z = 1.0 + headings = [] + positions = [] + velocities = [] + for i, traj in enumerate(traj_data): + x = traj["position"][0] + y = traj["position"][1] + positions.append({"x": float(x), "y": float(y), "z": float(z)}) + v_x = 0.0 if start_with_zero_velocity and i == 0 else traj["velocity"]["x"] + v_y = 0.0 if start_with_zero_velocity and i == 0 else traj["velocity"]["y"] + velocities.append({"x": float(v_x), "y": float(v_y)}) + headings.append(float(traj["heading"])) + + object_data = { + "position": list(positions), + "width": 2.0, + "length": 4.5, + "height": 1.8, + "id": id, + "heading": list(headings), + "velocity": list(velocities), + "valid": [True] * len(positions), + "goalPosition": { + "x": float(positions[-1]["x"]), + "y": float(positions[-1]["y"]), + "z": float(positions[-1]["z"]), + }, + "type": object_type, + "mark_as_expert": False, + } + + objects = xodr_json.get("objects", []) + objects.append(object_data) + xodr_json["objects"] = objects + return id + 1 + + +def generate_data_each_map( + town_names, + carla_map_dir, + resolution, + input_json_base_path, + output_json_root_dir, + num_data_per_map, + num_objects, + make_only_first_agent_controllable, + start_with_zero_velocity=True, +): + os.makedirs(output_json_root_dir, exist_ok=True) + for town_name in town_names: + json_file = town_name + ".json" + odr_file = os.path.join(carla_map_dir, "T" + town_name[1:] + ".xodr") + input_json_path = os.path.join(input_json_base_path, json_file) + + for id in range(num_data_per_map): + output_json_path = os.path.join(output_json_root_dir, f"{town_name}_{id}.json") + + road_network = RoadNetwork(xodr_file_path=odr_file, resolution=resolution) + roads = road_network.get_roads() + print(f"Number of roads in the network: {len(roads)}") + + # Create a dictionary to map road_id to RoadLinkObject + road_link_map = {} + + # First create the lane link elements + create_lane_link_elements(road_network, roads, road_link_map) + + # Create successor predecessor elements + create_successor_predecessor_elements(road_network, roads, road_link_map) + + # Now add outgoing and incoming edges based on driving direction + add_incoming_outgoing_edges(road_network, roads, road_link_map) + + # Test linkage + test_linkage(road_link_map) + + with open(input_json_path, "r") as f: + xodr_json = json.load(f) + + roads = xodr_json["roads"] + all_geometries = [] + for road in roads: + geometry = [[pt["x"], pt["y"], pt["z"]] for pt in road["geometry"]] + all_geometries.extend(geometry) + + print(f"Total number of geometry points: {len(all_geometries)}") + # print(all_geometries[:100]) + + xodr_json["objects"] = [] + + for i in range(num_objects): + id = i + 1 + save_object_to_json( + xodr_json, + road_link_map, + id, + resolution=resolution, + all_geometries=all_geometries, + start_with_zero_velocity=start_with_zero_velocity, + ) + + # Make first agent only controllable + if make_only_first_agent_controllable: + for i, obj in enumerate(xodr_json.get("objects", [])): + if i == 0: + obj["mark_as_expert"] = False + else: + obj["mark_as_expert"] = True + + # Save to file + with open(output_json_path, "w") as f: + json.dump(xodr_json, f, indent=2) + + # Verify number of objects + with open(output_json_path, "r") as f: + xodr_json = json.load(f) + assert len(xodr_json.get("objects", [])) == num_objects + + print(f"Saved {num_objects} objects to {output_json_path}") + + +if __name__ == "__main__": + town_names = ["Town01", "Town02", "Town03", "Town04", "Town05", "Town06", "Town07", "Town10HD"] + # town_names = ['Town03'] + input_json_base_path = "data_utils/carla" + output_json_root_dir = "data/processed/carla_data" + carla_map_dir = "/scratch/pm3881/Carla-0.10.0-Linux-Shipping/CarlaUnreal/Content/Carla/Maps/OpenDrive" + resolution = 0.1 + num_data_per_map = 20 + num_objects = 32 + make_only_first_agent_controllable = False + start_with_zero_velocity = True + generate_data_each_map( + town_names, + carla_map_dir, + resolution, + input_json_base_path, + output_json_root_dir, + num_data_per_map, + num_objects=num_objects, + make_only_first_agent_controllable=make_only_first_agent_controllable, + start_with_zero_velocity=start_with_zero_velocity, + ) diff --git a/mlops/README.md b/mlops/README.md new file mode 100644 index 0000000000..cd46c1f367 --- /dev/null +++ b/mlops/README.md @@ -0,0 +1,131 @@ +# PufferDrive MLOps + +Tooling for running PufferDrive experiments on GCP Vertex AI. + +## Concepts + +- **Experiment**: A group of related training runs (e.g., "hyperparam_sweep", "baseline_comparison") +- **Run**: An individual training job within an experiment + +Output is organized hierarchically in the bucket: +``` +gs://bucket/experiments/// +``` + +## Quick Start + +```bash +# Launch a single run +python mlops/mlops.py launch my_experiment my_run --machine T4 + +# Launch multiple runs from config +python mlops/mlops.py launch-batch mlops/configs/experiments.yaml + +# Build base Docker image (only needed after dependency changes) +python mlops/mlops.py build-image +``` + +## Commands + +### `launch` - Run a single training job + +```bash +python mlops/mlops.py launch [options] + +Options: + --machine, -m Machine preset (see table below). Default: T4 + --dataset-path, -d GCS path to training dataset + --eval-dataset-path GCS path to eval dataset + --mode Run mode: train or sweep. Default: train + --project, -p GCP project (DRILAX, NOA, ...). Default: DRILAX + --verbose, -v Show Docker build/push output + +# Examples: +python mlops/mlops.py launch baseline run1 --machine T4 +python mlops/mlops.py launch hyperparam_sweep lr_1e-3 -m L4 + +# Hyperparameter sweep (uses [sweep.*] sections from drive.ini) +python mlops/mlops.py launch my_sweep run1 --project NOA --machine T4 --mode sweep --max-runs 5 --dataset-path /gcs/valeo-cp2879-driving-policy/datasets/womd/1000 +``` + +### `launch-batch` - Run multiple experiments/runs + +Define experiments in a YAML file: + +```yaml +experiments: + # Experiment with multiple runs + - name: hyperparam_sweep + machine: T4 + params: + env.num-agents: 1024 + runs: + - name: lr_1e-3 + params: + train.learning-rate: 0.001 + - name: lr_5e-4 + params: + train.learning-rate: 0.0005 + + # Single run experiment (no 'runs' field — experiment name used as run name) + - name: baseline_T4 + machine: T4 + params: + train.minibatch-size: 11648 +``` + +Then run: + +```bash +python mlops/mlops.py launch-batch mlops/configs/experiments.yaml +``` + +One Docker image is built per project, compiled for the union of all CUDA architectures +used by the runs in that project. Mixed machine types in the same batch are supported. + +### `train-test` - CI smoke test + +Launches the runs defined in `mlops/configs/smoke-tests.yaml`. Called automatically on PR merge. + +```bash +python mlops/mlops.py train-test +``` + +### `build-image` - Build base Docker image + +Only needed when Python dependencies change (torch, heavyball, etc.): + +```bash +python mlops/mlops.py build-image +``` + +## Machine Presets + +| Preset | Machine Type | GPU | Count | +|-------------|--------------------|----------------------|-------| +| T4 | n1-standard-16 | NVIDIA T4 | 1 | +| T4_2 | n1-standard-32 | NVIDIA T4 | 2 | +| T4_4 | n1-standard-32 | NVIDIA T4 | 4 | +| L4 | g2-standard-16 | NVIDIA L4 | 1 | +| L4_8 | g2-standard-96 | NVIDIA L4 | 8 | +| A100 | a2-highgpu-1g | NVIDIA A100 | 1 | +| A100_4 | a2-megagpu-16g | NVIDIA A100 | 4 | +| RTX6000 | g4-standard-48 | NVIDIA RTX Pro 6000 | 1 | +| RTX6000_2 | g4-standard-96 | NVIDIA RTX Pro 6000 | 2 | +| RTX6000_4 | g4-standard-192 | NVIDIA RTX Pro 6000 | 4 | +| RTX6000_8 | g4-standard-384 | NVIDIA RTX Pro 6000 | 8 | + +## File Structure + +``` +mlops/ +├── mlops.py # Main CLI +├── run_training.sh # Container entrypoint script +├── configs/ +│ ├── smoke-tests.yaml # CI smoke test runs +│ └── experiments.yaml # (user-created) batch experiment configs +├── dockerfile/ +│ ├── .dockerignore # Docker ignore file (copied to root during build) +│ ├── build.dockerfile # Base image with Python deps (rebuild on dep changes) +│ └── launch.dockerfile # Training image (rebuilds on code changes) +``` diff --git a/mlops/configs/experiments.yaml b/mlops/configs/experiments.yaml new file mode 100644 index 0000000000..de59bb837b --- /dev/null +++ b/mlops/configs/experiments.yaml @@ -0,0 +1,92 @@ +# PufferDrive Experiments Configuration +# ====================================== +# +# Define experiments and runs to execute on GCP Vertex AI. +# +# Structure: +# experiments: +# - name: experiment_name # Group name for related runs +# machine: T4 # Default machine for all runs (optional) +# dataset_path: gs://... # Default dataset for all runs (optional) +# params: {...} # Default params inherited by all runs (optional) +# param.key: value # Flat params also supported at experiment level +# runs: # List of individual runs +# - name: run_name # Unique name within the experiment +# machine: L4 # Override machine for this run (optional) +# params: {...} # Additional/override params (optional) +# +# If no 'runs' are specified, the experiment itself becomes a single run. +# +# Output structure in bucket: +# gs://bucket/experiments/// +# +# Usage: +# python mlops/mlops.py launch-batch mlops/experiments.yaml + + +experiments: + - name: gigaflow + machine: RTX6000_2 + params: + # Vec + vec.num-envs: 32 + # Env + env.num-agents: 4096 + # Train + train.minibatch-size: 262_144 + train.max-minibatch-size: 262_144 + train.total-timesteps: 300_000_000_000 + # Paper hparams + train.gamma: 0.99 + train.gae-lambda: 0.95 + train.learning-rate: 0.0005 + train.ent-coef: 0.01 + train.vf-coef: 0.5 + train.clip-coef: 0.2 + train.max-grad-norm: 0.5 + train.update-epochs: 1 + runs: + # # 1. Faithful paper replication: FF + large arch + paper hparams + # - name: ff_paper_arch + # params: + # rnn-name: None + # policy.input-size: 128 + # policy.backbone-hidden-size: 1024 + # policy.backbone-num-layers: 3 + # policy.encoder-gigaflow: True + + # # 2. LSTM + paper-sized arch + paper hparams + # # (split-network must be False: LSTM wrapper asserts this) + # - name: lstm_paper_arch + # params: + # policy.input-size: 128 + # policy.backbone-hidden-size: 1024 + # policy.backbone-num-layers: 2 + # policy.encoder-gigaflow: True + # policy.dropout: 0.2 + # rnn.input-size: 1024 # must match backbone-hidden-size + # rnn.hidden-size: 1024 + + # - name: l4_lstm_new_arch_input_128_24_env + # params: + # policy.input-size: 128 + # policy.encoder-gigaflow: True + # policy.dropout: 0.2 + + # - name: test_dockerfile + # params: + # rnn-name: Recurrent + # policy.encoder-gigaflow: True + # policy.dropout: 0.2 + + - name: test_dockerfile_ptx + params: + rnn-name: None + policy.encoder-gigaflow: True + policy.dropout: 0.2 + + # 4. LSTM + current (small) arch + paper hparams → cheapest LSTM comparison + # - name: lstm_small_arch + # params: + # policy.encoder-gigaflow: True + # rnn.hidden-size: 512 diff --git a/mlops/configs/smoke-tests.yaml b/mlops/configs/smoke-tests.yaml new file mode 100644 index 0000000000..fc09505a83 --- /dev/null +++ b/mlops/configs/smoke-tests.yaml @@ -0,0 +1,174 @@ +# PufferDrive Smoke Tests Configuration +# ====================================== +# +# Short training runs (~200M steps, ~60 min on T4) for CI convergence checks. +# Launched on every PR merge to develop via cmd_train_test. +# +# 4 runs covering the key training setups to verify reward trends upward. + +experiments: + - name: smoke_tests + machine: T4 + params: + vec.num-envs: 8 + train.total-timesteps: 500_000_000 + train.torch-deterministic: True + env.num-agents: 1024 + env.dt: 0.1 + env.reach-goal-behavior: 1 + env.max-lane-segment-observations: 32 + env.max-boundary-segment-observations: 12 + env.max-partner-observations: 4 + env.max-traffic-light-observations: 4 + env.max-stop-sign-observations: 1 + policy.input-size: 64 + policy.backbone-hidden-size: 256 + policy.backbone-num-layers: 1 + policy.actor-hidden-size: 256 + policy.actor-num-layers: 0 + policy.critic-hidden-size: 256 + policy.critic-num-layers: 0 + policy.split-network: False + policy.encoder-gigaflow: False + policy.dropout: 0.0 + rnn.input-size: 256 + rnn.hidden-size: 256 + + runs: + - name: smoke_gf_disc_jerk + dataset_path: "/gcs/valeo-cp2386-datasets/pufferdrive/v1.1/carla/" + params: + # Training parameters + train.minibatch-size: 4096 + train.max-minibatch-size: 4096 + train.bptt-horizon: 64 + # Environment parameters + env.simulation-mode: "gigaflow" + env.action-type: "discrete" + env.dynamics-model: "jerk" + env.reward-conditioning: True + env.reward-randomization: True + env.scenario-length: 128 + env.resample-frequency: 0 + env.num-maps: 1 + + # Observations + env.target-type: "static" + env.num-target-waypoints: 1 + env.min-waypoint-spacing: 2.0 + env.max-waypoint-spacing: 2.0 + + - name: smoke_replay_cont_classic + dataset_path: "/gcs/valeo-cp2386-datasets/pufferdrive/v1.1/womd/1000/" + params: + # Training parameters + train.minibatch-size: 5824 + train.max-minibatch-size: 5824 + train.bptt-horizon: 91 + # Environment parameters + env.simulation-mode: "replay" + env.action-type: "continuous" + env.dynamics-model: "classic" + env.reward-conditioning: False + env.reward-randomization: False + env.scenario-length: 91 + env.resample-frequency: 910 + env.num-maps: 1000 + + # Observations + env.target-type: "dynamic" + env.num-target-waypoints: 20 + env.min-waypoint-spacing: 2.0 + env.max-waypoint-spacing: 2.0 + + # Reward + env.reward-vehicle-collision: 1.0 + env.reward-offroad-collision: 1.0 + env.reward-traffic-light-violation: 1.0 + env.reward-ade: 0.0 + env.reward-goal: 0.0 + env.reward-progression: 0.2 + env.reward-under-speed: 0.05 + env.reward-comfort: 0.0 + env.reward-vel-align: 0.0 + env.reward-velocity: 0.0 + env.reward-lane-align: 0.0 + env.reward-lane-center: 1.0 + env.reward-timestep: 0.0 + + - name: smoke_replay_traj_jerk + dataset_path: "/gcs/valeo-cp2386-datasets/pufferdrive/v1.1/womd/1000/" + params: + # Training parameters + train.minibatch-size: 5824 + train.max-minibatch-size: 5824 + train.bptt-horizon: 91 + # Environment parameters + env.simulation-mode: "replay" + env.action-type: "trajectory_jerk" + env.dynamics-model: "jerk" + env.reward-conditioning: False + env.reward-randomization: False + env.trajectory-scaling-factors: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0] + env.scenario-length: 91 + env.resample-frequency: 910 + env.num-maps: 1000 + + # Observations + env.target-type: "dynamic" + env.num-target-waypoints: 20 + env.min-waypoint-spacing: 2.0 + env.max-waypoint-spacing: 2.0 + + # Reward + env.reward-vehicle-collision: 1.0 + env.reward-offroad-collision: 1.0 + env.reward-traffic-light-violation: 1.0 + env.reward-ade: 0.0 + env.reward-goal: 0.0 + env.reward-progression: 0.2 + env.reward-under-speed: 0.05 + env.reward-comfort: 0.0 + env.reward-vel-align: 0.0 + env.reward-velocity: 0.0 + env.reward-lane-align: 0.0 + env.reward-lane-center: 1.0 + env.reward-timestep: 0.0 + + - name: smoke_replay_disc_jerk + dataset_path: "/gcs/valeo-cp2386-datasets/pufferdrive/v1.1/womd/1000/" + params: + # Training parameters + train.minibatch-size: 5824 + train.max-minibatch-size: 5824 + train.bptt-horizon: 91 + # Environment parameters + env.simulation-mode: "replay" + env.action-type: "discrete" + env.dynamics-model: "jerk" + env.reward-conditioning: False + env.reward-randomization: False + env.scenario-length: 91 + env.resample-frequency: 910 + env.num-maps: 1000 + + # Observations + env.target-type: "dynamic" + env.num-target-waypoints: 20 + env.min-waypoint-spacing: 2.0 + env.max-waypoint-spacing: 2.0 + + # Reward + env.reward-vehicle-collision: 1.0 + env.reward-offroad-collision: 1.0 + env.reward-traffic-light-violation: 1.0 + env.reward-ade: 0.0 + env.reward-goal: 0.0 + env.reward-progression: 0.2 + env.reward-under-speed: 0.05 + env.reward-comfort: 0.0 + env.reward-vel-align: 0.0 + env.reward-velocity: 0.0 + env.reward-lane-align: 0.0 + env.reward-lane-center: 1.0 + env.reward-timestep: 0.0 diff --git a/mlops/dockerfile/.dockerignore b/mlops/dockerfile/.dockerignore new file mode 100644 index 0000000000..ced3190b86 --- /dev/null +++ b/mlops/dockerfile/.dockerignore @@ -0,0 +1,83 @@ +# ============================================================================= +# Docker Ignore for PufferDrive +# ============================================================================= +# Excludes files not needed in the training container to speed up builds. +# This file is copied to the project root during docker build by mlops.py + +# Git +.git/ +.gitignore +.github/ + +# Python virtual environment +.venv/ +venv/ +env/ + +# Python cache and compiled files +**/__pycache__/ +*.pyc +*.pyo +*.pyd +.pytest_cache/ +.mypy_cache/ +.ruff_cache/ + +# Build artifacts (will be rebuilt in container) +build/ +dist/ +*.egg-info/ +*.so +*.o +*.a + +# Pre-built binaries for other platforms (keep linux only) +*_macos*/ +*_webassembly*/ +build_web/ + +# Data files (downloaded at runtime) +pufferlib/resources/drive/**/*.bin +resources/ +data/ + +# IDE and editor configuration +.vscode/ +.idea/ +*.swp +*.swo + +# Documentation (exclude .md but keep pyproject.toml) +*.md +*.pdf +!mlops/sync/readme.md +LICENSE + +# Config files not needed in container +.pre-commit-config.yaml +ruff.toml + +# MLOps files not needed in container (keep run_training.sh and dockerfile/) +mlops/configs/ +mlops/__pycache__/ +mlops/sync/ + +# Experiment outputs and logs +experiments/ +benchmark/ +wandb/ +videos*/ +runs/ + +# Test files +tests/ + +# Example files +examples/ + +# Misc +*.gif +*.mp4 +*.png +*.jpg +config/ diff --git a/mlops/dockerfile/build.dockerfile b/mlops/dockerfile/build.dockerfile new file mode 100644 index 0000000000..93302ab668 --- /dev/null +++ b/mlops/dockerfile/build.dockerfile @@ -0,0 +1,86 @@ +# ============================================================================= +# PufferDrive Base Image +# ============================================================================= + +FROM nvcr.io/nvidia/cuda:12.8.1-cudnn-devel-ubuntu24.04 + +ENV TZ=Europe/Paris \ + DEBIAN_FRONTEND=noninteractive \ + PYTHONUNBUFFERED=1 \ + PYTHONDONTWRITEBYTECODE=1 \ + UV_COMPILE_BYTECODE=1 \ + UV_NO_DEV=1 \ + PATH="/usr/local/nvidia/bin:${PATH}" \ + LD_LIBRARY_PATH="/usr/local/nvidia/lib64:${LD_LIBRARY_PATH}" \ + NVIDIA_VISIBLE_DEVICES=all \ + NVIDIA_DRIVER_CAPABILITIES=compute,utility + +WORKDIR /pufferdrive + +# ============================================================================= +# System Dependencies +# ============================================================================= +RUN --mount=type=cache,target=/var/cache/apt \ + --mount=type=cache,target=/var/lib/apt/lists \ + apt-get update && \ + apt-get install -y --no-install-recommends \ + curl \ + build-essential \ + apt-transport-https \ + ca-certificates \ + gnupg && \ + # Google Cloud SDK + echo "deb [signed-by=/usr/share/keyrings/cloud.google.gpg] https://packages.cloud.google.com/apt cloud-sdk main" \ + | tee /etc/apt/sources.list.d/google-cloud-sdk.list && \ + curl -fsSL https://packages.cloud.google.com/apt/doc/apt-key.gpg \ + | gpg --dearmor -o /usr/share/keyrings/cloud.google.gpg && \ + apt-get update && \ + apt-get install -y --no-install-recommends google-cloud-cli && \ + rm -rf /tmp/* /var/tmp/* + +# ============================================================================= +# Install uv + Python + venv +# ============================================================================= +COPY --from=ghcr.io/astral-sh/uv:latest /uv /usr/local/bin/uv + +ENV PATH="/root/.local/bin:$PATH" + +RUN uv venv --python 3.12 /pufferdrive/.venv + +# Activate venv for all subsequent commands +ENV VIRTUAL_ENV=/pufferdrive/.venv \ + PATH="/pufferdrive/.venv/bin:$PATH" + +# ============================================================================= +# Python Dependencies (heavy packages - cached separately) +# ============================================================================= +RUN --mount=type=cache,target=/root/.cache/uv \ + uv pip install torch --index-url https://download.pytorch.org/whl/cu128 + +RUN --mount=type=cache,target=/root/.cache/uv \ + uv pip install \ + psutil \ + rich \ + rich_argparse \ + pandas \ + tqdm \ + matplotlib \ + imageio \ + pyro-ppl \ + mediapy \ + heavyball \ + neptune \ + wandb \ + tensorboard + +# ============================================================================= +# PufferLib Installation +# ============================================================================= +COPY pyproject.toml setup.py setup.cfg ./ +COPY pufferlib ./pufferlib + +RUN --mount=type=cache,target=/root/.cache/uv \ + NO_TRAIN=1 NO_OCEAN=1 uv pip install -e . + +# Remove .so files in pufferlib/ +RUN find /pufferdrive/pufferlib -name "*.so" -delete diff --git a/mlops/dockerfile/launch.dockerfile b/mlops/dockerfile/launch.dockerfile new file mode 100644 index 0000000000..f2b2538e30 --- /dev/null +++ b/mlops/dockerfile/launch.dockerfile @@ -0,0 +1,33 @@ +# ============================================================================= +# PufferDrive Training Image +# ============================================================================= + +FROM europe-west4-docker.pkg.dev/valeo-cp2386-dev/build-images/pufferdrive_cuda12.8_py3.12:latest + +# ============================================================================= +# Build C Extensions (only C files to maximize cache hits) +# ============================================================================= +COPY pufferlib/ocean ./pufferlib/ocean +COPY pufferlib/extensions/ ./pufferlib/extensions/ + +ARG GPU_ARCH +ENV TORCH_CUDA_ARCH_LIST="${GPU_ARCH}" +RUN python setup.py build_ext --inplace --force + +# ============================================================================= +# Copy Source Code +# ============================================================================= +COPY --chmod=755 mlops/run_training.sh /pufferdrive/run_training.sh +COPY pufferlib/*.py ./pufferlib/ +COPY pufferlib/resources ./pufferlib/resources +COPY pufferlib/config ./pufferlib/config + +# ============================================================================= +# Copy Staged Model (For Finetuning) +# ============================================================================= +COPY mlops/staging_models/ /pufferdrive/preload_model/ + +# ============================================================================= +# Entrypoint +# ============================================================================= +ENTRYPOINT ["/pufferdrive/run_training.sh"] diff --git a/mlops/mlops.py b/mlops/mlops.py new file mode 100644 index 0000000000..c50687c24c --- /dev/null +++ b/mlops/mlops.py @@ -0,0 +1,719 @@ +#!/usr/bin/env python3 +""" +Unified MLOps CLI for PufferDrive training on GCP Vertex AI. + +Usage: + # Launch a single run within an experiment + python mlops/mlops.py launch my_experiment my_run --machine T4 + + # Launch multiple runs from config file + python mlops/mlops.py launch-batch mlops/configs/experiments.yaml + + # Build and push the base image + python mlops/mlops.py build-image + + # Sync data to/from GCS + python mlops/mlops.py sync-data gs://bucket/path local/path --download +""" + +import argparse +import os +import shutil +import subprocess +import sys +from contextlib import contextmanager +from dataclasses import dataclass, field +from datetime import datetime +from google.cloud import aiplatform +from google.cloud.aiplatform.compat.types import reservation_affinity_v1 as reservation_affinity +from pathlib import Path +import yaml + +# Suppress noisy Google Cloud logs +os.environ["GRPC_VERBOSITY"] = "ERROR" +import logging + +logging.getLogger("google.api_core").setLevel(logging.ERROR) +logging.getLogger("google.auth").setLevel(logging.ERROR) +logging.getLogger("google.cloud").setLevel(logging.ERROR) +logging.getLogger("grpc").setLevel(logging.ERROR) + + +# ============================================================================= +# Configuration +# ============================================================================= + + +@dataclass +class GCPConfig: + """GCP project configuration.""" + + project_id: str + staging_bucket: str + service_account: str + default_dataset_path: str + training_container_registry: str + use_reservation: bool = False + location: str = "europe-west4" + builder_image: str = "europe-west4-docker.pkg.dev/valeo-cp2386-dev/build-images/pufferdrive_cuda12.8_py3.12:latest" + tensorboard_resource: str | None = None + + +GCP_CONFIGS = { + "DRILAX": GCPConfig( + project_id="valeo-cp2386-dev", + staging_bucket="gs://valeo-cp2386-runs", + service_account="valeo-cp2386-dev@appspot.gserviceaccount.com", + default_dataset_path="/gcs/valeo-cp2386-datasets/pufferdrive/v1.0/womd/training/", + training_container_registry="europe-west4-docker.pkg.dev/valeo-cp2386-dev/training-images", + tensorboard_resource="projects/858441583698/locations/europe-west4/tensorboards/6648324600696406016", + ), + "NOA": GCPConfig( + project_id="valeo-cp2879-dev", + staging_bucket="gs://valeo-cp2879-driving-policy", + service_account="valeo-cp2879-dev-job@valeo-cp2879-dev.iam.gserviceaccount.com", + default_dataset_path="/gcs/valeo-cp2879-driving-policy/datasets/carla/", + training_container_registry="europe-west4-docker.pkg.dev/valeo-cp2879-dev/driving-policy/training-images", + use_reservation=False, + tensorboard_resource=None, + ), +} + + +def get_config(project): + if project not in GCP_CONFIGS: + raise ValueError(f"Unknown project: {project}. Available: {list(GCP_CONFIGS.keys())}") + return GCP_CONFIGS[project] + + +@dataclass +class MachineSpec: + """Machine type specification.""" + + machine_type: str + accelerator_type: str + accelerator_count: int + cuda_arch: str + boot_disk_type: str = "pd-ssd" + + +# Machine type presets +MACHINE_PRESETS: dict[str, MachineSpec] = { + "T4": MachineSpec("n1-standard-16", "NVIDIA_TESLA_T4", 1, "7.5"), + "T4_2": MachineSpec("n1-standard-32", "NVIDIA_TESLA_T4", 2, "7.5"), + "T4_4": MachineSpec("n1-standard-32", "NVIDIA_TESLA_T4", 4, "7.5"), + "T4_8": MachineSpec("n1-standard-64", "NVIDIA_TESLA_T4", 8, "7.5"), + "L4": MachineSpec("g2-standard-16", "NVIDIA_L4", 1, "8.9"), + "L4_2": MachineSpec("g2-standard-32", "NVIDIA_L4", 2, "8.9"), + "L4_4": MachineSpec("g2-standard-64", "NVIDIA_L4", 4, "8.9"), + "L4_8": MachineSpec("g2-standard-96", "NVIDIA_L4", 8, "8.9"), + "A100": MachineSpec("a2-highgpu-1g", "NVIDIA_A100", 1, "8.0"), + "A100_2": MachineSpec("a2-highgpu-2g", "NVIDIA_A100", 2, "8.0"), + "A100_4": MachineSpec("a2-megagpu-16g", "NVIDIA_A100", 4, "8.0"), + "A100_8": MachineSpec("a2-megagpu-16g", "NVIDIA_A100", 8, "8.0"), + "RTX6000": MachineSpec("g4-standard-48", "NVIDIA_RTX_PRO_6000", 1, "12.0", "hyperdisk-balanced"), + "RTX6000_2": MachineSpec("g4-standard-96", "NVIDIA_RTX_PRO_6000", 2, "12.0", "hyperdisk-balanced"), + "RTX6000_4": MachineSpec("g4-standard-192", "NVIDIA_RTX_PRO_6000", 4, "12.0", "hyperdisk-balanced"), + "RTX6000_8": MachineSpec("g4-standard-384", "NVIDIA_RTX_PRO_6000", 8, "12.0", "hyperdisk-balanced"), +} + + +@dataclass +class Run: + """Run configuration within an experiment.""" + + experiment: str + name: str + machine: str = "T4" + dataset_path: str = "" + eval_dataset_path: str = "" + project: str = "DRILAX" + params: dict = field(default_factory=dict) + extra_args: list[str] = field(default_factory=list) + + +# ============================================================================= +# Utility Functions +# ============================================================================= + + +def run_command(command: list[str], description: str, verbose: bool = False) -> bool: + """Run a shell command with nice output.""" + print(f"🔧 {description}") + print(f" $ {' '.join(command)}") + + try: + subprocess.run(command, check=True, capture_output=not verbose, text=True) + print(" ✅ Success") + return True + except subprocess.CalledProcessError as e: + print(" ❌ Failed") + if not verbose and e.stdout: + print(f" STDOUT: {e.stdout}") + if not verbose and e.stderr: + print(f" STDERR: {e.stderr}") + return False + + +@contextmanager +def setup_dockerignore(): + """Context manager to copy .dockerignore to root for build and clean up after.""" + dockerignore_src = "mlops/dockerfile/.dockerignore" + dockerignore_dst = ".dockerignore" + + if os.path.exists(dockerignore_src): + shutil.copy(dockerignore_src, dockerignore_dst) + + try: + yield + finally: + if os.path.exists(dockerignore_dst): + os.remove(dockerignore_dst) + + +@contextmanager +def stage_model_artifacts(extra_args: list[str]): + """ + Checks if --load-model-path is present. If so: + 1. Copies the model and config.yaml to mlops/staging_models/ + 2. Updates the arg path to point to the container location. + 3. Cleans up after the build. + """ + staging_dir = Path("mlops/staging_models") + + # 1. Clean start: Create staging dir (even if empty, to satisfy Docker COPY) + if staging_dir.exists(): + shutil.rmtree(staging_dir) + staging_dir.mkdir(parents=True) + (staging_dir / ".keep").touch() # Prevent Docker COPY failure on empty dir + + # Check for argument + model_arg_idx = -1 + if "--load-model-path" in extra_args: + print("🔍 Detected --load-model-path argument, preparing model artifacts...") + model_arg_idx = extra_args.index("--load-model-path") + + try: + # If model arg exists and has a value + if model_arg_idx != -1 and model_arg_idx + 1 < len(extra_args): + local_path_str = extra_args[model_arg_idx + 1] + local_pt = Path(local_path_str) + + if not local_pt.exists(): + print(f"❌ Error: Model file not found: {local_pt}") + sys.exit(1) + + # Copy .pt file + print(f"📦 Staging model for Docker build: {local_pt.name}") + shutil.copy(local_pt, staging_dir / local_pt.name) + + # Copy config.yaml (check parent dir) + local_config = local_pt.parent.parent / "config.yaml" + if local_config.exists(): + shutil.copy(local_config, staging_dir / "config.yaml") + print(f"📦 Staged config: {local_config.name}") + else: + print("❌ Error: Config file not found: config.yaml") + + # The file will be at /pufferdrive/preload_model/ + container_path = f"/pufferdrive/preload_model/{local_pt.name}" + extra_args[model_arg_idx + 1] = container_path + print(f"🔄 Remapped --load-model-path to container path: {container_path}") + + yield + + finally: + # Cleanup staging area after build/push is done + if staging_dir.exists(): + shutil.rmtree(staging_dir) + + +# ============================================================================= +# Vertex AI Job Submission +# ============================================================================= + + +def submit_vertex_job( + config: GCPConfig, + display_name: str, + container_uri: str, + machine: MachineSpec, + experiment_name: str, + run_name: str, + project: str, + dataset_path: str, + eval_dataset_path: str = "", + extra_args: list[str] | None = None, + mode: str = "train", +) -> None: + """Submit a training job to Vertex AI.""" + + experiment_name = experiment_name.replace("_", "-") + run_name = run_name.replace("_", "-") + if config.project_id == "valeo-cp2879-dev": + cloud_path = f"{config.staging_bucket}/runs/{experiment_name}/{run_name}" + else: + cloud_path = f"{config.staging_bucket}/{experiment_name}/{run_name}" + + aiplatform.init( + project=config.project_id, + location=config.location, + staging_bucket=config.staging_bucket, + # experiment=experiment_name, + # experiment_tensorboard=config.tensorboard_resource, + ) + + # # Explicitly register the run in the Vertex ML Metadata store + # try: + # # First, try to fetch it if it already exists (e.g., re-running a failed job) + # aiplatform.start_run(run=run_name, resume=True) + # except Exception: + # # If it throws a 404 (doesn't exist yet), create it fresh + # aiplatform.start_run(run=run_name) + # finally: + # # Always end the local session so the CustomJob can take over + # aiplatform.end_run() + + # Build container arguments + container_args = ["--mode", mode, "--accelerator-count", str(machine.accelerator_count)] + if not dataset_path: + dataset_path = config.default_dataset_path + job_extra_args = list(extra_args) if extra_args else [] + job_extra_args.extend(["--env.map-dir", dataset_path]) + + # Add eval dataset path if provided + if eval_dataset_path: + job_extra_args.extend(["--eval.map-dir", eval_dataset_path]) + + container_args.extend(job_extra_args) + + # Machine spec with optional reservation affinity + machine_spec = { + "machine_type": machine.machine_type, + "accelerator_type": machine.accelerator_type, + "accelerator_count": machine.accelerator_count, + } + if config.use_reservation: + machine_spec["reservation_affinity"] = reservation_affinity.ReservationAffinity( + reservation_affinity_type=reservation_affinity.ReservationAffinity.Type.ANY_RESERVATION + ) + + worker_pool_specs = [ + { + "machine_spec": machine_spec, + "replica_count": 1, + "disk_spec": { + "boot_disk_type": machine.boot_disk_type, + "boot_disk_size_gb": 100, + }, + "container_spec": { + "image_uri": container_uri, + "args": container_args, + "env": [ + { + "name": "TORCH_CUDA_ARCH_LIST", + "value": machine.cuda_arch, + }, + { + "name": "CLOUD", + "value": "1", + }, + { + "name": "CLOUD_PATH", + "value": cloud_path, + }, + ], + }, + } + ] + + if sha := os.environ.get("GITHUB_SHA"): + labels = {"commit_sha": sha} + else: + labels = {"username": os.getlogin()} + + # Create and submit job + job = aiplatform.CustomJob( + display_name=display_name, + worker_pool_specs=worker_pool_specs, + staging_bucket=config.staging_bucket, + base_output_dir=cloud_path, + labels=labels, + project=config.project_id, + location=config.location, + ) + + print(f"📤 Submitting job: {display_name}") + job.submit( + service_account=config.service_account, + enable_web_access=False, + scheduling_strategy="STANDARD", + tensorboard=config.tensorboard_resource, + disable_retries=True, + # experiment=experiment_name, + # experiment_run=run_name, + ) + print(" ✅ Job submitted successfully") + + +# ============================================================================= +# Docker Operations +# ============================================================================= + + +def build_and_push_image(config: GCPConfig, tag: str, verbose: bool = False, gpu_arch: str = "") -> str: + """Build and push the training Docker image.""" + if not gpu_arch.strip(): + print("❌ GPU_ARCH is empty. Set machine.cuda_arch explicitly for this build.") + sys.exit(1) + + if os.environ.get("GITHUB_SHA"): + username = "pr-test" + else: + username = os.getlogin() + container_uri = f"{config.training_container_registry}/{username}:{tag}" + + with setup_dockerignore(): + build_cmd = [ + "docker", + "build", + "--tag", + container_uri, + "--build-arg", + f"GPU_ARCH={gpu_arch}", + "--file", + "mlops/dockerfile/launch.dockerfile", + ".", + ] + if not run_command(build_cmd, "Building Docker image", verbose): + sys.exit(1) + + # Push image + push_cmd = ["docker", "push", container_uri] + if not run_command(push_cmd, "Pushing Docker image", verbose): + print("💡 Tip: Run `gcloud auth configure-docker europe-west4-docker.pkg.dev`") + sys.exit(1) + + return container_uri + + +def build_base_image(config: GCPConfig, verbose: bool = False) -> None: + """Build and push the base builder image.""" + + with setup_dockerignore(): + build_cmd = [ + "docker", + "build", + "--tag", + config.builder_image, + "--file", + "mlops/dockerfile/build.dockerfile", + ".", + ] + if not run_command(build_cmd, "Building base image", verbose): + sys.exit(1) + + push_cmd = ["docker", "push", config.builder_image] + if not run_command(push_cmd, "Pushing base image", verbose): + sys.exit(1) + + print(f"✅ Base image ready: {config.builder_image}") + + +# ============================================================================= +# Experiment Loading +# ============================================================================= + + +def load_runs_from_yaml(path: str, default_project: str = "DRILAX") -> list[Run]: + """Load runs from a YAML file.""" + with open(path) as f: + data = yaml.safe_load(f) + + runs = [] + for exp_data in data.get("experiments", []): + experiment_name = exp_data["name"] + experiment_machine = exp_data.get("machine", "T4") + experiment_dataset = exp_data.get("dataset_path", "") + experiment_eval_dataset = exp_data.get("eval_dataset_path", "") + experiment_project = exp_data.get("project", default_project) + experiment_params = exp_data.get("params", {}) + experiment_extra_args = exp_data.get("extra_args", []) + + # If runs are defined, create a Run for each + if "runs" in exp_data: + for run_data in exp_data["runs"]: + # Merge experiment-level params with run-level params (run takes precedence) + merged_params = {**experiment_params, **run_data.get("params", {})} + merged_extra_args = experiment_extra_args + run_data.get("extra_args", []) + runs.append( + Run( + experiment=experiment_name, + name=run_data["name"], + machine=run_data.get("machine", experiment_machine), + dataset_path=run_data.get("dataset_path", experiment_dataset), + eval_dataset_path=run_data.get("eval_dataset_path", experiment_eval_dataset), + project=run_data.get("project", experiment_project), + params=merged_params, + extra_args=merged_extra_args, + ) + ) + else: + # No runs defined, treat experiment as a single run with same name + runs.append( + Run( + experiment=experiment_name, + name=experiment_name, + machine=experiment_machine, + dataset_path=experiment_dataset, + eval_dataset_path=experiment_eval_dataset, + project=experiment_project, + params=experiment_params, + extra_args=experiment_extra_args, + ) + ) + + return runs + + +def params_to_args(params: dict) -> list[str]: + """Convert parameter dictionary to command-line arguments.""" + args = [] + for key, value in params.items(): + # Convert param.subparam to --param.subparam format + args.extend([f"--{key}", str(value)]) + return args + + +# ============================================================================= +# CLI Commands +# ============================================================================= + + +def cmd_launch(args) -> None: + """Launch a single run within an experiment.""" + config = get_config(args.project) + + # Validate machine type + if args.machine not in MACHINE_PRESETS: + print(f"❌ Unknown machine type: {args.machine}") + print(f" Available: {', '.join(MACHINE_PRESETS.keys())}") + sys.exit(1) + + machine = MACHINE_PRESETS[args.machine] + + with stage_model_artifacts(args.extra_args): + # Generate tag and build image + tag = datetime.now().strftime("%d%m%H%M%S") + container_uri = build_and_push_image(config, tag, args.verbose, machine.cuda_arch) + + submit_vertex_job( + config=config, + display_name=f"pufferdrive/{args.experiment_name}/{args.run_name}", + container_uri=container_uri, + machine=machine, + experiment_name=args.experiment_name, + run_name=args.run_name, + project=args.project, + dataset_path=args.dataset_path or "", + eval_dataset_path=args.eval_dataset_path or "", + extra_args=args.extra_args, + mode=args.mode, + ) + + print( + f"\n🎉 Run '{args.run_name}' in experiment '{args.experiment_name}' launched on {args.machine} ({args.project})" + ) + + +def cmd_launch_batch(args) -> None: + """Launch multiple runs from a YAML config file.""" + # Load runs with CLI project as default + runs = load_runs_from_yaml(args.config_file, default_project=args.project) + if not runs: + print("❌ No runs found in config file") + sys.exit(1) + + print(f"📋 Found {len(runs)} runs") + + # Validate all machine types and projects upfront + valid_runs = [] + for run in runs: + if run.machine not in MACHINE_PRESETS: + print(f"⚠️ Unknown machine type '{run.machine}' for '{run.experiment}/{run.name}', skipping...") + elif run.project not in GCP_CONFIGS: + print(f"⚠️ Unknown project '{run.project}' for '{run.experiment}/{run.name}', skipping...") + else: + valid_runs.append(run) + + if not valid_runs: + print("❌ No valid runs to launch") + sys.exit(1) + + # Group runs by project to build one image per project + runs_by_project = {} + for run in valid_runs: + runs_by_project.setdefault(run.project, []).append(run) + + # Build and push images for each project + tag = datetime.now().strftime("%d%m%H%M%S") + container_uris = {} + + with stage_model_artifacts([]): + for project in runs_by_project: + config = get_config(project) + gpu_arch = " ".join(sorted({MACHINE_PRESETS[run.machine].cuda_arch for run in runs_by_project[project]})) + print(f"\n📦 Building image for {project}...") + container_uris[project] = build_and_push_image(config, tag, args.verbose, gpu_arch) + + print(f"\n🚀 Submitting {len(valid_runs)} runs sequentially...") + + for i, run in enumerate(valid_runs, 1): + print(f"\n[{i}/{len(valid_runs)}] Processing: {run.experiment}/{run.name} ...") + try: + config = get_config(run.project) + machine = MACHINE_PRESETS[run.machine] + # Construct args specifically for this run + extra_args = params_to_args(run.params) + run.extra_args + display_name = f"pufferdrive/{run.experiment}/{run.name}" + + submit_vertex_job( + config=config, + display_name=display_name, + container_uri=container_uris[run.project], + machine=machine, + experiment_name=run.experiment, + run_name=run.name, + project=run.project, + dataset_path=run.dataset_path, + eval_dataset_path=run.eval_dataset_path, + extra_args=extra_args, + ) + except Exception as e: + print(f" ❌ {run.experiment}/{run.name} failed: {e}") + continue + + print(f"\n🎉 All {len(valid_runs)} runs submitted!") + + +def cmd_train_test(args) -> None: + """Launch a training test for CI.""" + config_file = "mlops/configs/smoke-tests.yaml" + print(f"🚀 Launching CI training test from {config_file}") + + # Load runs with CLI project as default + runs = load_runs_from_yaml(config_file) + + # Group runs by project to build one image per project + runs_by_project = {} + for run in runs: + runs_by_project.setdefault(run.project, []).append(run) + + tag = os.environ.get("GITHUB_SHA") or datetime.now().strftime("%d%m%H%M%S") + short_sha = ( + os.environ.get("GITHUB_SHA") or subprocess.check_output(["git", "rev-parse", "HEAD"]).decode().strip() + )[:6] + container_uris = {} + + with stage_model_artifacts([]): # No extra args needed here + for project in runs_by_project: + config = get_config(project) + gpu_arch = " ".join(sorted({MACHINE_PRESETS[run.machine].cuda_arch for run in runs_by_project[project]})) + print(f"\n📦 Building image for {project}...") + container_uris[project] = build_and_push_image(config, tag, args.verbose, gpu_arch) + + print(f"\n🚀 Submitting {len(runs)} CI test runs sequentially...") + + for i, run in enumerate(runs, 1): + run_name = f"{short_sha}-{run.name}" + print(f"\n[{i}/{len(runs)}] Processing: {run.experiment}/{run_name} ...") + config = get_config(run.project) + machine = MACHINE_PRESETS[run.machine] + extra_args = params_to_args(run.params) + run.extra_args + display_name = f"pufferdrive-ci/{run.experiment}/{run_name}" + submit_vertex_job( + config=config, + display_name=display_name, + container_uri=container_uris[run.project], + machine=machine, + experiment_name=run.experiment, + run_name=run_name, + project=run.project, + dataset_path=run.dataset_path, + eval_dataset_path=run.eval_dataset_path, + extra_args=extra_args, + ) + + print(f"\n🎉 All {len(runs)} CI test runs submitted!") + + +def cmd_build_image(args) -> None: + """Build and push the base builder image.""" + config = get_config(args.project) + build_base_image(config, args.verbose) + + +# ============================================================================= +# Main +# ============================================================================= + + +def main(): + common = argparse.ArgumentParser(add_help=False) + common.add_argument( + "--project", + "-p", + default="DRILAX", + choices=list(GCP_CONFIGS.keys()), + help="GCP project to use (default: DRILAX)", + ) + + parser = argparse.ArgumentParser( + description="PufferDrive MLOps CLI", + formatter_class=argparse.RawDescriptionHelpFormatter, + ) + subparsers = parser.add_subparsers(dest="command", help="Available commands") + + # Launch single run + launch_parser = subparsers.add_parser("launch", parents=[common], help="Launch a single run within an experiment") + launch_parser.add_argument("experiment_name", help="Name for the experiment (groups related runs)") + launch_parser.add_argument("run_name", help="Name for this specific run") + launch_parser.add_argument( + "--machine", "-m", default="T4", choices=list(MACHINE_PRESETS.keys()), help="Machine type preset" + ) + launch_parser.add_argument("--dataset-path", "-d", help="GCS path to training dataset") + launch_parser.add_argument("--eval-dataset-path", help="GCS path to eval dataset") + launch_parser.add_argument("--verbose", "-v", action="store_true", help="Show verbose output") + launch_parser.add_argument("--mode", choices=["train", "sweep"], default="train", help="Run mode: train or sweep") + launch_parser.set_defaults(func=cmd_launch) + + # Launch batch runs + batch_parser = subparsers.add_parser("launch-batch", parents=[common], help="Launch multiple runs from config") + batch_parser.add_argument("config_file", help="YAML config file with experiments") + batch_parser.add_argument("--verbose", "-v", action="store_true", help="Show verbose output") + batch_parser.set_defaults(func=cmd_launch_batch) + + # Train test + train_test_parser = subparsers.add_parser("train-test", help="Launch a training test for CI") + train_test_parser.add_argument("--verbose", "-v", action="store_true", help="Show verbose output") + train_test_parser.set_defaults(func=cmd_train_test) + + # Build base image + build_parser = subparsers.add_parser("build-image", parents=[common], help="Build and push base Docker image") + build_parser.add_argument("--verbose", "-v", action="store_true", help="Show verbose output") + build_parser.set_defaults(func=cmd_build_image) + + args, extra_args = parser.parse_known_args() + + # Store extra args for passing to the container + args.extra_args = extra_args + + if not args.command: + parser.print_help() + sys.exit(1) + + args.func(args) + + +if __name__ == "__main__": + main() diff --git a/mlops/run_training.sh b/mlops/run_training.sh new file mode 100755 index 0000000000..36cd57fcbe --- /dev/null +++ b/mlops/run_training.sh @@ -0,0 +1,182 @@ +#!/bin/bash +# +# Container entrypoint for PufferDrive training on GCP. + +set -eo pipefail + +echo "🚀 Starting PufferDrive training..." + +# ============================================================================= +# Argument Parsing +# ============================================================================= + +ACCELERATOR_COUNT=1 +MODE="train" +TRAINING_ARGS=() + +while [[ $# -gt 0 ]]; do + case "$1" in + --accelerator-count) + ACCELERATOR_COUNT="$2" + shift 2 + ;; + --mode) + MODE="$2" + shift 2 + ;; + *) + TRAINING_ARGS+=("$1") + shift + ;; + esac +done + +if ! [[ "$ACCELERATOR_COUNT" =~ ^[0-9]+$ ]] || [[ "$ACCELERATOR_COUNT" -lt 1 ]]; then + echo "❌ Error: --accelerator-count must be an integer >= 1 (got '$ACCELERATOR_COUNT')" + exit 1 +fi + +# ============================================================================= +# GPU Preflight Checks (fail fast, no CPU fallback) +# ============================================================================= + +echo "🔎 Validating GPU environment..." + +if ! command -v nvidia-smi >/dev/null 2>&1; then + echo "❌ Error: nvidia-smi not found. NVIDIA driver is missing or not visible in container." + exit 1 +fi + +if ! nvidia-smi -L >/dev/null 2>&1; then + echo "❌ Error: nvidia-smi failed to query GPUs. Driver/runtime setup is invalid." + exit 1 +fi + +VISIBLE_GPUS=$(nvidia-smi --query-gpu=name --format=csv,noheader | sed '/^$/d' | wc -l | tr -d ' ') +if [[ "$VISIBLE_GPUS" -lt "$ACCELERATOR_COUNT" ]]; then + echo "❌ Error: requested ${ACCELERATOR_COUNT} GPU(s), but only ${VISIBLE_GPUS} visible via nvidia-smi." + exit 1 +fi + +if ! EXPECTED_GPUS="$ACCELERATOR_COUNT" python - <<'PY' +import os +import sys +import torch + +expected = int(os.environ["EXPECTED_GPUS"]) + +if torch.version.cuda is None: + print("❌ Error: installed PyTorch is not CUDA-enabled (torch.version.cuda is None).") + sys.exit(1) + +if not torch.cuda.is_available(): + print("❌ Error: torch.cuda.is_available() is False. CUDA runtime/driver unavailable.") + sys.exit(1) + +count = torch.cuda.device_count() +if count < expected: + print(f"❌ Error: PyTorch sees {count} CUDA device(s), but {expected} required.") + sys.exit(1) + +if expected > 1 and not torch.distributed.is_nccl_available(): + print("❌ Error: NCCL backend is unavailable for multi-GPU torchrun.") + sys.exit(1) + +if not torch.backends.cudnn.is_available(): + print("❌ Error: cuDNN is unavailable. CUDA stack is incomplete.") + sys.exit(1) + +print(f"✅ GPU preflight passed: torch CUDA={torch.version.cuda}, visible CUDA devices={count}.") +PY +then + exit 1 +fi + +# ============================================================================= +# Background Sync & Trap Setup +# ============================================================================= + +OUTPUT_DIR="/pufferdrive/training_output/" +mkdir -p "$OUTPUT_DIR" + +SYNC_PID="" +GCS_DEST="" + +if [[ -n "${CLOUD_PATH:-}" && "${NODE_RANK:-0}" == "0" ]]; then + GCS_DEST="${CLOUD_PATH}/" +fi + +do_sync() { + # Use 'timeout' to guarantee gcloud never hangs the container infinitely + if [[ "$MODE" == "sweep" ]]; then + timeout 120s gcloud storage rsync -r "$OUTPUT_DIR/" "$GCS_DEST" --quiet || true + else + local run_dir + run_dir=$(find "$OUTPUT_DIR" -mindepth 1 -maxdepth 1 -type d 2>/dev/null | head -1) + if [[ -n "$run_dir" ]]; then + timeout 120s gcloud storage rsync -r "$run_dir/" "$GCS_DEST" --quiet || true + fi + fi +} + +cleanup() { + local exit_code=$? + # Clear traps instantly to prevent recursive loops + trap - EXIT INT TERM ERR + + echo "🛑 Script exiting (code: $exit_code). Forcing cleanup..." + + if [[ -n "$SYNC_PID" ]]; then + echo "🛑 Terminating background sync process (PID $SYNC_PID) and children..." + # pkill -P kills the child 'sleep' or 'gcloud' holding standard output open + pkill -TERM -P "$SYNC_PID" 2>/dev/null || true + kill -9 "$SYNC_PID" 2>/dev/null || true + fi + + if [[ -n "$GCS_DEST" ]]; then + echo "☁️ Final GCS sync → $GCS_DEST (Max 2 mins)" + do_sync + fi + + echo "👋 Container exit complete." + exit "$exit_code" +} + +# Catch normal exits, set -e errors, AND system interrupts from GCP +trap cleanup EXIT INT TERM ERR + +if [[ -n "$GCS_DEST" ]]; then + sync_loop() { + local sync_interval=90 + while true; do + sleep "$sync_interval" + do_sync + done + } + sync_loop & + SYNC_PID=$! + echo "☁️ Background GCS sync started (pid=$SYNC_PID) → $GCS_DEST" +fi + +# ============================================================================= +# Training +# ============================================================================= + +if [[ "$MODE" == "sweep" ]]; then + echo "Running hyperparameter sweep..." + python -m pufferlib.pufferl sweep puffer_drive \ + --tb \ + --train.data-dir "$OUTPUT_DIR" \ + "${TRAINING_ARGS[@]}" +else + torchrun \ + --standalone \ + --nproc-per-node="${ACCELERATOR_COUNT}" \ + --max-restarts=0 \ + -m pufferlib.pufferl train puffer_drive \ + --tb \ + --train.data-dir "$OUTPUT_DIR" \ + "${TRAINING_ARGS[@]}" +fi + +echo "✅ Training completed successfully!" diff --git a/notebooks/01_observations.ipynb b/notebooks/01_observations.ipynb new file mode 100644 index 0000000000..90e87364d6 --- /dev/null +++ b/notebooks/01_observations.ipynb @@ -0,0 +1,534 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 01 - Observation Pipeline Debug\n", + "Verify obs vector is correctly packed, normalized, interpretable." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from pufferlib.ocean.drive.drive import Drive\n", + "from pufferlib.ocean.drive import binding\n", + "import pufferlib.viz\n", + "\n", + "# --- Environment configuration ---\n", + "NUM_AGENTS = 64\n", + "SIMULATION_MODE = \"gigaflow\"\n", + "DYNAMICS_MODEL = \"jerk\"\n", + "ACTION_TYPE = \"discrete\"\n", + "DT = 0.1\n", + "SCENARIO_LENGTH = 512\n", + "RESAMPLE_FREQUENCY = 0\n", + "REWARD_CONDITIONING = True\n", + "REWARD_RANDOMIZATION = False\n", + "TARGET_TYPE = \"static\"\n", + "COLLISION_BEHAVIOR = 1\n", + "OFFROAD_BEHAVIOR = 1\n", + "SEED = 42\n", + "MAP_DIR = \"../pufferlib/resources/drive/binaries/carla\"\n", + "\n", + "# --- Observation dimensions (configurable) ---\n", + "MAX_PARTNERS = 16\n", + "MAX_LANES = 32\n", + "MAX_BOUNDS = 32\n", + "MAX_TRAFFIC = 10\n", + "MAX_STOP_SIGNS = 0\n", + "\n", + "# --- Derived from binding (compile-time) ---\n", + "EGO_DIM = binding.EGO_FEATURES_JERK\n", + "NUM_COEFS = binding.NUM_REWARD_COEFS\n", + "PARTNER_F = binding.PARTNER_FEATURES\n", + "ROAD_F = binding.ROAD_FEATURES\n", + "TRAFFIC_F = binding.TRAFFIC_LIGHT_FEATURES\n", + "STOP_SIGN_F = binding.STOP_SIGN_FEATURES\n", + "COEF_NAMES = [\n", + " \"goal_radius\",\n", + " \"collision\",\n", + " \"offroad\",\n", + " \"comfort\",\n", + " \"lane_align\",\n", + " \"lane_center\",\n", + " \"velocity\",\n", + " \"traffic_light\",\n", + " \"center_bias\",\n", + " \"vel_align\",\n", + " \"overspeed\",\n", + " \"timestep\",\n", + " \"reverse\",\n", + " \"throttle\",\n", + " \"steer\",\n", + " \"acc\",\n", + "]\n", + "\n", + "# --- Create environment ---\n", + "env = Drive(\n", + " num_agents=NUM_AGENTS,\n", + " num_maps=1,\n", + " min_agents_per_env=NUM_AGENTS,\n", + " max_agents_per_env=NUM_AGENTS,\n", + " simulation_mode=SIMULATION_MODE,\n", + " dynamics_model=DYNAMICS_MODEL,\n", + " action_type=ACTION_TYPE,\n", + " dt=DT,\n", + " scenario_length=SCENARIO_LENGTH,\n", + " resample_frequency=RESAMPLE_FREQUENCY,\n", + " reward_conditioning=REWARD_CONDITIONING,\n", + " reward_randomization=REWARD_RANDOMIZATION,\n", + " target_type=TARGET_TYPE,\n", + " map_dir=MAP_DIR,\n", + " collision_behavior=COLLISION_BEHAVIOR,\n", + " offroad_behavior=OFFROAD_BEHAVIOR,\n", + " max_lane_segment_observations=MAX_LANES,\n", + " max_boundary_segment_observations=MAX_BOUNDS,\n", + " max_partner_observations=MAX_PARTNERS,\n", + " max_traffic_light_observations=MAX_TRAFFIC,\n", + " max_stop_sign_observations=MAX_STOP_SIGNS,\n", + " seed=SEED,\n", + ")\n", + "obs, info = env.reset(seed=SEED)\n", + "\n", + "# --- Derived from env ---\n", + "MAX_TARGET = env.num_target_waypoints\n", + "TARGET_F = binding.STATIC_TARGET_FEATURES if TARGET_TYPE == \"static\" else binding.DYNAMIC_TARGET_FEATURES\n", + "TARGET_DIM = MAX_TARGET * TARGET_F\n", + "\n", + "print(f\"obs shape: {obs.shape}, dtype: {obs.dtype}\")\n", + "print(f\"EGO_DIM={EGO_DIM}, NUM_COEFS={NUM_COEFS}, MAX_PARTNERS={MAX_PARTNERS}, PARTNER_F={PARTNER_F}\")\n", + "print(f\"MAX_LANES={MAX_LANES}, MAX_BOUNDS={MAX_BOUNDS}, ROAD_F={ROAD_F}\")\n", + "print(f\"MAX_TRAFFIC={MAX_TRAFFIC}, TRAFFIC_F={TRAFFIC_F}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Raw obs inspection" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Take first step so obs are populated\n", + "actions = np.zeros([env.num_agents, 1], dtype=np.int64)\n", + "\n", + "obs, rew, term, trunc, info = env.step(actions)\n", + "\n", + "print(f\"shape: {obs.shape}, dtype: {obs.dtype}\")\n", + "print(f\"min: {obs.min():.4f}, max: {obs.max():.4f}, mean: {obs.mean():.4f}, std: {obs.std():.4f}\")\n", + "print(f\"NaN: {np.isnan(obs).sum()}, Inf: {np.isinf(obs).sum()}\")\n", + "print(f\"% zeros: {(obs == 0).mean() * 100:.1f}%\")\n", + "print(f\"% outside [-1,1]: {((obs < -1) | (obs > 1)).mean() * 100:.2f}%\")\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(14, 4))\n", + "axes[0].hist(obs.flatten(), bins=100, edgecolor=\"black\", alpha=0.7)\n", + "axes[0].set_title(\"Full obs distribution\")\n", + "axes[0].set_xlabel(\"Value\")\n", + "# Per-agent: show obs[0] vs obs[1]\n", + "for i in range(min(4, obs.shape[0])):\n", + " axes[1].plot(obs[i], alpha=0.5, label=f\"agent {i}\")\n", + "axes[1].set_title(\"Obs vector by index (first 4 agents)\")\n", + "axes[1].legend()\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Unpack with pufferlib.viz.unpack_obs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ego, target, partners, lanes, boundaries, traffic, _ = pufferlib.viz.unpack_obs(\n", + " obs[:1],\n", + " dynamics_model=DYNAMICS_MODEL,\n", + " target_type=TARGET_TYPE,\n", + " reward_conditioning=REWARD_CONDITIONING,\n", + " num_target_waypoints=env.num_target_waypoints,\n", + " max_partners=MAX_PARTNERS,\n", + " max_lane_segments=MAX_LANES,\n", + " max_boundary_segments=MAX_BOUNDS,\n", + " max_traffic_lights=MAX_TRAFFIC,\n", + " max_stop_signs=MAX_STOP_SIGNS,\n", + ")\n", + "print(f\"ego: {ego.shape} = {ego}\")\n", + "print(f\"target: {target.shape}\")\n", + "print(f\"partners: {partners.shape}\")\n", + "print(f\"lanes: {lanes.shape}\")\n", + "print(f\"boundaries: {boundaries.shape}\")\n", + "print(f\"traffic: {traffic.shape}\")\n", + "\n", + "\n", + "labels = [\n", + " \"speed\",\n", + " \"width\",\n", + " \"length\",\n", + " \"steering\",\n", + " \"a_long\",\n", + " \"a_lat\",\n", + " \"lane_center_dist_01\",\n", + " \"lane_heading_cos\",\n", + " \"speed_limit\",\n", + "]\n", + "for name, val in zip(labels, ego):\n", + " print(f\" {name}: {val:.4f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Manual slice verification" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "o = obs[0] # first agent flat obs\n", + "idx = 0\n", + "\n", + "# Ego\n", + "ego_manual = o[idx : idx + EGO_DIM]\n", + "idx += EGO_DIM\n", + "assert np.allclose(ego_manual, ego), f\"ego mismatch: {ego_manual} vs {ego}\"\n", + "\n", + "# Reward conditioning coefs\n", + "coefs_manual = o[idx : idx + NUM_COEFS]\n", + "idx += NUM_COEFS\n", + "\n", + "# Target\n", + "target_manual = o[idx : idx + MAX_TARGET * TARGET_F].reshape(MAX_TARGET, TARGET_F)\n", + "idx += MAX_TARGET * TARGET_F\n", + "assert np.allclose(target_manual, target), \"target mismatch\"\n", + "\n", + "# Partners\n", + "partners_manual = o[idx : idx + MAX_PARTNERS * PARTNER_F].reshape(MAX_PARTNERS, PARTNER_F)\n", + "idx += MAX_PARTNERS * PARTNER_F\n", + "assert np.allclose(partners_manual, partners), \"partners mismatch\"\n", + "\n", + "# Lanes\n", + "lanes_manual = o[idx : idx + MAX_LANES * ROAD_F].reshape(MAX_LANES, ROAD_F)\n", + "idx += MAX_LANES * ROAD_F\n", + "assert np.allclose(lanes_manual, lanes), \"lanes mismatch\"\n", + "\n", + "# Boundaries\n", + "bounds_manual = o[idx : idx + MAX_BOUNDS * ROAD_F].reshape(MAX_BOUNDS, ROAD_F)\n", + "idx += MAX_BOUNDS * ROAD_F\n", + "assert np.allclose(bounds_manual, boundaries), \"boundaries mismatch\"\n", + "\n", + "# Traffic\n", + "traffic_manual = o[idx : idx + MAX_TRAFFIC * TRAFFIC_F].reshape(MAX_TRAFFIC, TRAFFIC_F)\n", + "idx += MAX_TRAFFIC * TRAFFIC_F\n", + "assert np.allclose(traffic_manual, traffic), \"traffic mismatch\"\n", + "\n", + "assert idx == obs.shape[1], f\"obs size mismatch: used {idx}, total {obs.shape[1]}\"\n", + "print(f\"All slices match. Total features used: {idx}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reward conditioning coefficients" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "coefs = obs[0, EGO_DIM : EGO_DIM + NUM_COEFS]\n", + "fig, ax = plt.subplots(figsize=(12, 4))\n", + "bars = ax.bar(range(NUM_COEFS), coefs, tick_label=COEF_NAMES)\n", + "ax.set_ylabel(\"Normalized coef value\")\n", + "ax.set_title(\"Reward conditioning coefficients (agent 0)\")\n", + "plt.xticks(rotation=45, ha=\"right\")\n", + "for bar, val in zip(bars, coefs):\n", + " ax.text(bar.get_x() + bar.get_width() / 2, bar.get_height(), f\"{val:.3f}\", ha=\"center\", va=\"bottom\", fontsize=8)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Compare across agents\n", + "all_coefs = obs[:, EGO_DIM : EGO_DIM + NUM_COEFS]\n", + "print(\"Coef stats across agents:\")\n", + "for i, name in enumerate(COEF_NAMES):\n", + " c = all_coefs[:, i]\n", + " print(f\" {name:15s}: mean={c.mean():.3f} std={c.std():.3f} min={c.min():.3f} max={c.max():.3f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Partner observations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "partner_labels = [\"rel_x\", \"rel_y\", \"width\", \"length\", \"heading_cos\", \"heading_sin\", \"speed\"]\n", + "active_mask = ~np.all(partners == 0, axis=1)\n", + "n_active = active_mask.sum()\n", + "print(f\"Active partners: {n_active}/{MAX_PARTNERS}\")\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", + "\n", + "# Heatmap\n", + "im = axes[0].imshow(partners, aspect=\"auto\", cmap=\"RdBu_r\", vmin=-1, vmax=1)\n", + "axes[0].set_xticks(range(PARTNER_F))\n", + "axes[0].set_xticklabels(partner_labels, rotation=45, ha=\"right\")\n", + "axes[0].set_ylabel(\"Partner index\")\n", + "axes[0].set_title(f\"Partner obs heatmap ({n_active} active)\")\n", + "plt.colorbar(im, ax=axes[0])\n", + "\n", + "# Scatter in ego frame\n", + "active_partners = partners[active_mask]\n", + "if len(active_partners) > 0:\n", + " axes[1].scatter(active_partners[:, 0], active_partners[:, 1], c=\"gray\", s=100, edgecolors=\"black\")\n", + " for i, p in enumerate(active_partners):\n", + " axes[1].annotate(str(i), (p[0], p[1]), fontsize=8, ha=\"center\", va=\"bottom\")\n", + "axes[1].scatter(0, 0, c=\"blue\", s=200, marker=\"s\", label=\"ego\", zorder=10)\n", + "axes[1].set_xlabel(\"rel_x\")\n", + "axes[1].set_ylabel(\"rel_y\")\n", + "axes[1].set_title(\"Partners in ego frame\")\n", + "axes[1].legend()\n", + "axes[1].set_aspect(\"equal\")\n", + "axes[1].grid(True, alpha=0.3)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lane / boundary segments" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "road_labels = [\"rel_x\", \"rel_y\", \"length\", \"width\", \"dir_cos\", \"dir_sin\"]\n", + "\n", + "lane_active = ~np.all(lanes == 0, axis=1)\n", + "bound_active = ~np.all(boundaries == 0, axis=1)\n", + "print(f\"Active lanes: {lane_active.sum()}/{MAX_LANES}, boundaries: {bound_active.sum()}/{MAX_BOUNDS}\")\n", + "\n", + "fig, ax = plt.subplots(figsize=(10, 10))\n", + "\n", + "# Lanes\n", + "for seg in lanes[lane_active]:\n", + " x, y, length, width, dc, ds = seg\n", + " ax.plot([x - dc * length / 2, x + dc * length / 2], [y - ds * length / 2, y + ds * length / 2], c=\"lightgrey\", lw=1)\n", + " ax.scatter(x, y, c=\"lightgrey\", s=5)\n", + "\n", + "# Boundaries\n", + "for seg in boundaries[bound_active]:\n", + " x, y, length, width, dc, ds = seg\n", + " ax.plot([x - dc * length / 2, x + dc * length / 2], [y - ds * length / 2, y + ds * length / 2], c=\"black\", lw=1)\n", + " ax.scatter(x, y, c=\"black\", s=5)\n", + "\n", + "ax.scatter(0, 0, c=\"blue\", s=200, marker=\"s\", label=\"ego\", zorder=10)\n", + "ax.set_aspect(\"equal\")\n", + "ax.set_title(\"Lane + boundary segments in ego frame\")\n", + "ax.legend()\n", + "ax.grid(True, alpha=0.3)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Ego-centric view (pufferlib.viz)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "img = pufferlib.viz.plot_observation(\n", + " obs[:1],\n", + " dynamics_model=DYNAMICS_MODEL,\n", + " target_type=TARGET_TYPE,\n", + " reward_conditioning=True,\n", + " num_target_waypoints=env.num_target_waypoints,\n", + " max_partners=MAX_PARTNERS,\n", + " max_lane_segments=MAX_LANES,\n", + " max_boundary_segments=MAX_BOUNDS,\n", + " max_traffic_lights=MAX_TRAFFIC,\n", + " max_stop_signs=MAX_STOP_SIGNS,\n", + ")\n", + "fig, ax = plt.subplots(figsize=(10, 10))\n", + "ax.imshow(img)\n", + "ax.axis(\"off\")\n", + "ax.set_title(\"Ego-centric observation (agent 0)\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bird's eye view (simulator state)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "scenarios = env.get_state()\n", + "# get_state returns a list of scenario dicts (one per sub-env) or a single dict\n", + "if isinstance(scenarios, list):\n", + " scenario = scenarios[0]\n", + "else:\n", + " scenario = scenarios\n", + "\n", + "img = pufferlib.viz.plot_simulator_state(scenario, timestep=0)\n", + "fig, ax = plt.subplots(figsize=(12, 12))\n", + "ax.imshow(img)\n", + "ax.axis(\"off\")\n", + "ax.set_title(\"Bird's eye view\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Multi-step: ego features over time" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "N_STEPS = 20\n", + "ego_labels = [\"goal_x\", \"goal_y\", \"speed\", \"width\", \"length\", \"steering\", \"a_long\", \"a_lat\"]\n", + "ego_history = np.zeros((N_STEPS, EGO_DIM))\n", + "\n", + "for t in range(N_STEPS):\n", + " actions = np.zeros([env.num_agents, 1], dtype=np.int64)\n", + " obs_t, _, _, _, _ = env.step(actions)\n", + " ego_history[t] = obs_t[0, :EGO_DIM]\n", + "\n", + "fig, axes = plt.subplots(2, 2, figsize=(14, 8))\n", + "# Speed\n", + "axes[0, 0].plot(ego_history[:, 0])\n", + "axes[0, 0].set_title(\"speed\")\n", + "axes[0, 0].set_xlabel(\"step\")\n", + "# Steering\n", + "axes[0, 1].plot(ego_history[:, 3])\n", + "axes[0, 1].set_title(\"steering\")\n", + "axes[0, 1].set_xlabel(\"step\")\n", + "# a_long\n", + "axes[1, 0].plot(ego_history[:, 4])\n", + "axes[1, 0].set_title(\"a_long\")\n", + "axes[1, 0].set_xlabel(\"step\")\n", + "# a_lat\n", + "axes[1, 1].plot(ego_history[:, 5])\n", + "axes[1, 1].set_title(\"a_lat\")\n", + "axes[1, 1].set_xlabel(\"step\")\n", + "plt.suptitle(\"Agent 0 ego features over 20 steps (no-op action)\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Cross-agent distributions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Current obs across all agents\n", + "speeds = obs[:, 2] if EGO_DIM >= 3 else obs[:, 0]\n", + "goal_dists = np.sqrt(obs[:, 0] ** 2 + obs[:, 1] ** 2) # goal_x, goal_y\n", + "\n", + "# Count active partners per agent\n", + "partner_start = EGO_DIM + NUM_COEFS + TARGET_DIM\n", + "partner_end = partner_start + MAX_PARTNERS * PARTNER_F\n", + "all_partners = obs[:, partner_start:partner_end].reshape(-1, MAX_PARTNERS, PARTNER_F)\n", + "partner_counts = (~np.all(all_partners == 0, axis=2)).sum(axis=1)\n", + "\n", + "fig, axes = plt.subplots(1, 3, figsize=(15, 4))\n", + "axes[0].hist(speeds, bins=20, edgecolor=\"black\", alpha=0.7)\n", + "axes[0].set_title(f\"Speed distribution (N={len(speeds)})\")\n", + "axes[0].set_xlabel(\"speed\")\n", + "\n", + "axes[1].hist(goal_dists, bins=20, edgecolor=\"black\", alpha=0.7, color=\"orange\")\n", + "axes[1].set_title(\"Goal distance (L2 of goal_x, goal_y)\")\n", + "axes[1].set_xlabel(\"distance\")\n", + "\n", + "axes[2].hist(partner_counts, bins=range(MAX_PARTNERS + 2), edgecolor=\"black\", alpha=0.7, color=\"green\")\n", + "axes[2].set_title(\"Active partners per agent\")\n", + "axes[2].set_xlabel(\"count\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/02_rewards.ipynb b/notebooks/02_rewards.ipynb new file mode 100644 index 0000000000..ae1326e124 --- /dev/null +++ b/notebooks/02_rewards.ipynb @@ -0,0 +1,405 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 02 - Reward Signals Debug\n", + "Understand reward magnitudes, components, and correlation with agent behavior." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from pufferlib.ocean.drive.drive import Drive\n", + "from pufferlib.ocean.drive import binding\n", + "import pufferlib.viz\n", + "\n", + "# --- Environment configuration ---\n", + "NUM_AGENTS = 64\n", + "SIMULATION_MODE = \"gigaflow\"\n", + "DYNAMICS_MODEL = \"jerk\"\n", + "ACTION_TYPE = \"discrete\"\n", + "DT = 0.1\n", + "SCENARIO_LENGTH = 512\n", + "RESAMPLE_FREQUENCY = 0\n", + "REWARD_CONDITIONING = True\n", + "REWARD_RANDOMIZATION = False\n", + "TARGET_TYPE = \"static\"\n", + "COLLISION_BEHAVIOR = 1\n", + "OFFROAD_BEHAVIOR = 1\n", + "SEED = 42\n", + "MAP_DIR = \"../pufferlib/resources/drive/binaries/carla\"\n", + "\n", + "# --- Observation dimensions (configurable) ---\n", + "MAX_PARTNERS = 16\n", + "MAX_LANES = 32\n", + "MAX_BOUNDS = 32\n", + "MAX_TRAFFIC = 10\n", + "MAX_STOP_SIGNS = 0\n", + "\n", + "# --- Derived from binding (compile-time) ---\n", + "EGO_DIM = binding.EGO_FEATURES_JERK\n", + "NUM_COEFS = binding.NUM_REWARD_COEFS\n", + "PARTNER_F = binding.PARTNER_FEATURES\n", + "ROAD_F = binding.ROAD_FEATURES\n", + "TRAFFIC_F = binding.TRAFFIC_LIGHT_FEATURES\n", + "STOP_SIGN_F = binding.STOP_SIGN_FEATURES\n", + "COEF_NAMES = [\n", + " \"goal_radius\",\n", + " \"collision\",\n", + " \"offroad\",\n", + " \"comfort\",\n", + " \"lane_align\",\n", + " \"lane_center\",\n", + " \"velocity\",\n", + " \"traffic_light\",\n", + " \"center_bias\",\n", + " \"vel_align\",\n", + " \"overspeed\",\n", + " \"timestep\",\n", + " \"reverse\",\n", + " \"throttle\",\n", + " \"steer\",\n", + " \"acc\",\n", + "]\n", + "\n", + "# --- Create environment ---\n", + "env = Drive(\n", + " num_agents=NUM_AGENTS,\n", + " num_maps=1,\n", + " min_agents_per_env=NUM_AGENTS,\n", + " max_agents_per_env=NUM_AGENTS,\n", + " simulation_mode=SIMULATION_MODE,\n", + " dynamics_model=DYNAMICS_MODEL,\n", + " action_type=ACTION_TYPE,\n", + " dt=DT,\n", + " scenario_length=SCENARIO_LENGTH,\n", + " resample_frequency=RESAMPLE_FREQUENCY,\n", + " reward_conditioning=REWARD_CONDITIONING,\n", + " reward_randomization=REWARD_RANDOMIZATION,\n", + " target_type=TARGET_TYPE,\n", + " map_dir=MAP_DIR,\n", + " collision_behavior=COLLISION_BEHAVIOR,\n", + " offroad_behavior=OFFROAD_BEHAVIOR,\n", + " max_lane_segment_observations=MAX_LANES,\n", + " max_boundary_segment_observations=MAX_BOUNDS,\n", + " max_partner_observations=MAX_PARTNERS,\n", + " max_traffic_light_observations=MAX_TRAFFIC,\n", + " max_stop_sign_observations=MAX_STOP_SIGNS,\n", + " seed=SEED,\n", + ")\n", + "obs, info = env.reset(seed=SEED)\n", + "\n", + "# --- Derived from env ---\n", + "MAX_TARGET = env.num_target_waypoints\n", + "TARGET_F = binding.STATIC_TARGET_FEATURES if TARGET_TYPE == \"static\" else binding.DYNAMIC_TARGET_FEATURES\n", + "TARGET_DIM = MAX_TARGET * TARGET_F\n", + "N_ACTIONS = 12\n", + "N = env.num_agents\n", + "ACT_SHAPE = (N, len(env.single_action_space.nvec))\n", + "\n", + "print(f\"env ready: {N} agents, obs={obs.shape}, act_shape={ACT_SHAPE}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Single step: no-op reward distribution" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "actions = np.zeros(ACT_SHAPE, dtype=np.int64)\n", + "obs, rew, term, trunc, info = env.step(actions)\n", + "\n", + "print(f\"reward shape: {rew.shape}\")\n", + "print(f\"min: {rew.min():.6f}, max: {rew.max():.6f}, mean: {rew.mean():.6f}, std: {rew.std():.6f}\")\n", + "print(f\"NaN: {np.isnan(rew).sum()}, all zero: {(rew == 0).all()}\")\n", + "print(f\"terminals: {term.sum()}, truncations: {trunc.sum()}\")\n", + "\n", + "fig, ax = plt.subplots(figsize=(8, 4))\n", + "ax.bar(range(len(rew)), rew, color=[\"red\" if r < 0 else \"green\" for r in rew])\n", + "ax.set_xlabel(\"Agent index\")\n", + "ax.set_ylabel(\"Reward\")\n", + "ax.set_title(\"Single step reward (no-op action)\")\n", + "ax.axhline(0, color=\"black\", lw=0.5)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 100-step rollout: reward heatmap and cumulative returns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "N_STEPS = 100\n", + "rewards_history = np.zeros((N_STEPS, N))\n", + "terms_history = np.zeros((N_STEPS, N))\n", + "\n", + "for t in range(N_STEPS):\n", + " actions = np.random.randint(0, N_ACTIONS, size=ACT_SHAPE)\n", + " obs, rew, term, trunc, info = env.step(actions)\n", + " rewards_history[t] = rew\n", + " terms_history[t] = term\n", + "\n", + "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n", + "\n", + "axes[0].plot(rewards_history.mean(axis=1))\n", + "axes[0].set_xlabel(\"Step\")\n", + "axes[0].set_ylabel(\"Mean reward\")\n", + "axes[0].set_title(\"Mean reward per step\")\n", + "\n", + "im = axes[1].imshow(rewards_history.T, aspect=\"auto\", cmap=\"RdYlGn\", interpolation=\"nearest\")\n", + "axes[1].set_xlabel(\"Step\")\n", + "axes[1].set_ylabel(\"Agent\")\n", + "axes[1].set_title(\"Reward heatmap (steps x agents)\")\n", + "plt.colorbar(im, ax=axes[1])\n", + "\n", + "cum_returns = rewards_history.cumsum(axis=0)\n", + "for i in range(min(8, N)):\n", + " axes[2].plot(cum_returns[:, i], alpha=0.6, label=f\"agent {i}\")\n", + "axes[2].set_xlabel(\"Step\")\n", + "axes[2].set_ylabel(\"Cumulative return\")\n", + "axes[2].set_title(\"Cumulative returns\")\n", + "axes[2].legend(fontsize=7)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "print(f\"Total reward stats: mean={rewards_history.mean():.5f}, std={rewards_history.std():.5f}\")\n", + "print(f\"Per-episode return (100 steps): mean={cum_returns[-1].mean():.3f}, std={cum_returns[-1].std():.3f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reward coefficient inspection" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "all_coefs = obs[:, EGO_DIM : EGO_DIM + NUM_COEFS]\n", + "print(f\"Reward coefs shape: {all_coefs.shape}\")\n", + "print()\n", + "print(f\"{'Coef':>15s} | {'mean':>8s} {'std':>8s} {'min':>8s} {'max':>8s}\")\n", + "print(\"-\" * 55)\n", + "for i, name in enumerate(COEF_NAMES):\n", + " c = all_coefs[:, i]\n", + " print(f\"{name:>15s} | {c.mean():8.4f} {c.std():8.4f} {c.min():8.4f} {c.max():8.4f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Terminal analysis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "N_STEPS = 200\n", + "term_steps, trunc_steps = [], []\n", + "term_rewards, trunc_rewards = [], []\n", + "\n", + "for t in range(N_STEPS):\n", + " actions = np.random.randint(0, N_ACTIONS, size=ACT_SHAPE)\n", + " obs, rew, term, trunc, info = env.step(actions)\n", + " for i in range(N):\n", + " if term[i]:\n", + " term_steps.append(t)\n", + " term_rewards.append(rew[i])\n", + " if trunc[i]:\n", + " trunc_steps.append(t)\n", + " trunc_rewards.append(rew[i])\n", + "\n", + "print(f\"Terminals: {len(term_steps)}, Truncations: {len(trunc_steps)}\")\n", + "if term_rewards:\n", + " tr = np.array(term_rewards)\n", + " print(f\"Terminal reward: mean={tr.mean():.4f}, std={tr.std():.4f}\")\n", + " n_positive = (tr > 0).sum()\n", + " n_negative = (tr < 0).sum()\n", + " n_zero = (tr == 0).sum()\n", + " print(f\" positive: {n_positive}, negative: {n_negative}, zero: {n_zero}\")\n", + "\n", + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "if term_steps:\n", + " ax.scatter(term_steps, term_rewards, c=\"red\", s=20, alpha=0.5, label=f\"terminal ({len(term_steps)})\")\n", + "if trunc_steps:\n", + " ax.scatter(trunc_steps, trunc_rewards, c=\"blue\", s=20, alpha=0.5, label=f\"truncation ({len(trunc_steps)})\")\n", + "ax.axhline(0, color=\"black\", lw=0.5)\n", + "ax.set_xlabel(\"Step\")\n", + "ax.set_ylabel(\"Reward at terminal/truncation\")\n", + "ax.set_title(\"Terminal events over 200 steps\")\n", + "ax.legend()\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Goal detection: high reward events" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "N_STEPS = 512\n", + "goal_events = []\n", + "\n", + "for t in range(N_STEPS):\n", + " prev_obs = obs.copy()\n", + " actions = np.random.randint(0, N_ACTIONS, size=ACT_SHAPE)\n", + " obs, rew, term, trunc, info = env.step(actions)\n", + " for i in range(N):\n", + " if rew[i] >= 0.5:\n", + " goal_dist = np.sqrt(prev_obs[i, 0] ** 2 + prev_obs[i, 1] ** 2)\n", + " goal_events.append((t, i, rew[i], goal_dist))\n", + "\n", + "print(f\"Goal-like events (reward >= 0.5): {len(goal_events)}\")\n", + "if goal_events:\n", + " events = np.array(goal_events)\n", + " print(f\"Reward range: [{events[:, 2].min():.3f}, {events[:, 2].max():.3f}]\")\n", + " print(f\"Goal distance at event: mean={events[:, 3].mean():.3f}, std={events[:, 3].std():.3f}\")\n", + "\n", + " fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n", + " axes[0].hist(events[:, 2], bins=20, edgecolor=\"black\", alpha=0.7, color=\"gold\")\n", + " axes[0].set_title(\"Reward magnitude at goal events\")\n", + " axes[0].set_xlabel(\"Reward\")\n", + " axes[1].scatter(events[:, 3], events[:, 2], alpha=0.5)\n", + " axes[1].set_xlabel(\"Goal distance before event\")\n", + " axes[1].set_ylabel(\"Reward\")\n", + " axes[1].set_title(\"Goal distance vs reward\")\n", + " plt.tight_layout()\n", + " plt.show()\n", + "else:\n", + " print(\"No goal events detected in 512 steps with random actions\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reward scale for PPO" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "all_rewards = rewards_history.flatten()\n", + "episodic_returns = rewards_history.sum(axis=0)\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n", + "axes[0].hist(all_rewards[all_rewards != 0], bins=50, edgecolor=\"black\", alpha=0.7)\n", + "axes[0].set_title(f\"Per-step reward distribution (non-zero, N={(all_rewards != 0).sum()})\")\n", + "axes[0].set_xlabel(\"Reward\")\n", + "\n", + "axes[1].hist(episodic_returns, bins=20, edgecolor=\"black\", alpha=0.7, color=\"purple\")\n", + "axes[1].set_title(f\"Episodic return (100 steps): mean={episodic_returns.mean():.3f}\")\n", + "axes[1].set_xlabel(\"Return\")\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "print(f\"Reward magnitude range: [{all_rewards.min():.5f}, {all_rewards.max():.5f}]\")\n", + "print(f\"Mean episodic return: {episodic_returns.mean():.4f} +/- {episodic_returns.std():.4f}\")\n", + "if abs(episodic_returns.mean()) > 10:\n", + " print(\"WARNING: large episodic returns, consider scaling\")\n", + "if episodic_returns.std() < 1e-6:\n", + " print(\"WARNING: near-zero return variance\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Action-reward correlation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "STEPS_PER_ACTION = 20\n", + "action_rewards = {}\n", + "\n", + "for a in range(N_ACTIONS):\n", + " rews = []\n", + " for _ in range(STEPS_PER_ACTION):\n", + " actions = np.full(ACT_SHAPE, a, dtype=np.int64)\n", + " obs, rew, term, trunc, info = env.step(actions)\n", + " rews.append(rew.mean())\n", + " action_rewards[a] = np.mean(rews)\n", + "\n", + "fig, ax = plt.subplots(figsize=(10, 5))\n", + "actions_list = sorted(action_rewards.keys())\n", + "means = [action_rewards[a] for a in actions_list]\n", + "colors = [\"green\" if m > 0 else \"red\" for m in means]\n", + "labels = [f\"{a // 3}L,{a % 3}R\" for a in actions_list]\n", + "ax.bar(range(len(actions_list)), means, tick_label=labels, color=colors, edgecolor=\"black\")\n", + "ax.set_xlabel(\"Action (longitudinal, lateral)\")\n", + "ax.set_ylabel(\"Mean reward\")\n", + "ax.set_title(f\"Mean reward per action over {STEPS_PER_ACTION} steps\")\n", + "ax.axhline(0, color=\"black\", lw=0.5)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/03_metrics.ipynb b/notebooks/03_metrics.ipynb new file mode 100644 index 0000000000..01fb4b9b10 --- /dev/null +++ b/notebooks/03_metrics.ipynb @@ -0,0 +1,400 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 03 - Episode Metrics & Logging Debug\n", + "Verify vec_log returns correct metrics, aggregation is sane, episode boundaries handled." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from pufferlib.ocean.drive.drive import Drive\n", + "from pufferlib.ocean.drive import binding\n", + "import pufferlib.viz\n", + "\n", + "# --- Environment configuration ---\n", + "NUM_AGENTS = 64\n", + "SIMULATION_MODE = \"gigaflow\"\n", + "DYNAMICS_MODEL = \"jerk\"\n", + "ACTION_TYPE = \"discrete\"\n", + "DT = 0.1\n", + "SCENARIO_LENGTH = 512\n", + "RESAMPLE_FREQUENCY = 0\n", + "REWARD_CONDITIONING = True\n", + "REWARD_RANDOMIZATION = False\n", + "TARGET_TYPE = \"static\"\n", + "COLLISION_BEHAVIOR = 1\n", + "OFFROAD_BEHAVIOR = 1\n", + "SEED = 42\n", + "MAP_DIR = \"../pufferlib/resources/drive/binaries/carla\"\n", + "\n", + "# --- Observation dimensions (configurable) ---\n", + "MAX_PARTNERS = 16\n", + "MAX_LANES = 32\n", + "MAX_BOUNDS = 32\n", + "MAX_TRAFFIC = 10\n", + "MAX_STOP_SIGNS = 0\n", + "\n", + "# --- Derived from binding (compile-time) ---\n", + "EGO_DIM = binding.EGO_FEATURES_JERK\n", + "NUM_COEFS = binding.NUM_REWARD_COEFS\n", + "PARTNER_F = binding.PARTNER_FEATURES\n", + "ROAD_F = binding.ROAD_FEATURES\n", + "TRAFFIC_F = binding.TRAFFIC_LIGHT_FEATURES\n", + "STOP_SIGN_F = binding.STOP_SIGN_FEATURES\n", + "COEF_NAMES = [\n", + " \"goal_radius\",\n", + " \"collision\",\n", + " \"offroad\",\n", + " \"comfort\",\n", + " \"lane_align\",\n", + " \"lane_center\",\n", + " \"velocity\",\n", + " \"traffic_light\",\n", + " \"center_bias\",\n", + " \"vel_align\",\n", + " \"overspeed\",\n", + " \"timestep\",\n", + " \"reverse\",\n", + " \"throttle\",\n", + " \"steer\",\n", + " \"acc\",\n", + "]\n", + "\n", + "# --- Create environment ---\n", + "env = Drive(\n", + " num_agents=NUM_AGENTS,\n", + " num_maps=1,\n", + " min_agents_per_env=NUM_AGENTS,\n", + " max_agents_per_env=NUM_AGENTS,\n", + " simulation_mode=SIMULATION_MODE,\n", + " dynamics_model=DYNAMICS_MODEL,\n", + " action_type=ACTION_TYPE,\n", + " dt=DT,\n", + " scenario_length=SCENARIO_LENGTH,\n", + " resample_frequency=RESAMPLE_FREQUENCY,\n", + " reward_conditioning=REWARD_CONDITIONING,\n", + " reward_randomization=REWARD_RANDOMIZATION,\n", + " target_type=TARGET_TYPE,\n", + " map_dir=MAP_DIR,\n", + " collision_behavior=COLLISION_BEHAVIOR,\n", + " offroad_behavior=OFFROAD_BEHAVIOR,\n", + " max_lane_segment_observations=MAX_LANES,\n", + " max_boundary_segment_observations=MAX_BOUNDS,\n", + " max_partner_observations=MAX_PARTNERS,\n", + " max_traffic_light_observations=MAX_TRAFFIC,\n", + " seed=SEED,\n", + ")\n", + "obs, info = env.reset(seed=SEED)\n", + "\n", + "# --- Derived from env ---\n", + "MAX_TARGET = env.num_target_waypoints\n", + "TARGET_F = binding.STATIC_TARGET_FEATURES if TARGET_TYPE == \"static\" else binding.DYNAMIC_TARGET_FEATURES\n", + "TARGET_DIM = MAX_TARGET * TARGET_F\n", + "N_ACTIONS = 12\n", + "N = env.num_agents\n", + "ACT_SHAPE = (N, len(env.single_action_space.nvec))\n", + "\n", + "print(f\"env ready: {N} agents, act_shape={ACT_SHAPE}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Single vec_log call" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for _ in range(10):\n", + " actions = np.random.randint(0, N_ACTIONS, size=ACT_SHAPE)\n", + " obs, rew, term, trunc, info = env.step(actions)\n", + "\n", + "log = binding.vec_log(env.c_envs, N)\n", + "print(f\"vec_log type: {type(log)}\")\n", + "if log:\n", + " print(f\"Keys: {sorted(log.keys())}\")\n", + " for k, v in sorted(log.items()):\n", + " print(f\" {k}: {v}\")\n", + "else:\n", + " print(\"vec_log returned empty/None\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 512-step collection: all info dicts" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "N_STEPS = 512\n", + "all_logs = []\n", + "all_rewards = np.zeros((N_STEPS, N))\n", + "all_terms = np.zeros((N_STEPS, N))\n", + "all_truncs = np.zeros((N_STEPS, N))\n", + "\n", + "for t in range(N_STEPS):\n", + " actions = np.random.randint(0, N_ACTIONS, size=ACT_SHAPE)\n", + " obs, rew, term, trunc, info = env.step(actions)\n", + " all_rewards[t] = rew\n", + " all_terms[t] = term\n", + " all_truncs[t] = trunc\n", + " if info:\n", + " for log_entry in info:\n", + " log_entry[\"_step\"] = t\n", + " all_logs.append(log_entry)\n", + "\n", + "print(f\"Collected {len(all_logs)} log entries over {N_STEPS} steps\")\n", + "if all_logs:\n", + " keys = set()\n", + " for log in all_logs:\n", + " keys.update(log.keys())\n", + " keys.discard(\"_step\")\n", + " print(f\"\\n{'Metric':>25s} | {'count':>5s} {'mean':>10s} {'std':>10s} {'min':>10s} {'max':>10s}\")\n", + " print(\"-\" * 75)\n", + " for k in sorted(keys):\n", + " vals = [log[k] for log in all_logs if k in log and isinstance(log[k], (int, float))]\n", + " if vals:\n", + " v = np.array(vals)\n", + " print(f\"{k:>25s} | {len(v):5d} {v.mean():10.4f} {v.std():10.4f} {v.min():10.4f} {v.max():10.4f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Metric definitions reference\n", + "\n", + "| Metric | Description |\n", + "|--------|-------------|\n", + "| score | Goals reached cleanly (no collision/offroad) |\n", + "| collision_rate | Fraction of agents that collided |\n", + "| offroad_rate | Fraction of agents that went off-road |\n", + "| completion_rate | Fraction that reached goal (even with collision/offroad) |\n", + "| lane_heading_aligned_rate | Fraction of steps with cos(theta) >= 0.5 (within ~60 deg of lane heading) |\n", + "| lane_center_rate | Lane centering metric average (same as reward term) |\n", + "| avg_collisions_per_agent | Average collision events per agent per episode |" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Terminal / truncation timeline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "term_per_step = all_terms.sum(axis=1)\n", + "trunc_per_step = all_truncs.sum(axis=1)\n", + "\n", + "fig, ax = plt.subplots(figsize=(14, 4))\n", + "ax.plot(term_per_step, label=\"terminals\", alpha=0.7, color=\"red\")\n", + "ax.plot(trunc_per_step, label=\"truncations\", alpha=0.7, color=\"blue\")\n", + "ax.set_xlabel(\"Step\")\n", + "ax.set_ylabel(\"Count\")\n", + "ax.set_title(\"Terminal/truncation events per step\")\n", + "ax.legend()\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "print(f\"Total terminals: {all_terms.sum():.0f}, truncations: {all_truncs.sum():.0f}\")\n", + "print(f\"Terminals per step: mean={term_per_step.mean():.2f}, max={term_per_step.max():.0f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Agent lifecycle trajectories" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "TRACK_STEPS = 100\n", + "TRACK_AGENTS = min(5, N)\n", + "xy_history = np.zeros((TRACK_STEPS, TRACK_AGENTS, 2))\n", + "\n", + "for t in range(TRACK_STEPS):\n", + " actions = np.random.randint(0, N_ACTIONS, size=ACT_SHAPE)\n", + " env.step(actions)\n", + " states = env.get_global_agent_state()\n", + " for i in range(TRACK_AGENTS):\n", + " xy_history[t, i, 0] = states[\"x\"][i]\n", + " xy_history[t, i, 1] = states[\"y\"][i]\n", + "\n", + "fig, ax = plt.subplots(figsize=(10, 10))\n", + "for i in range(TRACK_AGENTS):\n", + " ax.plot(xy_history[:, i, 0], xy_history[:, i, 1], \"-o\", markersize=2, alpha=0.7, label=f\"agent {i}\")\n", + " ax.scatter(xy_history[0, i, 0], xy_history[0, i, 1], s=100, marker=\"s\", zorder=10)\n", + " ax.scatter(xy_history[-1, i, 0], xy_history[-1, i, 1], s=100, marker=\"*\", zorder=10)\n", + "ax.set_xlabel(\"x\")\n", + "ax.set_ylabel(\"y\")\n", + "ax.set_title(f\"{TRACK_AGENTS} agent trajectories over {TRACK_STEPS} steps\")\n", + "ax.legend()\n", + "ax.set_aspect(\"equal\")\n", + "ax.grid(True, alpha=0.3)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Consistency checks" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if all_logs:\n", + " passed = 0\n", + " failed = 0\n", + " for log in all_logs:\n", + " if \"score\" in log and \"completion_rate\" in log:\n", + " if log[\"score\"] > log[\"completion_rate\"] + 1e-6:\n", + " print(\n", + " f\"FAIL: score ({log['score']:.4f}) > completion_rate ({log['completion_rate']:.4f}) at step {log['_step']}\"\n", + " )\n", + " failed += 1\n", + " else:\n", + " passed += 1\n", + " for rate_key in [\"collision_rate\", \"offroad_rate\", \"completion_rate\", \"score\"]:\n", + " if rate_key in log:\n", + " v = log[rate_key]\n", + " if v < -1e-6 or v > 1.0 + 1e-6:\n", + " print(f\"FAIL: {rate_key} = {v:.4f} outside [0,1] at step {log['_step']}\")\n", + " failed += 1\n", + " else:\n", + " passed += 1\n", + " print(f\"\\nConsistency checks: {passed} passed, {failed} failed\")\n", + "else:\n", + " print(\"No logs to check\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Gigaflow agent dynamics" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "episode_lengths = []\n", + "agent_step_count = np.zeros(N)\n", + "active_counts = []\n", + "\n", + "for t in range(N_STEPS):\n", + " active = (~np.all(all_rewards[: t + 1] == 0, axis=0) if t > 0 else np.ones(N, dtype=bool)).sum()\n", + " active_counts.append(active)\n", + " for i in range(N):\n", + " agent_step_count[i] += 1\n", + " if all_terms[t, i] or all_truncs[t, i]:\n", + " episode_lengths.append(agent_step_count[i])\n", + " agent_step_count[i] = 0\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(14, 4))\n", + "axes[0].plot(active_counts)\n", + "axes[0].set_xlabel(\"Step\")\n", + "axes[0].set_ylabel(\"Active agents\")\n", + "axes[0].set_title(\"Active agent count over time\")\n", + "\n", + "if episode_lengths:\n", + " axes[1].hist(episode_lengths, bins=30, edgecolor=\"black\", alpha=0.7)\n", + " axes[1].set_xlabel(\"Episode length (steps)\")\n", + " axes[1].set_title(f\"Episode length distribution (N={len(episode_lengths)})\")\n", + " print(f\"Episode lengths: mean={np.mean(episode_lengths):.1f}, median={np.median(episode_lengths):.1f}\")\n", + "else:\n", + " print(\"No episodes completed\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Score vs cumulative reward" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if all_logs and \"score\" in all_logs[0]:\n", + " scores = [log[\"score\"] for log in all_logs if \"score\" in log]\n", + " log_steps = [log[\"_step\"] for log in all_logs if \"score\" in log]\n", + " cum_rew_at_log = [all_rewards[: t + 1].sum() / N for t in log_steps]\n", + "\n", + " fig, ax = plt.subplots(figsize=(8, 6))\n", + " ax.scatter(cum_rew_at_log, scores, alpha=0.5)\n", + " ax.set_xlabel(\"Avg cumulative reward up to step\")\n", + " ax.set_ylabel(\"Score\")\n", + " ax.set_title(\"Score vs cumulative reward\")\n", + " ax.grid(True, alpha=0.3)\n", + " plt.tight_layout()\n", + " plt.show()\n", + "else:\n", + " print(\"No score data available\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/04_training.ipynb b/notebooks/04_training.ipynb new file mode 100644 index 0000000000..c27363c941 --- /dev/null +++ b/notebooks/04_training.ipynb @@ -0,0 +1,625 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 04 - RL Training Loop Debug\n", + "End-to-end data flow from env -> policy -> loss. Debug encoding, sampling, advantages, gradients." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import torch\n", + "import torch.nn.functional as F\n", + "from pufferlib.ocean.drive.drive import Drive\n", + "from pufferlib.ocean.drive import binding\n", + "from pufferlib.ocean.torch import Drive as DrivePolicy\n", + "\n", + "# --- Environment configuration ---\n", + "NUM_AGENTS = 64\n", + "SIMULATION_MODE = \"gigaflow\"\n", + "DYNAMICS_MODEL = \"jerk\"\n", + "ACTION_TYPE = \"discrete\"\n", + "DT = 0.1\n", + "SCENARIO_LENGTH = 512\n", + "RESAMPLE_FREQUENCY = 0\n", + "REWARD_CONDITIONING = True\n", + "REWARD_RANDOMIZATION = False\n", + "TARGET_TYPE = \"static\"\n", + "COLLISION_BEHAVIOR = 1\n", + "OFFROAD_BEHAVIOR = 1\n", + "SEED = 42\n", + "MAP_DIR = \"../pufferlib/resources/drive/binaries/carla\"\n", + "\n", + "# --- Observation dimensions (configurable) ---\n", + "MAX_PARTNERS = 16\n", + "MAX_LANES = 32\n", + "MAX_BOUNDS = 32\n", + "MAX_TRAFFIC = 10\n", + "MAX_STOP_SIGNS = 0\n", + "\n", + "# --- Derived from binding (compile-time) ---\n", + "EGO_DIM = binding.EGO_FEATURES_JERK\n", + "NUM_COEFS = binding.NUM_REWARD_COEFS\n", + "PARTNER_F = binding.PARTNER_FEATURES\n", + "ROAD_F = binding.ROAD_FEATURES\n", + "TRAFFIC_F = binding.TRAFFIC_LIGHT_FEATURES\n", + "STOP_SIGN_F = binding.STOP_SIGN_FEATURES\n", + "\n", + "# --- Create environment ---\n", + "env = Drive(\n", + " num_agents=NUM_AGENTS,\n", + " num_maps=1,\n", + " min_agents_per_env=NUM_AGENTS,\n", + " max_agents_per_env=NUM_AGENTS,\n", + " simulation_mode=SIMULATION_MODE,\n", + " dynamics_model=DYNAMICS_MODEL,\n", + " action_type=ACTION_TYPE,\n", + " dt=DT,\n", + " scenario_length=SCENARIO_LENGTH,\n", + " resample_frequency=RESAMPLE_FREQUENCY,\n", + " reward_conditioning=REWARD_CONDITIONING,\n", + " reward_randomization=REWARD_RANDOMIZATION,\n", + " target_type=TARGET_TYPE,\n", + " map_dir=MAP_DIR,\n", + " collision_behavior=COLLISION_BEHAVIOR,\n", + " offroad_behavior=OFFROAD_BEHAVIOR,\n", + " max_lane_segment_observations=MAX_LANES,\n", + " max_boundary_segment_observations=MAX_BOUNDS,\n", + " max_partner_observations=MAX_PARTNERS,\n", + " max_traffic_light_observations=MAX_TRAFFIC,\n", + " max_stop_sign_observations=MAX_STOP_SIGNS,\n", + " seed=SEED,\n", + ")\n", + "obs, info = env.reset(seed=SEED)\n", + "\n", + "# --- Derived from env ---\n", + "MAX_TARGET = env.num_target_waypoints\n", + "TARGET_F = binding.STATIC_TARGET_FEATURES if TARGET_TYPE == \"static\" else binding.DYNAMIC_TARGET_FEATURES\n", + "TARGET_DIM = MAX_TARGET * TARGET_F\n", + "N_ACTIONS = 12\n", + "N = env.num_agents\n", + "ACT_SHAPE = (N, len(env.single_action_space.nvec))\n", + "\n", + "# --- Policy ---\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "policy = DrivePolicy(\n", + " env,\n", + " input_size=64,\n", + " backbone_hidden_size=128,\n", + " backbone_num_layers=1,\n", + " actor_hidden_size=128,\n", + " actor_num_layers=0,\n", + " critic_hidden_size=128,\n", + " critic_num_layers=0,\n", + " encoder_gigaflow=True,\n", + " dropout=0.0,\n", + " split_network=False,\n", + ").to(device)\n", + "print(f\"Policy on {device}, params: {sum(p.numel() for p in policy.parameters()):,}\")\n", + "print(f\"Action dim: {policy.atn_dim}, act_shape: {ACT_SHAPE}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Optional: load checkpoint" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# CHECKPOINT_PATH = '/home/o-vcharrau/Workspace/PufferDrive-Valeo/runs/big_test_7/models/model_puffer_drive_000520.pt'\n", + "# state_dict = torch.load(CHECKPOINT_PATH, map_location=device)\n", + "# state_dict = {k.replace(\"module.\", \"\"): v for k, v in state_dict.items()}\n", + "# print('Checkpoint loaded')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Encode observations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "actions = np.zeros(ACT_SHAPE, dtype=np.int64)\n", + "obs, rew, term, trunc, info = env.step(actions)\n", + "\n", + "obs_tensor = torch.FloatTensor(obs).to(device)\n", + "with torch.no_grad():\n", + " hidden = policy.encode_observations(obs_tensor)\n", + "\n", + "print(f\"Hidden shape: {hidden.shape}\")\n", + "print(f\"Hidden stats: min={hidden.min():.4f}, max={hidden.max():.4f}, mean={hidden.mean():.4f}\")\n", + "print(f\"NaN in hidden: {torch.isnan(hidden).sum().item()}\")\n", + "print(f\"Dead neurons (always 0): {(hidden.abs().sum(dim=0) == 0).sum().item()}/{hidden.shape[1]}\")\n", + "print(f\"% near-zero (<1e-6): {(hidden.abs() < 1e-6).float().mean().item() * 100:.1f}%\")\n", + "\n", + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "ax.hist(hidden.cpu().numpy().flatten(), bins=50, edgecolor=\"black\", alpha=0.7)\n", + "ax.set_title(\"Hidden activation distribution\")\n", + "ax.set_xlabel(\"Activation value\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Action sampling" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with torch.no_grad():\n", + " action_logits, value = policy.decode_actions(hidden)\n", + "\n", + "for i, logit in enumerate(action_logits):\n", + " print(f\"Action head {i}: shape={logit.shape}\")\n", + " probs = F.softmax(logit, dim=-1)\n", + " entropy = -(probs * probs.log()).sum(dim=-1).mean()\n", + " max_entropy = np.log(logit.shape[-1])\n", + " print(f\" Entropy: {entropy:.4f} / {max_entropy:.4f} (max) = {entropy / max_entropy:.2%}\")\n", + " print(f\" Logit range: [{logit.min():.3f}, {logit.max():.3f}]\")\n", + "\n", + "print(f\"\\nValue: mean={value.mean():.4f}, std={value.std():.4f}\")\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(14, 4))\n", + "probs = F.softmax(action_logits[0], dim=-1)\n", + "mean_probs = probs.mean(dim=0).cpu().numpy()\n", + "axes[0].bar(range(len(mean_probs)), mean_probs, edgecolor=\"black\", alpha=0.7)\n", + "axes[0].axhline(1.0 / len(mean_probs), color=\"red\", ls=\"--\", label=\"uniform\")\n", + "axes[0].set_xlabel(\"Action\")\n", + "axes[0].set_ylabel(\"Probability\")\n", + "axes[0].set_title(\"Mean action probabilities\")\n", + "axes[0].legend()\n", + "\n", + "axes[1].hist(value.cpu().numpy().flatten(), bins=20, edgecolor=\"black\", alpha=0.7, color=\"purple\")\n", + "axes[1].set_title(\"Value predictions\")\n", + "axes[1].set_xlabel(\"Value\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Manual encode trace: check each encoder for NaN" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x = obs_tensor\n", + "backbone = policy.actor_backbone\n", + "slide_idx = EGO_DIM\n", + "\n", + "ego_obs = x[:, :slide_idx]\n", + "print(\n", + " f\"ego_obs: shape={ego_obs.shape}, NaN={torch.isnan(ego_obs).sum().item()}, range=[{ego_obs.min():.3f}, {ego_obs.max():.3f}]\"\n", + ")\n", + "\n", + "cond_dim = backbone.conditioning_dim\n", + "if cond_dim > 0:\n", + " cond_obs = x[:, slide_idx : slide_idx + cond_dim]\n", + " slide_idx += cond_dim\n", + " print(f\"cond_obs: shape={cond_obs.shape}, NaN={torch.isnan(cond_obs).sum().item()}\")\n", + "\n", + "partner_dim = MAX_PARTNERS * PARTNER_F\n", + "lane_dim = MAX_LANES * ROAD_F\n", + "boundary_dim = MAX_BOUNDS * ROAD_F\n", + "\n", + "partner_obs = x[:, slide_idx : slide_idx + partner_dim]\n", + "slide_idx += partner_dim\n", + "lane_obs = x[:, slide_idx : slide_idx + lane_dim]\n", + "slide_idx += lane_dim\n", + "boundary_obs = x[:, slide_idx : slide_idx + boundary_dim]\n", + "slide_idx += boundary_dim\n", + "\n", + "with torch.no_grad():\n", + " ego_enc = backbone.ego_encoder(ego_obs)\n", + " partner_enc, _ = backbone.partner_encoder(partner_obs.view(-1, MAX_PARTNERS, PARTNER_F)).max(dim=1)\n", + " lane_enc, _ = backbone.lane_encoder(lane_obs.view(-1, MAX_LANES, ROAD_F)).max(dim=1)\n", + " bound_enc, _ = backbone.boundary_encoder(boundary_obs.view(-1, MAX_BOUNDS, ROAD_F)).max(dim=1)\n", + "\n", + "for name, enc in [(\"ego\", ego_enc), (\"partner\", partner_enc), (\"lane\", lane_enc), (\"boundary\", bound_enc)]:\n", + " print(\n", + " f\"{name:>10s}_enc: NaN={torch.isnan(enc).sum().item()}, dead={((enc.abs().sum(dim=0) == 0).sum().item())}, range=[{enc.min():.3f}, {enc.max():.3f}]\"\n", + " )\n", + "\n", + "if cond_dim > 0:\n", + " with torch.no_grad():\n", + " cond_enc = backbone.conditioning_encoder(cond_obs)\n", + " print(\n", + " f\"{'cond':>10s}_enc: NaN={torch.isnan(cond_enc).sum().item()}, dead={((cond_enc.abs().sum(dim=0) == 0).sum().item())}, range=[{cond_enc.min():.3f}, {cond_enc.max():.3f}]\"\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Forward-backward: fake advantage, loss, grads" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "policy.train()\n", + "optimizer = torch.optim.Adam(policy.parameters(), lr=3e-4)\n", + "\n", + "action_logits_list, value = policy(obs_tensor)\n", + "\n", + "fake_actions = torch.randint(0, N_ACTIONS, (N,), device=device)\n", + "fake_advantages = torch.randn(N, device=device)\n", + "fake_returns = torch.randn(N, device=device)\n", + "fake_old_logprobs = torch.randn(N, device=device)\n", + "\n", + "logits = action_logits_list[0]\n", + "dist = torch.distributions.Categorical(logits=logits)\n", + "new_logprobs = dist.log_prob(fake_actions)\n", + "entropy = dist.entropy()\n", + "\n", + "ratio = torch.exp(new_logprobs - fake_old_logprobs)\n", + "clip_coef = 0.2\n", + "pg_loss1 = -fake_advantages * ratio\n", + "pg_loss2 = -fake_advantages * torch.clamp(ratio, 1 - clip_coef, 1 + clip_coef)\n", + "pg_loss = torch.max(pg_loss1, pg_loss2).mean()\n", + "v_loss = 0.5 * ((value.squeeze() - fake_returns) ** 2).mean()\n", + "entropy_loss = entropy.mean()\n", + "loss = pg_loss + 0.5 * v_loss - 0.01 * entropy_loss\n", + "\n", + "print(f\"pg_loss: {pg_loss.item():.4f}\")\n", + "print(f\"v_loss: {v_loss.item():.4f}\")\n", + "print(f\"entropy: {entropy_loss.item():.4f}\")\n", + "print(f\"total: {loss.item():.4f}\")\n", + "print(f\"ratio: mean={ratio.mean():.4f}, std={ratio.std():.4f}\")\n", + "\n", + "optimizer.zero_grad()\n", + "loss.backward()\n", + "total_grad_norm = torch.nn.utils.clip_grad_norm_(policy.parameters(), float(\"inf\"))\n", + "print(f\"\\nTotal grad norm: {total_grad_norm:.4f}\")\n", + "print(f\"NaN in loss: {torch.isnan(loss).item()}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Gradient flow: per-parameter analysis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(f\"{'Parameter':>45s} | {'shape':>20s} | {'grad_norm':>10s} {'grad_mean':>10s} {'grad_max':>10s} | flag\")\n", + "print(\"-\" * 120)\n", + "for name, param in policy.named_parameters():\n", + " if param.grad is not None:\n", + " g = param.grad\n", + " norm = g.norm().item()\n", + " mean = g.mean().item()\n", + " mx = g.abs().max().item()\n", + " flag = \"\"\n", + " if norm == 0:\n", + " flag = \"ZERO GRAD\"\n", + " elif norm > 100:\n", + " flag = \"EXPLODING\"\n", + " elif norm < 1e-7:\n", + " flag = \"VANISHING\"\n", + " print(f\"{name:>45s} | {str(list(param.shape)):>20s} | {norm:10.6f} {mean:10.6f} {mx:10.6f} | {flag}\")\n", + " else:\n", + " print(f\"{name:>45s} | {str(list(param.shape)):>20s} | NO GRAD\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Experience buffer simulation: 128-step rollout" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "HORIZON = 128\n", + "obs_dim = obs.shape[1]\n", + "\n", + "obs_buf = np.zeros((HORIZON, N, obs_dim), dtype=np.float32)\n", + "act_buf = np.zeros((HORIZON, N), dtype=np.int64)\n", + "rew_buf = np.zeros((HORIZON, N), dtype=np.float32)\n", + "val_buf = np.zeros((HORIZON, N), dtype=np.float32)\n", + "logp_buf = np.zeros((HORIZON, N), dtype=np.float32)\n", + "done_buf = np.zeros((HORIZON, N), dtype=np.float32)\n", + "\n", + "policy.eval()\n", + "for t in range(HORIZON):\n", + " obs_t = torch.FloatTensor(obs).to(device)\n", + " with torch.no_grad():\n", + " logits_list, val = policy(obs_t)\n", + " dist = torch.distributions.Categorical(logits=logits_list[0])\n", + " act = dist.sample()\n", + " logp = dist.log_prob(act)\n", + "\n", + " obs_buf[t] = obs\n", + " act_buf[t] = act.cpu().numpy()\n", + " val_buf[t] = val.squeeze().cpu().numpy()\n", + " logp_buf[t] = logp.cpu().numpy()\n", + "\n", + " # Reshape (N,) -> (N, 1) for env.step with MultiDiscrete\n", + " env_actions = act.cpu().numpy().reshape(ACT_SHAPE)\n", + " obs, rew, term, trunc, info = env.step(env_actions)\n", + " rew_buf[t] = rew\n", + " done_buf[t] = term | trunc\n", + "\n", + "print(f\"Buffer shapes: obs={obs_buf.shape}, act={act_buf.shape}, rew={rew_buf.shape}\")\n", + "print(f\"Reward stats: mean={rew_buf.mean():.5f}, std={rew_buf.std():.5f}\")\n", + "print(f\"Value stats: mean={val_buf.mean():.5f}, std={val_buf.std():.5f}\")\n", + "print(f\"Done count: {done_buf.sum():.0f}\")\n", + "print(f\"LogProb stats: mean={logp_buf.mean():.4f}, std={logp_buf.std():.4f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## GAE advantage computation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gamma, lam = 0.98, 0.95\n", + "advantages = np.zeros_like(rew_buf)\n", + "\n", + "last_gae = np.zeros(N)\n", + "for t in reversed(range(HORIZON - 1)):\n", + " next_non_terminal = 1.0 - done_buf[t + 1]\n", + " delta = rew_buf[t + 1] + gamma * val_buf[t + 1] * next_non_terminal - val_buf[t]\n", + " last_gae = delta + gamma * lam * last_gae * next_non_terminal\n", + " advantages[t] = last_gae\n", + "\n", + "returns = advantages + val_buf\n", + "\n", + "print(f\"Advantages: mean={advantages.mean():.5f}, std={advantages.std():.5f}\")\n", + "print(f\"Returns: mean={returns.mean():.5f}, std={returns.std():.5f}\")\n", + "print(f\"Advantage vs Return corr: {np.corrcoef(advantages.flatten(), returns.flatten())[0, 1]:.4f}\")\n", + "\n", + "fig, axes = plt.subplots(1, 4, figsize=(18, 4))\n", + "axes[0].hist(advantages.flatten(), bins=50, edgecolor=\"black\", alpha=0.7)\n", + "axes[0].set_title(f\"Advantage distribution (std={advantages.std():.4f})\")\n", + "\n", + "axes[1].hist(returns.flatten(), bins=50, edgecolor=\"black\", alpha=0.7, color=\"orange\")\n", + "axes[1].set_title(\"Returns distribution\")\n", + "\n", + "axes[2].plot(advantages.mean(axis=1))\n", + "axes[2].set_xlabel(\"Step\")\n", + "axes[2].set_ylabel(\"Mean advantage\")\n", + "axes[2].set_title(\"Mean advantage over time\")\n", + "\n", + "axes[3].plot(done_buf.mean(axis=1), color=\"orange\")\n", + "axes[3].set_xlabel(\"Step\")\n", + "axes[3].set_ylabel(\"Mean done\")\n", + "axes[3].set_title(\"Mean done over time\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## PPO loss components" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "MB = 16\n", + "mb_obs = torch.FloatTensor(obs_buf[:MB].reshape(-1, obs_dim)).to(device)\n", + "mb_act = torch.LongTensor(act_buf[:MB].flatten()).to(device)\n", + "mb_old_logp = torch.FloatTensor(logp_buf[:MB].flatten()).to(device)\n", + "mb_adv = torch.FloatTensor(advantages[:MB].flatten()).to(device)\n", + "mb_ret = torch.FloatTensor(returns[:MB].flatten()).to(device)\n", + "mb_old_val = torch.FloatTensor(val_buf[:MB].flatten()).to(device)\n", + "\n", + "mb_adv = (mb_adv - mb_adv.mean()) / (mb_adv.std() + 1e-8)\n", + "\n", + "policy.train()\n", + "logits_list, newvalue = policy(mb_obs)\n", + "newvalue = newvalue.squeeze()\n", + "dist = torch.distributions.Categorical(logits=logits_list[0])\n", + "new_logp = dist.log_prob(mb_act)\n", + "entropy = dist.entropy()\n", + "\n", + "ratio = torch.exp(new_logp - mb_old_logp)\n", + "print(f\"Ratio: mean={ratio.mean():.4f}, std={ratio.std():.4f}, min={ratio.min():.4f}, max={ratio.max():.4f}\")\n", + "if ratio.mean() < 0.5 or ratio.mean() > 2.0:\n", + " print(\"WARNING: ratio far from 1.0, policy may have diverged\")\n", + "\n", + "clip_coef = 0.2\n", + "pg_loss1 = -mb_adv * ratio\n", + "pg_loss2 = -mb_adv * torch.clamp(ratio, 1 - clip_coef, 1 + clip_coef)\n", + "pg_loss = torch.max(pg_loss1, pg_loss2).mean()\n", + "\n", + "vf_clip = 0.2\n", + "v_clipped = mb_old_val + torch.clamp(newvalue - mb_old_val, -vf_clip, vf_clip)\n", + "v_loss_unclipped = (newvalue - mb_ret) ** 2\n", + "v_loss_clipped = (v_clipped - mb_ret) ** 2\n", + "v_loss = 0.5 * torch.max(v_loss_unclipped, v_loss_clipped).mean()\n", + "\n", + "entropy_loss = entropy.mean()\n", + "\n", + "print(f\"\\npg_loss: {pg_loss.item():.6f}\")\n", + "print(f\"v_loss: {v_loss.item():.6f}\")\n", + "print(f\"entropy: {entropy_loss.item():.6f} (max={np.log(N_ACTIONS):.4f})\")\n", + "print(f\"total: {(pg_loss + 0.5 * v_loss - 0.01 * entropy_loss).item():.6f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5-epoch sanity training" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "optimizer = torch.optim.Adam(policy.parameters(), lr=3e-4)\n", + "all_obs = torch.FloatTensor(obs_buf.reshape(-1, obs_dim)).to(device)\n", + "all_act = torch.LongTensor(act_buf.flatten()).to(device)\n", + "all_old_logp = torch.FloatTensor(logp_buf.flatten()).to(device)\n", + "all_adv = torch.FloatTensor(advantages.flatten()).to(device)\n", + "all_ret = torch.FloatTensor(returns.flatten()).to(device)\n", + "\n", + "all_adv = (all_adv - all_adv.mean()) / (all_adv.std() + 1e-8)\n", + "\n", + "N_EPOCHS = 5\n", + "history = {\"pg_loss\": [], \"v_loss\": [], \"entropy\": [], \"kl\": []}\n", + "\n", + "policy.train()\n", + "for epoch in range(N_EPOCHS):\n", + " logits_list, newval = policy(all_obs)\n", + " newval = newval.squeeze()\n", + " dist = torch.distributions.Categorical(logits=logits_list[0])\n", + " new_logp = dist.log_prob(all_act)\n", + " ent = dist.entropy().mean()\n", + "\n", + " ratio = torch.exp(new_logp - all_old_logp)\n", + " approx_kl = (all_old_logp - new_logp).mean()\n", + "\n", + " pg1 = -all_adv * ratio\n", + " pg2 = -all_adv * torch.clamp(ratio, 0.8, 1.2)\n", + " pg = torch.max(pg1, pg2).mean()\n", + " vl = 0.5 * ((newval - all_ret) ** 2).mean()\n", + " loss = pg + 0.5 * vl - 0.01 * ent\n", + "\n", + " optimizer.zero_grad()\n", + " loss.backward()\n", + " torch.nn.utils.clip_grad_norm_(policy.parameters(), 0.5)\n", + " optimizer.step()\n", + "\n", + " history[\"pg_loss\"].append(pg.item())\n", + " history[\"v_loss\"].append(vl.item())\n", + " history[\"entropy\"].append(ent.item())\n", + " history[\"kl\"].append(approx_kl.item())\n", + " print(f\"Epoch {epoch}: pg={pg.item():.5f}, v={vl.item():.5f}, ent={ent.item():.4f}, kl={approx_kl.item():.5f}\")\n", + "\n", + "fig, axes = plt.subplots(1, 4, figsize=(16, 3))\n", + "for i, (key, color) in enumerate(zip(history.keys(), [\"red\", \"blue\", \"green\", \"orange\"])):\n", + " axes[i].plot(history[key], \"-o\", color=color)\n", + " axes[i].set_title(key)\n", + " axes[i].set_xlabel(\"Epoch\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Value accuracy: predicted vs actual returns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "policy.eval()\n", + "with torch.no_grad():\n", + " _, pred_values = policy(all_obs)\n", + "pred_values = pred_values.squeeze().cpu().numpy()\n", + "actual_returns = returns.flatten()\n", + "\n", + "var_actual = np.var(actual_returns)\n", + "explained_var = 1 - np.var(actual_returns - pred_values) / (var_actual + 1e-8) if var_actual > 1e-8 else 0.0\n", + "\n", + "fig, ax = plt.subplots(figsize=(7, 7))\n", + "ax.scatter(actual_returns, pred_values, alpha=0.3, s=10)\n", + "lims = [min(actual_returns.min(), pred_values.min()), max(actual_returns.max(), pred_values.max())]\n", + "ax.plot(lims, lims, \"r--\", label=\"perfect\")\n", + "ax.set_xlabel(\"Actual return\")\n", + "ax.set_ylabel(\"Predicted value\")\n", + "ax.set_title(f\"Value accuracy (explained var: {explained_var:.4f})\")\n", + "ax.legend()\n", + "ax.grid(True, alpha=0.3)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "print(f\"Explained variance: {explained_var:.4f}\")\n", + "print(f\"Value MSE: {np.mean((actual_returns - pred_values) ** 2):.6f}\")\n", + "if explained_var < 0:\n", + " print(\"WARNING: negative explained variance, value head worse than predicting mean\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/05_inference.ipynb b/notebooks/05_inference.ipynb new file mode 100644 index 0000000000..640835d0fe --- /dev/null +++ b/notebooks/05_inference.ipynb @@ -0,0 +1,1682 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "cell-0", + "metadata": {}, + "source": [ + "# 05 - Model Inference Debug\n", + "End-to-end inference pipeline: config loading, policy forward pass, rollouts (deterministic vs stochastic), observation/reward analysis, value accuracy, trajectories, LSTM state." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-1", + "metadata": {}, + "outputs": [], + "source": [ + "import os, ast, glob, yaml, configparser\n", + "from collections import defaultdict\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import torch\n", + "import torch.nn.functional as F\n", + "\n", + "while not os.path.exists(\"pufferlib\") and os.getcwd() != \"/\":\n", + " os.chdir(\"..\")\n", + "print(\"CWD:\", os.getcwd())\n", + "\n", + "# --- Config ---\n", + "CHECKPOINT_PATH = \"\"\n", + "ENV_NAME = \"puffer_drive\"\n", + "\n", + "# --- Observation dimensions (configurable) ---\n", + "MAX_PARTNERS = 16\n", + "MAX_LANES = 32\n", + "MAX_BOUNDS = 32\n", + "MAX_TRAFFIC = 10\n", + "MAX_STOP_SIGNS = 0\n", + "\n", + "\n", + "def load_notebook_config(checkpoint_path=None, env_name=\"puffer_drive\"):\n", + " \"\"\"Load config from INI defaults, optionally overlaying checkpoint's config.yaml.\"\"\"\n", + " default_ini = \"pufferlib/config/default.ini\"\n", + " env_ini = None\n", + " for path in glob.glob(\"pufferlib/config/**/*.ini\", recursive=True):\n", + " p = configparser.ConfigParser()\n", + " p.read([default_ini, path])\n", + " if p.has_option(\"base\", \"env_name\") and env_name in p[\"base\"][\"env_name\"]:\n", + " env_ini = path\n", + " break\n", + " assert env_ini, f\"No config for {env_name}\"\n", + "\n", + " def parse_val(v):\n", + " try:\n", + " return ast.literal_eval(v)\n", + " except:\n", + " return v\n", + "\n", + " args = defaultdict(dict)\n", + " for section in p.sections():\n", + " for key in p[section]:\n", + " val = parse_val(p[section][key])\n", + " if section == \"base\":\n", + " args[key] = val\n", + " else:\n", + " args[section][key] = val\n", + "\n", + " # Overlay checkpoint config.yaml if exists\n", + " if checkpoint_path:\n", + " exp_dir = os.path.dirname(os.path.dirname(checkpoint_path))\n", + " cfg_yaml = os.path.join(exp_dir, \"config.yaml\")\n", + " if os.path.exists(cfg_yaml):\n", + " print(f\"Loading config.yaml from {cfg_yaml}\")\n", + " with open(cfg_yaml) as f:\n", + " ycfg = yaml.safe_load(f)\n", + " for section in [\"env\", \"train\", \"policy\", \"rnn\"]:\n", + " if section in ycfg and isinstance(ycfg[section], dict):\n", + " for k, v in ycfg[section].items():\n", + " args[section][k] = v\n", + "\n", + " args[\"train\"][\"use_rnn\"] = args.get(\"rnn_name\") is not None\n", + " return dict(args)\n", + "\n", + "\n", + "config = load_notebook_config(CHECKPOINT_PATH, ENV_NAME)\n", + "\n", + "# --- Env ---\n", + "from pufferlib.ocean.drive.drive import Drive\n", + "from pufferlib.ocean.drive import binding\n", + "\n", + "# Override for notebook: fewer agents, single env\n", + "config[\"env\"][\"num_agents\"] = 64\n", + "config[\"env\"][\"num_maps\"] = 1\n", + "config[\"env\"][\"eval_mode\"] = 1\n", + "config[\"env\"][\"map_dir\"] = \"pufferlib/resources/drive/binaries/carla\"\n", + "config[\"env\"][\"max_lane_segment_observations\"] = MAX_LANES\n", + "config[\"env\"][\"max_boundary_segment_observations\"] = MAX_BOUNDS\n", + "config[\"env\"][\"max_partner_observations\"] = MAX_PARTNERS\n", + "config[\"env\"][\"max_traffic_light_observations\"] = MAX_TRAFFIC\n", + "config[\"env\"][\"max_stop_sign_observations\"] = MAX_STOP_SIGNS\n", + "config[\"eval\"][\"map_dir\"] = \"pufferlib/resources/drive/binaries/carla\"\n", + "\n", + "env = Drive(**config[\"env\"])\n", + "obs, info = env.reset(seed=42)\n", + "N = env.num_agents\n", + "\n", + "# --- Derived from binding ---\n", + "EGO_DIM = binding.EGO_FEATURES_JERK\n", + "NUM_COEFS = binding.NUM_REWARD_COEFS\n", + "PARTNER_F = binding.PARTNER_FEATURES\n", + "ROAD_F = binding.ROAD_FEATURES\n", + "TRAFFIC_F = binding.TRAFFIC_LIGHT_FEATURES\n", + "STOP_SIGN_F = binding.STOP_SIGN_FEATURES\n", + "\n", + "# --- Policy ---\n", + "from pufferlib.ocean.torch import Drive as DrivePolicy, Recurrent\n", + "import pufferlib.pytorch\n", + "\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "policy = DrivePolicy(env, **config[\"policy\"]).to(device)\n", + "use_rnn = config[\"train\"].get(\"use_rnn\", False)\n", + "if use_rnn:\n", + " policy = Recurrent(env, policy, **config[\"rnn\"]).to(device)\n", + "\n", + "# Load checkpoint weights if provided\n", + "if CHECKPOINT_PATH:\n", + " sd = torch.load(CHECKPOINT_PATH, map_location=device)\n", + " sd = {k.replace(\"module.\", \"\"): v for k, v in sd.items()}\n", + " policy.load_state_dict(sd)\n", + " print(f\"Loaded checkpoint: {CHECKPOINT_PATH}\")\n", + "\n", + "# LSTM state\n", + "hidden_size = policy.hidden_size\n", + "state = {}\n", + "if use_rnn:\n", + " state = {\n", + " \"lstm_h\": torch.zeros(N, hidden_size, device=device),\n", + " \"lstm_c\": torch.zeros(N, hidden_size, device=device),\n", + " }\n", + "\n", + "# Action shape\n", + "inner_policy = policy.policy if use_rnn else policy\n", + "is_continuous = inner_policy.is_continuous\n", + "ACT_SHAPE = (N, len(env.single_action_space.nvec)) if not is_continuous else (N, env.single_action_space.shape[0])\n", + "\n", + "print(f\"Policy on {device}, params: {sum(p.numel() for p in policy.parameters()):,}\")\n", + "print(f\"Obs shape: {obs.shape}, Action space: {env.single_action_space}\")\n", + "print(f\"Config: dynamics={config['env']['dynamics_model']}, action={config['env']['action_type']}\")\n", + "print(f\"Use RNN: {use_rnn}, hidden_size: {hidden_size}\")" + ] + }, + { + "cell_type": "markdown", + "id": "cell-2", + "metadata": {}, + "source": [ + "## Single-step policy output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-3", + "metadata": {}, + "outputs": [], + "source": [ + "# Take one step to get fresh obs\n", + "actions = np.zeros(ACT_SHAPE, dtype=np.int64 if not is_continuous else np.float32)\n", + "obs, rew, term, trunc, info = env.step(actions)\n", + "\n", + "obs_tensor = torch.FloatTensor(obs).to(device)\n", + "policy.eval()\n", + "\n", + "with torch.no_grad():\n", + " if use_rnn:\n", + " logits_list, value = policy.forward_eval(obs_tensor, state)\n", + " else:\n", + " logits_list, value = policy(obs_tensor)\n", + "\n", + "# Sample actions\n", + "action, logprob, ent = pufferlib.pytorch.sample_logits(logits_list)\n", + "action_det, _, _ = pufferlib.pytorch.sample_logits(logits_list, deterministic=True)\n", + "\n", + "print(f\"Value: mean={value.mean():.4f}, std={value.std():.4f}, range=[{value.min():.4f}, {value.max():.4f}]\")\n", + "print(f\"Entropy: mean={ent.mean():.4f}, std={ent.std():.4f}\")\n", + "print(f\"LogProb: mean={logprob.mean():.4f}, std={logprob.std():.4f}\")\n", + "print(f\"Stochastic action sample: {action[0].cpu().numpy()}\")\n", + "print(f\"Deterministic action: {action_det[0].cpu().numpy()}\")\n", + "\n", + "# Plot\n", + "fig, axes = plt.subplots(1, 2, figsize=(14, 4))\n", + "\n", + "# Action probs (first head for multi-discrete, or full logits)\n", + "if isinstance(logits_list, list) or isinstance(logits_list, tuple):\n", + " probs = F.softmax(logits_list[0], dim=-1)\n", + "else:\n", + " probs = F.softmax(logits_list, dim=-1)\n", + "mean_probs = probs.mean(dim=0).cpu().numpy()\n", + "axes[0].bar(range(len(mean_probs)), mean_probs, edgecolor=\"black\", alpha=0.7)\n", + "axes[0].axhline(1.0 / len(mean_probs), color=\"red\", ls=\"--\", label=\"uniform\")\n", + "axes[0].set_xlabel(\"Action\")\n", + "axes[0].set_ylabel(\"Probability\")\n", + "axes[0].set_title(\"Mean action probabilities (across agents)\")\n", + "axes[0].legend()\n", + "\n", + "axes[1].hist(value.cpu().numpy().flatten(), bins=30, edgecolor=\"black\", alpha=0.7, color=\"purple\")\n", + "axes[1].set_title(\"Value predictions across agents\")\n", + "axes[1].set_xlabel(\"Value\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "cell-4", + "metadata": {}, + "source": [ + "## Full rollout: deterministic vs stochastic" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-5", + "metadata": {}, + "outputs": [], + "source": [ + "HORIZON = 256\n", + "TRACKED_AGENT = 0 # agent index to track in detail\n", + "obs_dim = obs.shape[1]\n", + "\n", + "dyn_model = config[\"env\"][\"dynamics_model\"]\n", + "tgt_type = config[\"env\"][\"target_type\"]\n", + "rew_cond = config[\"env\"].get(\"reward_conditioning\", False)\n", + "n_tgt_wp = config[\"env\"].get(\"num_target_waypoints\", 3)\n", + "\n", + "\n", + "def run_rollout(env, policy, deterministic=False, horizon=HORIZON):\n", + " obs, _ = env.reset(seed=42)\n", + " N = env.num_agents\n", + " st = {}\n", + " if use_rnn:\n", + " st = {\n", + " \"lstm_h\": torch.zeros(N, hidden_size, device=device),\n", + " \"lstm_c\": torch.zeros(N, hidden_size, device=device),\n", + " }\n", + "\n", + " buffers = {\n", + " \"obs\": np.zeros((horizon, N, obs_dim), dtype=np.float32),\n", + " \"actions\": np.zeros((horizon, N), dtype=np.int64),\n", + " \"rewards\": np.zeros((horizon, N), dtype=np.float32),\n", + " \"values\": np.zeros((horizon, N), dtype=np.float32),\n", + " \"logprobs\": np.zeros((horizon, N), dtype=np.float32),\n", + " \"entropy\": np.zeros((horizon, N), dtype=np.float32),\n", + " \"terminals\": np.zeros((horizon, N), dtype=np.float32),\n", + " \"truncations\": np.zeros((horizon, N), dtype=np.float32),\n", + " \"positions_x\": np.zeros((horizon, N), dtype=np.float32),\n", + " \"positions_y\": np.zeros((horizon, N), dtype=np.float32),\n", + " }\n", + " if use_rnn:\n", + " buffers[\"lstm_h_norm\"] = np.zeros((horizon, N), dtype=np.float32)\n", + "\n", + " policy.eval()\n", + " for t in range(horizon):\n", + " obs_t = torch.FloatTensor(obs).to(device)\n", + " with torch.no_grad():\n", + " if use_rnn:\n", + " logits_list, val = policy.forward_eval(obs_t, st)\n", + " else:\n", + " logits_list, val = policy(obs_t)\n", + " act, logp, entr = pufferlib.pytorch.sample_logits(logits_list, deterministic=deterministic)\n", + "\n", + " buffers[\"obs\"][t] = obs\n", + " buffers[\"actions\"][t] = act.cpu().numpy().reshape(N) if act.dim() > 1 else act.cpu().numpy()\n", + " buffers[\"values\"][t] = val.squeeze().cpu().numpy()\n", + " buffers[\"logprobs\"][t] = logp.cpu().numpy()\n", + " buffers[\"entropy\"][t] = entr.cpu().numpy()\n", + "\n", + " if use_rnn:\n", + " buffers[\"lstm_h_norm\"][t] = st[\"lstm_h\"].norm(dim=-1).cpu().numpy()\n", + "\n", + " # Get positions\n", + " gstate = env.get_global_agent_state()\n", + " buffers[\"positions_x\"][t] = gstate[\"x\"]\n", + " buffers[\"positions_y\"][t] = gstate[\"y\"]\n", + "\n", + " # Step env\n", + " env_actions = act.cpu().numpy().reshape(ACT_SHAPE)\n", + " obs, rew, term, trunc, info = env.step(env_actions)\n", + " buffers[\"rewards\"][t] = rew\n", + " buffers[\"terminals\"][t] = term\n", + " buffers[\"truncations\"][t] = trunc\n", + "\n", + " return buffers\n", + "\n", + "\n", + "print(\"Running stochastic rollout...\")\n", + "buf_stoch = run_rollout(env, policy, deterministic=False)\n", + "print(\"Running deterministic rollout...\")\n", + "buf_det = run_rollout(env, policy, deterministic=True)\n", + "\n", + "for name, buf in [(\"Stochastic\", buf_stoch), (\"Deterministic\", buf_det)]:\n", + " print(f\"\\n--- {name} ---\")\n", + " print(f\" Reward: mean={buf['rewards'].mean():.5f}, std={buf['rewards'].std():.5f}\")\n", + " print(f\" Value: mean={buf['values'].mean():.5f}, std={buf['values'].std():.5f}\")\n", + " print(f\" Entropy: mean={buf['entropy'].mean():.4f}\")\n", + " print(f\" Terminals: {buf['terminals'].sum():.0f}, Truncations: {buf['truncations'].sum():.0f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "cell-6", + "metadata": {}, + "source": [ + "## Observation analysis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-7", + "metadata": {}, + "outputs": [], + "source": [ + "from pufferlib.viz import unpack_obs, plot_observation, plot_simulator_state\n", + "\n", + "# Ego-centric observation at t=50 for tracked agent\n", + "sample_t = min(50, HORIZON - 1)\n", + "sample_obs = buf_stoch[\"obs\"][sample_t : sample_t + 1, TRACKED_AGENT : TRACKED_AGENT + 1][0]\n", + "print(dyn_model, tgt_type, rew_cond, n_tgt_wp)\n", + "img = plot_observation(\n", + " sample_obs,\n", + " dynamics_model=dyn_model,\n", + " target_type=tgt_type,\n", + " reward_conditioning=rew_cond,\n", + " num_target_waypoints=n_tgt_wp,\n", + " max_stop_signs=MAX_STOP_SIGNS,\n", + ")\n", + "plt.figure(figsize=(10, 10))\n", + "plt.imshow(img)\n", + "plt.axis(\"off\")\n", + "plt.title(f\"Ego-centric obs | agent={TRACKED_AGENT}, t={sample_t}\")\n", + "plt.show()\n", + "\n", + "# BEV simulator state\n", + "scenarios = env.get_state()\n", + "if scenarios and len(scenarios) > 0:\n", + " img_bev = plot_simulator_state(scenarios[0], timestep=0)\n", + " plt.figure(figsize=(10, 10))\n", + " plt.imshow(img_bev)\n", + " plt.axis(\"off\")\n", + " plt.title(\"BEV Simulator State\")\n", + " plt.show()\n", + "\n", + "# Ego feature time series for tracked agent\n", + "ego_features_over_time = []\n", + "for t in range(HORIZON):\n", + " ego, *_ = unpack_obs(\n", + " buf_stoch[\"obs\"][t : t + 1, TRACKED_AGENT : TRACKED_AGENT + 1][0],\n", + " dynamics_model=dyn_model,\n", + " target_type=tgt_type,\n", + " reward_conditioning=rew_cond,\n", + " num_target_waypoints=n_tgt_wp,\n", + " max_stop_signs=MAX_STOP_SIGNS,\n", + " )\n", + " ego_features_over_time.append(ego)\n", + "ego_ts = np.array(ego_features_over_time)\n", + "\n", + "if dyn_model == \"jerk\":\n", + " labels = [\"speed\", \"width\", \"length\", \"steering\", \"a_long\", \"a_lat\", \"lcenter\", \"lalign\", \"speed_limit\"]\n", + " plot_idxs = [0, 3, 4, 5] # speed, steering, a_long, a_lat\n", + "else:\n", + " labels = [\"speed\", \"width\", \"length\", \"lcenter\", \"lalign\", \"speed_limit\"]\n", + " plot_idxs = [0, 3, 4, 5] # speed, lcenter, lalign, speed_limit\n", + "\n", + "fig, axes = plt.subplots(len(plot_idxs), 1, figsize=(14, 3 * len(plot_idxs)), sharex=True)\n", + "for i, idx in enumerate(plot_idxs):\n", + " axes[i].plot(ego_ts[:, idx])\n", + " print(ego_ts[10:, idx].argmin())\n", + " axes[i].set_ylabel(labels[idx])\n", + " axes[i].grid(True, alpha=0.3)\n", + "axes[-1].set_xlabel(\"Step\")\n", + "fig.suptitle(f\"Ego features over time | agent={TRACKED_AGENT}\", fontsize=14)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "czs3aiuhgyo", + "metadata": {}, + "source": [ + "## Observation layer breakdown\n", + "\n", + "Obs layout (all ego-centric, normalized):\n", + "- **Ego**: speed, width, length, [jerk: steering, a_long, a_lat], lane_center_dist, lane_angle, speed_limit\n", + "- **Conditioning** (if enabled): 16 reward coefs (goal_radius, collision, offroad, comfort, lane_align, lane_center, velocity, traffic_light, center_bias, vel_align, overspeed, timestep, reverse, throttle, steer, acc) + target waypoints\n", + "- **Target**: static=rel_x,rel_y per waypoint; dynamic=rel_x,rel_y,heading_cos,heading_sin per waypoint\n", + "- **Partners** (MAX_PARTNERS x 7): rel_x, rel_y, width, length, heading_cos, heading_sin, speed\n", + "- **Lanes** (MAX_LANES x 6): rel_x, rel_y, seg_length, seg_width, dir_cos, dir_sin\n", + "- **Boundaries** (MAX_BOUNDS x 6): same as lanes\n", + "- **Traffic** (MAX_TRAFFIC x 3): rel_x, rel_y, state_normalized" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ytjdjnb471l", + "metadata": {}, + "outputs": [], + "source": [ + "from pufferlib.viz import unpack_obs\n", + "\n", + "sample_t = min(50, HORIZON - 1)\n", + "sample_obs = buf_stoch[\"obs\"][sample_t : sample_t + 1, TRACKED_AGENT : TRACKED_AGENT + 1][0]\n", + "ego, target, partners, lanes, boundaries, traffic, stop_sign = unpack_obs(\n", + " sample_obs,\n", + " dynamics_model=dyn_model,\n", + " target_type=tgt_type,\n", + " reward_conditioning=rew_cond,\n", + " num_target_waypoints=n_tgt_wp,\n", + " max_stop_signs=MAX_STOP_SIGNS,\n", + ")\n", + "\n", + "# Also unpack conditioning manually (unpack_obs doesn't return it separately)\n", + "ego_dim = binding.EGO_FEATURES_JERK if dyn_model == \"jerk\" else binding.EGO_FEATURES_CLASSIC\n", + "cond_dim = binding.NUM_REWARD_COEFS if rew_cond else 0\n", + "cond_obs = sample_obs[0, ego_dim : ego_dim + cond_dim] if cond_dim > 0 else None\n", + "\n", + "\n", + "# --- Print all layer shapes + stats ---\n", + "def layer_stats(name, arr):\n", + " flat = arr.flatten() if hasattr(arr, \"flatten\") else np.array(arr).flatten()\n", + " if flat.size == 0:\n", + " print(f\"{name:>14s}: shape={str(list(arr.shape)):>16s} (empty)\")\n", + " return\n", + " nonzero = np.count_nonzero(flat)\n", + " print(\n", + " f\"{name:>14s}: shape={str(list(arr.shape)):>16s} \"\n", + " f\"nonzero={nonzero:>5d}/{flat.size:<5d} \"\n", + " f\"range=[{flat.min():.4f}, {flat.max():.4f}] \"\n", + " f\"mean={flat.mean():.4f} std={flat.std():.4f}\"\n", + " )\n", + "\n", + "\n", + "print(f\"--- Observation breakdown at t={sample_t}, agent={TRACKED_AGENT} ---\")\n", + "print(f\"Total obs dim: {sample_obs.shape[-1]}\")\n", + "print()\n", + "layer_stats(\"Ego\", ego)\n", + "if cond_obs is not None:\n", + " layer_stats(\"Conditioning\", cond_obs)\n", + "layer_stats(\"Target\", target)\n", + "layer_stats(\"Partners\", partners)\n", + "layer_stats(\"Lanes\", lanes)\n", + "layer_stats(\"Boundaries\", boundaries)\n", + "layer_stats(\"Traffic\", traffic)\n", + "layer_stats(\"StopSign\", stop_sign)\n", + "\n", + "# --- Ego features detail ---\n", + "if dyn_model == \"jerk\":\n", + " ego_labels = [\"speed\", \"width\", \"length\", \"steering\", \"a_long\", \"a_lat\", \"lane_center\", \"lane_align\", \"speed_limit\"]\n", + "else:\n", + " ego_labels = [\"speed\", \"width\", \"length\", \"lane_center\", \"lane_align\", \"speed_limit\"]\n", + "\n", + "print(f\"\\n--- Ego features ---\")\n", + "for i, (label, val) in enumerate(zip(ego_labels, ego)):\n", + " print(f\" [{i}] {label:>14s} = {val:.4f}\")\n", + "\n", + "# --- Conditioning detail ---\n", + "if cond_obs is not None:\n", + " cond_labels = [\n", + " \"goal_radius\",\n", + " \"collision\",\n", + " \"offroad\",\n", + " \"comfort\",\n", + " \"lane_align\",\n", + " \"lane_center\",\n", + " \"velocity\",\n", + " \"traffic_light\",\n", + " \"center_bias\",\n", + " \"vel_align\",\n", + " \"overspeed\",\n", + " \"timestep\",\n", + " \"reverse\",\n", + " \"throttle\",\n", + " \"steer\",\n", + " \"acc\",\n", + " ]\n", + " print(f\"\\n--- Conditioning (reward coefs, normalized) ---\")\n", + " for i, (label, val) in enumerate(zip(cond_labels, cond_obs)):\n", + " print(f\" [{i:>2d}] {label:>16s} = {val:.4f}\")\n", + "\n", + "# --- Target waypoints ---\n", + "tgt_feat = binding.STATIC_TARGET_FEATURES if tgt_type == \"static\" else binding.DYNAMIC_TARGET_FEATURES\n", + "if tgt_type == \"static\":\n", + " tgt_labels = [\"rel_x\", \"rel_y\"]\n", + "else:\n", + " tgt_labels = [\"rel_x\", \"rel_y\", \"heading_cos\", \"heading_sin\"]\n", + "\n", + "print(f\"\\n--- Target waypoints (n={n_tgt_wp}, type={tgt_type}) ---\")\n", + "for wp in range(target.shape[0]):\n", + " vals = \", \".join(f\"{tgt_labels[j]}={target[wp, j]:.4f}\" for j in range(tgt_feat))\n", + " active = \"ACTIVE\" if not np.allclose(target[wp], 0) else \"zeroed\"\n", + " print(f\" wp[{wp}]: {vals} ({active})\")\n", + "\n", + "# --- Partner summary ---\n", + "n_visible = np.sum(np.any(partners != 0, axis=1))\n", + "print(f\"\\n--- Partners: {n_visible}/{partners.shape[0]} visible ---\")\n", + "partner_labels = [\"rel_x\", \"rel_y\", \"width\", \"length\", \"heading_cos\", \"heading_sin\", \"speed\"]\n", + "for p in range(min(int(n_visible), 5)):\n", + " vals = \", \".join(f\"{partner_labels[j]}={partners[p, j]:.3f}\" for j in range(7))\n", + " print(f\" [{p}] {vals}\")\n", + "if n_visible > 5:\n", + " print(f\" ... ({n_visible - 5} more)\")\n", + "\n", + "# --- Lane/boundary occupancy ---\n", + "n_lanes = np.sum(np.any(lanes != 0, axis=1))\n", + "n_bounds = np.sum(np.any(boundaries != 0, axis=1))\n", + "print(f\"\\n--- Road: {n_lanes}/{lanes.shape[0]} lane segs, {n_bounds}/{boundaries.shape[0]} boundary segs ---\")\n", + "\n", + "# --- Traffic ---\n", + "n_traffic = np.sum(np.any(traffic != 0, axis=1))\n", + "print(f\"\\n--- Traffic lights: {n_traffic}/{traffic.shape[0]} visible ---\")\n", + "traffic_labels = [\"rel_x\", \"rel_y\", \"state_normalized\"]\n", + "for t in range(min(int(n_traffic), 5)):\n", + " vals = \", \".join(\n", + " f\"{traffic_labels[j]}={traffic[t, j]:.3f}\" for j in range(min(len(traffic_labels), traffic.shape[1]))\n", + " )\n", + " print(f\" [{t}] {vals}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "65bg95pn7dp", + "metadata": {}, + "outputs": [], + "source": [ + "# --- Layer-level stats across ALL agents at sample_t ---\n", + "all_obs = buf_stoch[\"obs\"][sample_t] # (N, obs_dim)\n", + "\n", + "ego_dim = binding.EGO_FEATURES_JERK if dyn_model == \"jerk\" else binding.EGO_FEATURES_CLASSIC\n", + "cond_dim = binding.NUM_REWARD_COEFS if rew_cond else 0\n", + "tgt_feat = binding.STATIC_TARGET_FEATURES if tgt_type == \"static\" else binding.DYNAMIC_TARGET_FEATURES\n", + "tgt_dim = n_tgt_wp * tgt_feat\n", + "partner_dim = MAX_PARTNERS * PARTNER_F\n", + "lane_dim = MAX_LANES * ROAD_F\n", + "boundary_dim = MAX_BOUNDS * ROAD_F\n", + "traffic_dim = MAX_TRAFFIC * TRAFFIC_F\n", + "\n", + "# Slice indices\n", + "idx = 0\n", + "slices = {}\n", + "slices[\"ego\"] = (idx, idx + ego_dim)\n", + "idx += ego_dim\n", + "if cond_dim > 0:\n", + " slices[\"conditioning\"] = (idx, idx + cond_dim)\n", + " idx += cond_dim\n", + "slices[\"target\"] = (idx, idx + tgt_dim)\n", + "idx += tgt_dim\n", + "slices[\"partners\"] = (idx, idx + partner_dim)\n", + "idx += partner_dim\n", + "slices[\"lanes\"] = (idx, idx + lane_dim)\n", + "idx += lane_dim\n", + "slices[\"boundaries\"] = (idx, idx + boundary_dim)\n", + "idx += boundary_dim\n", + "slices[\"traffic\"] = (idx, idx + traffic_dim)\n", + "idx += traffic_dim\n", + "\n", + "print(f\"Obs dim used: {idx} / {all_obs.shape[1]}\")\n", + "print(\n", + " f\"\\n{'Layer':>14s} | {'start':>5s}-{'end':>5s} | {'dim':>5s} | {'mean':>8s} | {'std':>8s} | {'min':>8s} | {'max':>8s} | {'%nonzero':>8s}\"\n", + ")\n", + "print(\"-\" * 95)\n", + "for name, (s, e) in slices.items():\n", + " chunk = all_obs[:, s:e]\n", + " nz_pct = 100 * np.count_nonzero(chunk) / chunk.size\n", + " print(\n", + " f\"{name:>14s} | {s:>5d}-{e:>5d} | {e - s:>5d} | {chunk.mean():>8.4f} | {chunk.std():>8.4f} | \"\n", + " f\"{chunk.min():>8.4f} | {chunk.max():>8.4f} | {nz_pct:>7.1f}%\"\n", + " )\n", + "\n", + "# --- Plots ---\n", + "n_layers = len(slices)\n", + "fig, axes = plt.subplots(2, (n_layers + 1) // 2, figsize=(5 * ((n_layers + 1) // 2), 8))\n", + "axes = axes.flatten()\n", + "\n", + "for i, (name, (s, e)) in enumerate(slices.items()):\n", + " chunk = all_obs[:, s:e].flatten()\n", + " # Filter out exact zeros for histogram readability on sparse layers\n", + " nonzero_vals = chunk[chunk != 0]\n", + " if len(nonzero_vals) > 0:\n", + " axes[i].hist(nonzero_vals, bins=50, edgecolor=\"black\", alpha=0.7)\n", + " axes[i].set_title(f\"{name} (nonzero only, {len(nonzero_vals)}/{len(chunk)})\")\n", + " else:\n", + " axes[i].hist(chunk, bins=50, edgecolor=\"black\", alpha=0.7)\n", + " axes[i].set_title(f\"{name} (all zeros)\")\n", + " axes[i].set_xlabel(\"Value\")\n", + "\n", + "# Hide unused axes\n", + "for j in range(i + 1, len(axes)):\n", + " axes[j].set_visible(False)\n", + "\n", + "fig.suptitle(f\"Observation distributions across {N} agents at t={sample_t}\", fontsize=14)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dfm7par7pmo", + "metadata": {}, + "outputs": [], + "source": [ + "# --- Per-feature detail for partners, lanes, boundaries over time (tracked agent) ---\n", + "\n", + "\n", + "def unpack_all_timesteps(bufs, agent_idx):\n", + " \"\"\"Unpack all obs layers across time for one agent.\"\"\"\n", + " H = bufs[\"obs\"].shape[0]\n", + " egos, targets, conds = [], [], []\n", + " n_partners, n_lanes, n_bounds, n_traffic, n_stop_signs = [], [], [], [], []\n", + "\n", + " for t in range(H):\n", + " ob = bufs[\"obs\"][t : t + 1, agent_idx : agent_idx + 1][0]\n", + " ego, tgt, part, lane, bnd, tfc, stop_sign = unpack_obs(\n", + " ob,\n", + " dynamics_model=dyn_model,\n", + " target_type=tgt_type,\n", + " reward_conditioning=rew_cond,\n", + " num_target_waypoints=n_tgt_wp,\n", + " max_stop_signs=MAX_STOP_SIGNS,\n", + " )\n", + " egos.append(ego)\n", + " targets.append(tgt)\n", + " n_partners.append(np.sum(np.any(part != 0, axis=1)))\n", + " n_lanes.append(np.sum(np.any(lane != 0, axis=1)))\n", + " n_bounds.append(np.sum(np.any(bnd != 0, axis=1)))\n", + " n_traffic.append(np.sum(np.any(tfc != 0, axis=1)))\n", + " n_stop_signs.append(np.sum(np.any(stop_sign != 0, axis=1)))\n", + "\n", + " if rew_cond:\n", + " ed = binding.EGO_FEATURES_JERK if dyn_model == \"jerk\" else binding.EGO_FEATURES_CLASSIC\n", + " conds.append(ob[0, ed : ed + binding.NUM_REWARD_COEFS])\n", + "\n", + " return {\n", + " \"ego\": np.array(egos),\n", + " \"target\": np.array(targets),\n", + " \"cond\": np.array(conds) if conds else None,\n", + " \"n_partners\": np.array(n_partners),\n", + " \"n_lanes\": np.array(n_lanes),\n", + " \"n_bounds\": np.array(n_bounds),\n", + " \"n_traffic\": np.array(n_traffic),\n", + " \"n_stop_signs\": np.array(n_stop_signs),\n", + " }\n", + "\n", + "\n", + "ts = unpack_all_timesteps(buf_stoch, TRACKED_AGENT)\n", + "\n", + "fig, axes = plt.subplots(2, 2, figsize=(16, 10))\n", + "\n", + "# Occupancy over time\n", + "axes[0, 0].plot(ts[\"n_partners\"], label=\"partners\", alpha=0.8)\n", + "axes[0, 0].plot(ts[\"n_lanes\"], label=\"lanes\", alpha=0.8)\n", + "axes[0, 0].plot(ts[\"n_bounds\"], label=\"boundaries\", alpha=0.8)\n", + "axes[0, 0].plot(ts[\"n_traffic\"], label=\"traffic\", alpha=0.8)\n", + "axes[0, 0].plot(ts[\"n_stop_signs\"], label=\"stop_signs\", alpha=0.8)\n", + "axes[0, 0].set_xlabel(\"Step\")\n", + "axes[0, 0].set_ylabel(\"Visible count\")\n", + "axes[0, 0].set_title(f\"Obs occupancy over time | agent={TRACKED_AGENT}\")\n", + "axes[0, 0].legend()\n", + "axes[0, 0].grid(True, alpha=0.3)\n", + "\n", + "# Target waypoint distances over time\n", + "tgt_x = ts[\"target\"][:, :, 0]\n", + "tgt_y = ts[\"target\"][:, :, 1]\n", + "tgt_dist = np.sqrt(tgt_x**2 + tgt_y**2)\n", + "for wp in range(n_tgt_wp):\n", + " axes[0, 1].plot(tgt_dist[:, wp], label=f\"wp[{wp}]\", alpha=0.8)\n", + "axes[0, 1].set_xlabel(\"Step\")\n", + "axes[0, 1].set_ylabel(\"Distance (normalized)\")\n", + "axes[0, 1].set_title(\"Target waypoint distance over time\")\n", + "axes[0, 1].legend()\n", + "axes[0, 1].grid(True, alpha=0.3)\n", + "\n", + "# Conditioning heatmap over time\n", + "if ts[\"cond\"] is not None:\n", + " cond_labels = [\n", + " \"goal_rad\",\n", + " \"coll\",\n", + " \"offrd\",\n", + " \"comf\",\n", + " \"l_align\",\n", + " \"l_ctr\",\n", + " \"vel\",\n", + " \"traf\",\n", + " \"c_bias\",\n", + " \"v_align\",\n", + " \"ovspd\",\n", + " \"tstep\",\n", + " \"rev\",\n", + " \"throt\",\n", + " \"steer\",\n", + " \"acc\",\n", + " ]\n", + " im = axes[1, 0].imshow(ts[\"cond\"].T, aspect=\"auto\", cmap=\"coolwarm\", interpolation=\"nearest\")\n", + " axes[1, 0].set_yticks(range(len(cond_labels)))\n", + " axes[1, 0].set_yticklabels(cond_labels, fontsize=8)\n", + " axes[1, 0].set_xlabel(\"Step\")\n", + " axes[1, 0].set_title(\"Conditioning coefs over time\")\n", + " plt.colorbar(im, ax=axes[1, 0])\n", + "else:\n", + " axes[1, 0].text(0.5, 0.5, \"No conditioning\", ha=\"center\", va=\"center\", transform=axes[1, 0].transAxes)\n", + " axes[1, 0].set_title(\"Conditioning (disabled)\")\n", + "\n", + "# Partner closest distance over time\n", + "partner_dists = []\n", + "for t in range(HORIZON):\n", + " ob = buf_stoch[\"obs\"][t : t + 1, TRACKED_AGENT : TRACKED_AGENT + 1][0]\n", + " _, _, part, _, _, _, _ = unpack_obs(\n", + " ob,\n", + " dynamics_model=dyn_model,\n", + " target_type=tgt_type,\n", + " reward_conditioning=rew_cond,\n", + " num_target_waypoints=n_tgt_wp,\n", + " max_stop_signs=MAX_STOP_SIGNS,\n", + " )\n", + " dists = np.sqrt(part[:, 0] ** 2 + part[:, 1] ** 2)\n", + " visible = np.any(part != 0, axis=1)\n", + " partner_dists.append(dists[visible].min() if visible.any() else np.nan)\n", + "\n", + "axes[1, 1].plot(partner_dists, alpha=0.8, color=\"red\")\n", + "axes[1, 1].set_xlabel(\"Step\")\n", + "axes[1, 1].set_ylabel(\"Min partner dist (normalized)\")\n", + "axes[1, 1].set_title(\"Closest partner distance over time\")\n", + "axes[1, 1].grid(True, alpha=0.3)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "j1h4h99tnve", + "metadata": {}, + "outputs": [], + "source": [ + "# --- Spatial scatter: all observed entities in ego frame at sample_t ---\n", + "sample_obs = buf_stoch[\"obs\"][sample_t : sample_t + 1, TRACKED_AGENT : TRACKED_AGENT + 1][0]\n", + "ego, target, partners, lanes, boundaries, traffic, stop_sign = unpack_obs(\n", + " sample_obs,\n", + " dynamics_model=dyn_model,\n", + " target_type=tgt_type,\n", + " reward_conditioning=rew_cond,\n", + " num_target_waypoints=n_tgt_wp,\n", + " max_stop_signs=MAX_STOP_SIGNS,\n", + ")\n", + "\n", + "fig, ax = plt.subplots(figsize=(10, 10))\n", + "\n", + "# Ego vehicle at origin\n", + "from matplotlib.patches import Rectangle\n", + "\n", + "ax.add_patch(\n", + " Rectangle((-ego[2] / 2, -ego[1] / 2), ego[2], ego[1], facecolor=\"blue\", edgecolor=\"black\", alpha=0.7, zorder=10)\n", + ")\n", + "ax.annotate(\"EGO\", (0, 0), fontsize=9, ha=\"center\", va=\"center\", color=\"white\", fontweight=\"bold\", zorder=11)\n", + "\n", + "# Lane segments\n", + "for i in range(lanes.shape[0]):\n", + " if np.allclose(lanes[i], 0):\n", + " continue\n", + " rx, ry, length, _, dc, ds = lanes[i]\n", + " ax.plot(\n", + " [rx - dc * length / 2, rx + dc * length / 2],\n", + " [ry - ds * length / 2, ry + ds * length / 2],\n", + " color=\"lightgray\",\n", + " linewidth=1,\n", + " zorder=1,\n", + " )\n", + "ax.scatter(\n", + " lanes[np.any(lanes != 0, axis=1), 0],\n", + " lanes[np.any(lanes != 0, axis=1), 1],\n", + " s=5,\n", + " color=\"gray\",\n", + " alpha=0.5,\n", + " label=f\"lanes ({n_lanes})\",\n", + " zorder=2,\n", + ")\n", + "\n", + "# Boundary segments\n", + "for i in range(boundaries.shape[0]):\n", + " if np.allclose(boundaries[i], 0):\n", + " continue\n", + " rx, ry, length, _, dc, ds = boundaries[i]\n", + " ax.plot(\n", + " [rx - dc * length / 2, rx + dc * length / 2],\n", + " [ry - ds * length / 2, ry + ds * length / 2],\n", + " color=\"black\",\n", + " linewidth=1,\n", + " zorder=1,\n", + " )\n", + "bnd_mask = np.any(boundaries != 0, axis=1)\n", + "if bnd_mask.any():\n", + " ax.scatter(\n", + " boundaries[bnd_mask, 0],\n", + " boundaries[bnd_mask, 1],\n", + " s=8,\n", + " color=\"black\",\n", + " alpha=0.6,\n", + " label=f\"boundaries ({n_bounds})\",\n", + " zorder=2,\n", + " )\n", + "\n", + "# Partners\n", + "for i in range(partners.shape[0]):\n", + " if np.allclose(partners[i], 0):\n", + " continue\n", + " rx, ry, w, l, hc, hs, spd = partners[i]\n", + " heading = np.arctan2(hs, hc)\n", + " rect = Rectangle((-l / 2, -w / 2), l, w, facecolor=\"orange\", edgecolor=\"black\", alpha=0.6, zorder=9)\n", + " rect.set_transform(plt.matplotlib.transforms.Affine2D().rotate(heading).translate(rx, ry) + ax.transData)\n", + " ax.add_patch(rect)\n", + " ax.annotate(f\"{spd:.2f}\", (rx, ry), fontsize=7, ha=\"center\", color=\"darkred\", zorder=12)\n", + "part_mask = np.any(partners != 0, axis=1)\n", + "if part_mask.any():\n", + " ax.scatter(\n", + " partners[part_mask, 0],\n", + " partners[part_mask, 1],\n", + " s=40,\n", + " color=\"orange\",\n", + " edgecolors=\"black\",\n", + " label=f\"partners ({n_visible})\",\n", + " zorder=8,\n", + " )\n", + "\n", + "# Target waypoints\n", + "for wp in range(target.shape[0]):\n", + " if np.allclose(target[wp], 0):\n", + " continue\n", + " marker = \"*\" if wp == 0 else \"o\"\n", + " s = 200 if wp == 0 else 80\n", + " color = \"red\" if wp == 0 else \"salmon\"\n", + " ax.scatter(\n", + " target[wp, 0],\n", + " target[wp, 1],\n", + " color=color,\n", + " marker=marker,\n", + " s=s,\n", + " zorder=15,\n", + " label=f\"target wp[{wp}]\" if wp < 3 else None,\n", + " )\n", + "\n", + "# Traffic lights\n", + "for i in range(traffic.shape[0]):\n", + " if np.allclose(traffic[i], 0):\n", + " continue\n", + " state_colors = {0: \"red\", 1: \"yellow\", 2: \"green\"}\n", + " c = state_colors.get(int(round(traffic[i, 2] * 2)), \"gray\")\n", + " ax.scatter(\n", + " traffic[i, 0], traffic[i, 1], color=c, s=100, marker=\"s\", edgecolors=\"black\", zorder=15, label=f\"traffic ({c})\"\n", + " )\n", + "\n", + "ax.set_xlim(-1, 1)\n", + "ax.set_ylim(-1, 1)\n", + "ax.set_aspect(\"equal\")\n", + "ax.set_xlabel(\"X (ego frame, normalized)\")\n", + "ax.set_ylabel(\"Y (ego frame, normalized)\")\n", + "ax.set_title(f\"All observed entities | agent={TRACKED_AGENT}, t={sample_t}\")\n", + "ax.legend(loc=\"upper right\", fontsize=8)\n", + "ax.grid(True, alpha=0.3)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "gsyrbg02vc", + "metadata": {}, + "source": [ + "### Ego + conditioning distributions across all agents" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "wwisx2muaj", + "metadata": {}, + "outputs": [], + "source": [ + "# Ego feature distributions across all agents, pooled over full rollout\n", + "ego_dim = binding.EGO_FEATURES_JERK if dyn_model == \"jerk\" else binding.EGO_FEATURES_CLASSIC\n", + "all_ego = buf_stoch[\"obs\"][:, :, :ego_dim].reshape(-1, ego_dim) # (H*N, ego_dim)\n", + "\n", + "if dyn_model == \"jerk\":\n", + " ego_labels = [\"speed\", \"width\", \"length\", \"steering\", \"a_long\", \"a_lat\", \"lane_center\", \"lane_align\", \"speed_limit\"]\n", + "else:\n", + " ego_labels = [\"speed\", \"width\", \"length\", \"lane_center\", \"lane_align\", \"speed_limit\"]\n", + "\n", + "fig, axes = plt.subplots(2, len(ego_labels), figsize=(3.5 * len(ego_labels), 7))\n", + "\n", + "# Row 0: histograms\n", + "for i, label in enumerate(ego_labels):\n", + " vals = all_ego[:, i]\n", + " print(f\"{label}: mean={vals}\")\n", + " axes[0, i].hist(vals, bins=60, edgecolor=\"black\", alpha=0.7, color=\"steelblue\")\n", + " axes[0, i].set_title(label, fontsize=10)\n", + " axes[0, i].set_xlabel(\"\")\n", + " axes[0, i].tick_params(labelsize=7)\n", + " axes[0, i].axvline(vals.mean(), color=\"red\", ls=\"--\", lw=1)\n", + "\n", + "# Row 1: boxplots per-agent (distribution across timesteps for each agent)\n", + "ego_per_agent = buf_stoch[\"obs\"][:, :, :ego_dim] # (H, N, ego_dim)\n", + "for i, label in enumerate(ego_labels):\n", + " data = [ego_per_agent[:, a, i] for a in range(N)]\n", + " bp = axes[1, i].boxplot(\n", + " data,\n", + " showfliers=False,\n", + " patch_artist=True,\n", + " boxprops=dict(facecolor=\"steelblue\", alpha=0.5),\n", + " medianprops=dict(color=\"red\"),\n", + " )\n", + " axes[1, i].set_xlabel(\"Agent\")\n", + " axes[1, i].tick_params(labelsize=7)\n", + " axes[1, i].set_title(f\"{label} per agent\", fontsize=9)\n", + "\n", + "fig.suptitle(\"Ego features: full rollout distributions\", fontsize=13)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Conditioning distributions across all agents (if enabled)\n", + "if rew_cond:\n", + " cond_start = ego_dim\n", + " cond_end = cond_start + binding.NUM_REWARD_COEFS\n", + " all_cond = buf_stoch[\"obs\"][:, :, cond_start:cond_end].reshape(-1, binding.NUM_REWARD_COEFS)\n", + "\n", + " cond_labels = [\n", + " \"goal_rad\",\n", + " \"coll\",\n", + " \"offrd\",\n", + " \"comf\",\n", + " \"l_align\",\n", + " \"l_ctr\",\n", + " \"vel\",\n", + " \"traf\",\n", + " \"c_bias\",\n", + " \"v_align\",\n", + " \"ovspd\",\n", + " \"tstep\",\n", + " \"rev\",\n", + " \"throt\",\n", + " \"steer\",\n", + " \"acc\",\n", + " ]\n", + "\n", + " fig, ax = plt.subplots(figsize=(14, 5))\n", + " parts = ax.violinplot(\n", + " [all_cond[:, i] for i in range(binding.NUM_REWARD_COEFS)],\n", + " positions=range(binding.NUM_REWARD_COEFS),\n", + " showmeans=True,\n", + " showmedians=True,\n", + " )\n", + " ax.set_xticks(range(binding.NUM_REWARD_COEFS))\n", + " ax.set_xticklabels(cond_labels, rotation=45, ha=\"right\", fontsize=9)\n", + " ax.set_ylabel(\"Normalized value\")\n", + " ax.set_title(\"Conditioning coef distributions (all agents, full rollout)\")\n", + " ax.grid(True, alpha=0.3, axis=\"y\")\n", + " plt.tight_layout()\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "fu6dbejpxdu", + "metadata": {}, + "source": [ + "### Partner per-feature distributions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6g55putjnwo", + "metadata": {}, + "outputs": [], + "source": [ + "# Partner per-feature distributions (pooled over all agents + timesteps, visible only)\n", + "partner_labels = [\"rel_x\", \"rel_y\", \"width\", \"length\", \"heading_cos\", \"heading_sin\", \"speed\"]\n", + "max_partners = MAX_PARTNERS\n", + "pf = PARTNER_F\n", + "\n", + "# Compute slices\n", + "_ego_d = binding.EGO_FEATURES_JERK if dyn_model == \"jerk\" else binding.EGO_FEATURES_CLASSIC\n", + "_cond_d = binding.NUM_REWARD_COEFS if rew_cond else 0\n", + "_tgt_f = binding.STATIC_TARGET_FEATURES if tgt_type == \"static\" else binding.DYNAMIC_TARGET_FEATURES\n", + "_tgt_d = n_tgt_wp * _tgt_f\n", + "_p_start = _ego_d + _cond_d + _tgt_d\n", + "_p_end = _p_start + max_partners * pf\n", + "\n", + "all_partners = buf_stoch[\"obs\"][:, :, _p_start:_p_end].reshape(-1, max_partners, pf) # (H*N, 16, 7)\n", + "# Mask: partner is visible if any feature != 0\n", + "visible_mask = np.any(all_partners != 0, axis=2) # (H*N, 16)\n", + "visible_partners = all_partners[visible_mask] # (K, 7) — all visible partner observations\n", + "\n", + "print(\n", + " f\"Total partner obs: {all_partners.shape[0] * max_partners}, visible: {len(visible_partners)} \"\n", + " f\"({100 * len(visible_partners) / (all_partners.shape[0] * max_partners):.1f}%)\"\n", + ")\n", + "\n", + "fig, axes = plt.subplots(2, 4, figsize=(20, 8))\n", + "axes = axes.flatten()\n", + "\n", + "for i, label in enumerate(partner_labels):\n", + " vals = visible_partners[:, i]\n", + " axes[i].hist(vals, bins=80, edgecolor=\"black\", alpha=0.7, color=\"darkorange\")\n", + " axes[i].set_title(f\"{label} (n={len(vals)})\", fontsize=10)\n", + " axes[i].axvline(vals.mean(), color=\"red\", ls=\"--\", lw=1, label=f\"mean={vals.mean():.3f}\")\n", + " axes[i].legend(fontsize=7)\n", + " axes[i].tick_params(labelsize=7)\n", + "\n", + "# rel_x vs rel_y scatter in last panel\n", + "axes[7].scatter(visible_partners[:, 0], visible_partners[:, 1], s=1, alpha=0.15, color=\"darkorange\")\n", + "axes[7].set_xlabel(\"rel_x\")\n", + "axes[7].set_ylabel(\"rel_y\")\n", + "axes[7].set_title(\"Partner positions (ego frame)\")\n", + "axes[7].set_aspect(\"equal\")\n", + "axes[7].grid(True, alpha=0.3)\n", + "\n", + "fig.suptitle(\"Partner features: all visible, full rollout\", fontsize=13)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Partner count distribution across (timestep, agent)\n", + "partner_counts = visible_mask.sum(axis=1) # (H*N,)\n", + "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n", + "axes[0].hist(partner_counts, bins=range(max_partners + 2), edgecolor=\"black\", alpha=0.7, color=\"darkorange\")\n", + "axes[0].set_xlabel(\"Visible partners\")\n", + "axes[0].set_ylabel(\"Count\")\n", + "axes[0].set_title(\"Partner count distribution (per agent per step)\")\n", + "\n", + "# Partner distance distribution\n", + "dists = np.sqrt(visible_partners[:, 0] ** 2 + visible_partners[:, 1] ** 2)\n", + "axes[1].hist(dists, bins=80, edgecolor=\"black\", alpha=0.7, color=\"coral\")\n", + "axes[1].set_xlabel(\"Distance (normalized)\")\n", + "axes[1].set_ylabel(\"Count\")\n", + "axes[1].set_title(f\"Partner distance distribution (mean={dists.mean():.3f})\")\n", + "axes[1].axvline(dists.mean(), color=\"red\", ls=\"--\", lw=1)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "5n8a8dwmna3", + "metadata": {}, + "source": [ + "### Road (lanes + boundaries) and target distributions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4xgikpryeg", + "metadata": {}, + "outputs": [], + "source": [ + "# Road per-feature distributions (lanes + boundaries)\n", + "road_labels = [\"rel_x\", \"rel_y\", \"seg_length\", \"seg_width\", \"dir_cos\", \"dir_sin\"]\n", + "rf = ROAD_F\n", + "max_lanes = MAX_LANES\n", + "max_bounds = MAX_BOUNDS\n", + "\n", + "_l_start = _p_end\n", + "_l_end = _l_start + max_lanes * rf\n", + "_b_start = _l_end\n", + "_b_end = _b_start + max_bounds * rf\n", + "\n", + "all_lanes = buf_stoch[\"obs\"][:, :, _l_start:_l_end].reshape(-1, max_lanes, rf)\n", + "all_bounds = buf_stoch[\"obs\"][:, :, _b_start:_b_end].reshape(-1, max_bounds, rf)\n", + "\n", + "vis_lanes = all_lanes[np.any(all_lanes != 0, axis=2)]\n", + "vis_bounds = all_bounds[np.any(all_bounds != 0, axis=2)]\n", + "\n", + "print(\n", + " f\"Lanes: {len(vis_lanes)} visible / {all_lanes.shape[0] * max_lanes} total \"\n", + " f\"({100 * len(vis_lanes) / (all_lanes.shape[0] * max_lanes):.1f}%)\"\n", + ")\n", + "print(\n", + " f\"Boundaries: {len(vis_bounds)} visible / {all_bounds.shape[0] * max_bounds} total \"\n", + " f\"({100 * len(vis_bounds) / (all_bounds.shape[0] * max_bounds):.1f}%)\"\n", + ")\n", + "\n", + "fig, axes = plt.subplots(2, 6, figsize=(24, 8))\n", + "for i, label in enumerate(road_labels):\n", + " # Lanes\n", + " axes[0, i].hist(vis_lanes[:, i], bins=80, edgecolor=\"black\", alpha=0.7, color=\"silver\")\n", + " axes[0, i].set_title(f\"lane {label}\", fontsize=9)\n", + " axes[0, i].axvline(vis_lanes[:, i].mean(), color=\"red\", ls=\"--\", lw=1)\n", + " axes[0, i].tick_params(labelsize=7)\n", + " # Boundaries\n", + " axes[1, i].hist(vis_bounds[:, i], bins=80, edgecolor=\"black\", alpha=0.7, color=\"dimgray\")\n", + " axes[1, i].set_title(f\"boundary {label}\", fontsize=9)\n", + " axes[1, i].axvline(vis_bounds[:, i].mean(), color=\"red\", ls=\"--\", lw=1)\n", + " axes[1, i].tick_params(labelsize=7)\n", + "\n", + "fig.suptitle(\"Road features: all visible, full rollout (top=lanes, bottom=boundaries)\", fontsize=13)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Spatial scatter: lane vs boundary positions (pooled)\n", + "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n", + "axes[0].scatter(vis_lanes[:, 0], vis_lanes[:, 1], s=0.5, alpha=0.05, color=\"gray\")\n", + "axes[0].set_xlabel(\"rel_x\")\n", + "axes[0].set_ylabel(\"rel_y\")\n", + "axes[0].set_title(f\"Lane segment positions (n={len(vis_lanes)})\")\n", + "axes[0].set_aspect(\"equal\")\n", + "axes[0].grid(True, alpha=0.3)\n", + "\n", + "axes[1].scatter(vis_bounds[:, 0], vis_bounds[:, 1], s=0.5, alpha=0.05, color=\"black\")\n", + "axes[1].set_xlabel(\"rel_x\")\n", + "axes[1].set_ylabel(\"rel_y\")\n", + "axes[1].set_title(f\"Boundary segment positions (n={len(vis_bounds)})\")\n", + "axes[1].set_aspect(\"equal\")\n", + "axes[1].grid(True, alpha=0.3)\n", + "\n", + "# Lane + boundary segment length comparison\n", + "axes[2].hist(vis_lanes[:, 2], bins=80, alpha=0.6, color=\"silver\", edgecolor=\"black\", label=\"lanes\")\n", + "axes[2].hist(vis_bounds[:, 2], bins=80, alpha=0.6, color=\"dimgray\", edgecolor=\"black\", label=\"boundaries\")\n", + "axes[2].set_xlabel(\"Segment length (normalized)\")\n", + "axes[2].set_ylabel(\"Count\")\n", + "axes[2].set_title(\"Segment length distribution\")\n", + "axes[2].legend()\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Target distributions across all agents, full rollout\n", + "_tgt_start = _ego_d + _cond_d\n", + "_tgt_end = _tgt_start + _tgt_d\n", + "all_target = buf_stoch[\"obs\"][:, :, _tgt_start:_tgt_end].reshape(-1, n_tgt_wp, _tgt_f)\n", + "\n", + "if tgt_type == \"static\":\n", + " tgt_flabels = [\"rel_x\", \"rel_y\"]\n", + "else:\n", + " tgt_flabels = [\"rel_x\", \"rel_y\", \"heading_cos\", \"heading_sin\"]\n", + "\n", + "fig, axes = plt.subplots(1, n_tgt_wp + 1, figsize=(5 * (n_tgt_wp + 1), 4))\n", + "\n", + "for wp in range(n_tgt_wp):\n", + " wp_data = all_target[:, wp, :]\n", + " active = np.any(wp_data != 0, axis=1)\n", + " wp_active = wp_data[active]\n", + " dist = np.sqrt(wp_active[:, 0] ** 2 + wp_active[:, 1] ** 2) if len(wp_active) > 0 else np.array([])\n", + " axes[wp].hist(dist, bins=60, edgecolor=\"black\", alpha=0.7, color=[\"red\", \"salmon\", \"lightsalmon\"][wp % 3])\n", + " axes[wp].set_title(f\"wp[{wp}] distance (n={len(wp_active)}/{len(wp_data)})\", fontsize=10)\n", + " axes[wp].set_xlabel(\"Distance (normalized)\")\n", + "\n", + "# All waypoints x-y scatter\n", + "for wp in range(n_tgt_wp):\n", + " wp_data = all_target[:, wp, :]\n", + " active = np.any(wp_data != 0, axis=1)\n", + " wp_active = wp_data[active]\n", + " if len(wp_active) > 0:\n", + " axes[n_tgt_wp].scatter(wp_active[:, 0], wp_active[:, 1], s=1, alpha=0.1, label=f\"wp[{wp}]\")\n", + "axes[n_tgt_wp].set_xlabel(\"rel_x\")\n", + "axes[n_tgt_wp].set_ylabel(\"rel_y\")\n", + "axes[n_tgt_wp].set_title(\"Target positions (ego frame)\")\n", + "axes[n_tgt_wp].set_aspect(\"equal\")\n", + "axes[n_tgt_wp].legend(fontsize=8)\n", + "axes[n_tgt_wp].grid(True, alpha=0.3)\n", + "\n", + "fig.suptitle(\"Target waypoint distributions (all agents, full rollout)\", fontsize=13)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "csj7ji1j6ed", + "metadata": {}, + "source": [ + "### Observation sparsity and layer occupancy heatmaps" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3xbi2pbsfyv", + "metadata": {}, + "outputs": [], + "source": [ + "# Sparsity heatmap: fraction of nonzero per layer, per agent, over time\n", + "layer_names = [\"partners\", \"lanes\", \"boundaries\"]\n", + "layer_slices = [\n", + " (_p_start, _p_end, MAX_PARTNERS, PARTNER_F),\n", + " (_l_start, _l_end, MAX_LANES, ROAD_F),\n", + " (_b_start, _b_end, MAX_BOUNDS, ROAD_F),\n", + "]\n", + "\n", + "fig, axes = plt.subplots(1, 3, figsize=(20, 5))\n", + "for ax, name, (s, e, n_obj, n_feat) in zip(axes, layer_names, layer_slices):\n", + " # (H, N) -> fraction of visible objects per (timestep, agent)\n", + " raw = buf_stoch[\"obs\"][:, :, s:e].reshape(HORIZON, N, n_obj, n_feat)\n", + " occupancy = np.any(raw != 0, axis=3).sum(axis=2) / n_obj # (H, N)\n", + " im = ax.imshow(occupancy.T, aspect=\"auto\", cmap=\"YlOrRd\", interpolation=\"nearest\", vmin=0, vmax=1)\n", + " ax.set_xlabel(\"Step\")\n", + " ax.set_ylabel(\"Agent\")\n", + " ax.set_title(f\"{name} occupancy (frac visible)\")\n", + " plt.colorbar(im, ax=ax)\n", + "\n", + "plt.suptitle(\"Per-layer occupancy heatmaps (fraction of max slots filled)\", fontsize=13)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Per-layer mean occupancy over time\n", + "fig, axes = plt.subplots(1, 2, figsize=(16, 4))\n", + "\n", + "# Mean across agents\n", + "for name, (s, e, n_obj, n_feat) in zip(layer_names, layer_slices):\n", + " raw = buf_stoch[\"obs\"][:, :, s:e].reshape(HORIZON, N, n_obj, n_feat)\n", + " occ_mean = np.any(raw != 0, axis=3).sum(axis=2).mean(axis=1) # (H,)\n", + " axes[0].plot(occ_mean, label=name, alpha=0.8)\n", + "axes[0].set_xlabel(\"Step\")\n", + "axes[0].set_ylabel(\"Mean visible count\")\n", + "axes[0].set_title(\"Mean occupancy over time (across agents)\")\n", + "axes[0].legend()\n", + "axes[0].grid(True, alpha=0.3)\n", + "\n", + "# Mean across timesteps (per agent)\n", + "for name, (s, e, n_obj, n_feat) in zip(layer_names, layer_slices):\n", + " raw = buf_stoch[\"obs\"][:, :, s:e].reshape(HORIZON, N, n_obj, n_feat)\n", + " occ_per_agent = np.any(raw != 0, axis=3).sum(axis=2).mean(axis=0) # (N,)\n", + " axes[1].bar(range(N), occ_per_agent, alpha=0.5, label=name)\n", + "axes[1].set_xlabel(\"Agent\")\n", + "axes[1].set_ylabel(\"Mean visible count\")\n", + "axes[1].set_title(\"Mean occupancy per agent (across timesteps)\")\n", + "axes[1].legend()\n", + "axes[1].grid(True, alpha=0.3)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Full obs sparsity: fraction of zero features per obs dimension, pooled\n", + "all_flat = buf_stoch[\"obs\"].reshape(-1, obs_dim) # (H*N, obs_dim)\n", + "zero_frac = (all_flat == 0).mean(axis=0) # (obs_dim,)\n", + "fig, ax = plt.subplots(figsize=(18, 3))\n", + "ax.bar(range(obs_dim), zero_frac, width=1.0, color=\"steelblue\", alpha=0.7)\n", + "# Annotate layer boundaries\n", + "prev_e = 0\n", + "for name, (s, e) in slices.items():\n", + " ax.axvline(s, color=\"red\", ls=\"--\", lw=0.5, alpha=0.7)\n", + " mid = (s + e) / 2\n", + " ax.text(mid, 1.02, name, ha=\"center\", va=\"bottom\", fontsize=7, rotation=0, color=\"red\")\n", + " prev_e = e\n", + "ax.set_xlim(0, obs_dim)\n", + "ax.set_ylim(0, 1.1)\n", + "ax.set_xlabel(\"Obs dimension index\")\n", + "ax.set_ylabel(\"Fraction zero\")\n", + "ax.set_title(\"Per-dimension sparsity (fraction zero across full rollout)\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "cell-8", + "metadata": {}, + "source": [ + "## Policy outputs over time" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-9", + "metadata": {}, + "outputs": [], + "source": [ + "# Compute action probs over time for tracked agent (stochastic rollout)\n", + "n_actions = env.single_action_space.nvec[0] if not is_continuous else 1\n", + "action_probs_time = np.zeros((HORIZON, n_actions))\n", + "for t in range(HORIZON):\n", + " obs_t = torch.FloatTensor(buf_stoch[\"obs\"][t : t + 1, TRACKED_AGENT : TRACKED_AGENT + 1][0]).to(device)\n", + " with torch.no_grad():\n", + " if use_rnn:\n", + " # Can't replay RNN states here easily, use feedforward approximation\n", + " inner = policy.policy if hasattr(policy, \"policy\") else policy\n", + " h = inner.encode_observations(obs_t)\n", + " logits_list, _ = inner.decode_actions(h)\n", + " else:\n", + " logits_list, _ = policy(obs_t)\n", + " logits = logits_list[0] if isinstance(logits_list, (list, tuple)) else logits_list\n", + " action_probs_time[t] = F.softmax(logits, dim=-1).cpu().numpy().flatten()\n", + "\n", + "fig, axes = plt.subplots(2, 2, figsize=(16, 10))\n", + "\n", + "# Action distribution heatmap\n", + "im = axes[0, 0].imshow(action_probs_time.T, aspect=\"auto\", cmap=\"viridis\", interpolation=\"nearest\")\n", + "axes[0, 0].set_xlabel(\"Step\")\n", + "axes[0, 0].set_ylabel(\"Action ID\")\n", + "axes[0, 0].set_title(f\"Action prob heatmap | agent={TRACKED_AGENT}\")\n", + "plt.colorbar(im, ax=axes[0, 0])\n", + "\n", + "# Entropy over time\n", + "axes[0, 1].plot(buf_stoch[\"entropy\"][:, TRACKED_AGENT], label=\"stochastic\", alpha=0.8)\n", + "axes[0, 1].set_xlabel(\"Step\")\n", + "axes[0, 1].set_ylabel(\"Entropy\")\n", + "axes[0, 1].set_title(\"Entropy over time\")\n", + "axes[0, 1].grid(True, alpha=0.3)\n", + "\n", + "# Value over time\n", + "axes[1, 0].plot(buf_stoch[\"values\"][:, TRACKED_AGENT], label=\"stochastic\", alpha=0.8)\n", + "axes[1, 0].plot(buf_det[\"values\"][:, TRACKED_AGENT], label=\"deterministic\", alpha=0.8)\n", + "axes[1, 0].set_xlabel(\"Step\")\n", + "axes[1, 0].set_ylabel(\"Value\")\n", + "axes[1, 0].set_title(\"Value predictions over time\")\n", + "axes[1, 0].legend()\n", + "axes[1, 0].grid(True, alpha=0.3)\n", + "\n", + "# Actions over time: deterministic vs stochastic\n", + "axes[1, 1].step(range(HORIZON), buf_stoch[\"actions\"][:, TRACKED_AGENT], label=\"stochastic\", alpha=0.7)\n", + "axes[1, 1].step(range(HORIZON), buf_det[\"actions\"][:, TRACKED_AGENT], label=\"deterministic\", alpha=0.7)\n", + "axes[1, 1].set_xlabel(\"Step\")\n", + "axes[1, 1].set_ylabel(\"Action\")\n", + "axes[1, 1].set_title(\"Selected action over time\")\n", + "axes[1, 1].legend()\n", + "axes[1, 1].grid(True, alpha=0.3)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "cell-10", + "metadata": {}, + "source": [ + "## Rewards and returns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-11", + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(2, 3, figsize=(18, 10))\n", + "\n", + "# Per-step mean reward\n", + "axes[0, 0].plot(buf_stoch[\"rewards\"].mean(axis=1), label=\"stochastic\", alpha=0.8)\n", + "axes[0, 0].plot(buf_det[\"rewards\"].mean(axis=1), label=\"deterministic\", alpha=0.8)\n", + "axes[0, 0].set_xlabel(\"Step\")\n", + "axes[0, 0].set_ylabel(\"Mean reward\")\n", + "axes[0, 0].set_title(\"Mean reward per step\")\n", + "axes[0, 0].legend()\n", + "axes[0, 0].grid(True, alpha=0.3)\n", + "\n", + "# Reward heatmap (stochastic)\n", + "im = axes[0, 1].imshow(buf_stoch[\"rewards\"].T, aspect=\"auto\", cmap=\"RdYlGn\", interpolation=\"nearest\")\n", + "axes[0, 1].set_xlabel(\"Step\")\n", + "axes[0, 1].set_ylabel(\"Agent\")\n", + "axes[0, 1].set_title(\"Reward heatmap (stochastic)\")\n", + "plt.colorbar(im, ax=axes[0, 1])\n", + "\n", + "# Cumulative return per agent\n", + "cum_ret_stoch = buf_stoch[\"rewards\"].sum(axis=0)\n", + "cum_ret_det = buf_det[\"rewards\"].sum(axis=0)\n", + "axes[0, 2].hist(cum_ret_stoch, bins=30, alpha=0.6, label=\"stochastic\", edgecolor=\"black\")\n", + "axes[0, 2].hist(cum_ret_det, bins=30, alpha=0.6, label=\"deterministic\", edgecolor=\"black\")\n", + "axes[0, 2].set_xlabel(\"Cumulative return\")\n", + "axes[0, 2].set_ylabel(\"Count\")\n", + "axes[0, 2].set_title(\"Return distribution across agents\")\n", + "axes[0, 2].legend()\n", + "\n", + "# Reward distribution histogram\n", + "axes[1, 0].hist(buf_stoch[\"rewards\"].flatten(), bins=50, alpha=0.7, edgecolor=\"black\")\n", + "axes[1, 0].set_xlabel(\"Reward\")\n", + "axes[1, 0].set_ylabel(\"Count\")\n", + "axes[1, 0].set_title(\"Per-step reward distribution (stochastic)\")\n", + "axes[1, 0].set_yscale(\"log\")\n", + "\n", + "# Terminal/truncation timeline\n", + "axes[1, 1].plot(buf_stoch[\"terminals\"].sum(axis=1), label=\"terminals\", alpha=0.8)\n", + "axes[1, 1].plot(buf_stoch[\"truncations\"].sum(axis=1), label=\"truncations\", alpha=0.8)\n", + "axes[1, 1].set_xlabel(\"Step\")\n", + "axes[1, 1].set_ylabel(\"Count\")\n", + "axes[1, 1].set_title(\"Terminals/Truncations per step (stochastic)\")\n", + "axes[1, 1].legend()\n", + "axes[1, 1].grid(True, alpha=0.3)\n", + "\n", + "# Tracked agent reward\n", + "axes[1, 2].plot(buf_stoch[\"rewards\"][:, TRACKED_AGENT], label=\"stochastic\", alpha=0.8)\n", + "axes[1, 2].plot(buf_det[\"rewards\"][:, TRACKED_AGENT], label=\"deterministic\", alpha=0.8)\n", + "axes[1, 2].set_xlabel(\"Step\")\n", + "axes[1, 2].set_ylabel(\"Reward\")\n", + "axes[1, 2].set_title(f\"Reward over time | agent={TRACKED_AGENT}\")\n", + "axes[1, 2].legend()\n", + "axes[1, 2].grid(True, alpha=0.3)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "cell-12", + "metadata": {}, + "source": [ + "## Episode metrics" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-13", + "metadata": {}, + "outputs": [], + "source": [ + "# Collect episode-level metrics from the C logging\n", + "log_stoch = binding.vec_log(env.c_envs, N)\n", + "\n", + "# eval_mode=1 returns a list of per-env dicts; aggregate by averaging\n", + "if isinstance(log_stoch, list) and log_stoch:\n", + " all_keys = set(k for d in log_stoch for k in d if isinstance(d[k], (int, float)))\n", + " log_stoch = {k: np.mean([d[k] for d in log_stoch if k in d]) for k in all_keys}\n", + "\n", + "if log_stoch:\n", + " print(\"Episode metrics (after stochastic rollout):\")\n", + " for k, v in sorted(log_stoch.items()):\n", + " if isinstance(v, (int, float)):\n", + " print(f\" {k}: {v:.4f}\")\n", + "\n", + " # Bar chart of key metrics\n", + " keys = [\"score\", \"collision_rate\", \"offroad_rate\", \"completion_rate\", \"dnf_rate\"]\n", + " vals = [log_stoch.get(k, 0) for k in keys]\n", + " fig, ax = plt.subplots(figsize=(10, 4))\n", + " bars = ax.bar(keys, vals, edgecolor=\"black\", alpha=0.7, color=[\"green\", \"red\", \"orange\", \"blue\", \"gray\"])\n", + " ax.set_ylabel(\"Rate\")\n", + " ax.set_title(\"Episode Metrics\")\n", + " for bar, v in zip(bars, vals):\n", + " ax.text(bar.get_x() + bar.get_width() / 2, bar.get_height() + 0.01, f\"{v:.3f}\", ha=\"center\", fontsize=10)\n", + " plt.tight_layout()\n", + " plt.show()\n", + "else:\n", + " print(\"No episode metrics available yet (not enough episodes completed)\")" + ] + }, + { + "cell_type": "markdown", + "id": "cell-14", + "metadata": {}, + "source": [ + "## Value predictions vs actual returns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-15", + "metadata": {}, + "outputs": [], + "source": [ + "gamma = config[\"train\"].get(\"gamma\", 0.98)\n", + "lam = config[\"train\"].get(\"gae_lambda\", 0.95)\n", + "\n", + "\n", + "def compute_gae(rewards, values, terminals, truncations, gamma, lam):\n", + " H, N = rewards.shape\n", + " advantages = np.zeros_like(rewards)\n", + " last_gae = np.zeros(N)\n", + " for t in reversed(range(H - 1)):\n", + " done = np.maximum(terminals[t + 1], truncations[t + 1])\n", + " next_non_terminal = 1.0 - done\n", + " delta = rewards[t + 1] + gamma * values[t + 1] * next_non_terminal - values[t]\n", + " last_gae = delta + gamma * lam * last_gae * next_non_terminal\n", + " advantages[t] = last_gae\n", + " return advantages\n", + "\n", + "\n", + "adv_stoch = compute_gae(\n", + " buf_stoch[\"rewards\"], buf_stoch[\"values\"], buf_stoch[\"terminals\"], buf_stoch[\"truncations\"], gamma, lam\n", + ")\n", + "returns_stoch = adv_stoch + buf_stoch[\"values\"]\n", + "\n", + "pred_v = buf_stoch[\"values\"].flatten()\n", + "actual_r = returns_stoch.flatten()\n", + "\n", + "var_actual = np.var(actual_r)\n", + "explained_var = 1 - np.var(actual_r - pred_v) / (var_actual + 1e-8) if var_actual > 1e-8 else 0.0\n", + "\n", + "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n", + "\n", + "# Scatter: predicted vs actual\n", + "axes[0].scatter(actual_r, pred_v, alpha=0.2, s=5)\n", + "lims = [min(actual_r.min(), pred_v.min()), max(actual_r.max(), pred_v.max())]\n", + "axes[0].plot(lims, lims, \"r--\", label=\"perfect\")\n", + "axes[0].set_xlabel(\"Actual return\")\n", + "axes[0].set_ylabel(\"Predicted value\")\n", + "axes[0].set_title(f\"Value accuracy (EV: {explained_var:.4f})\")\n", + "axes[0].legend()\n", + "axes[0].grid(True, alpha=0.3)\n", + "\n", + "# Value error over time\n", + "value_error = np.abs(returns_stoch - buf_stoch[\"values\"]).mean(axis=1)\n", + "axes[1].plot(value_error)\n", + "axes[1].set_xlabel(\"Step\")\n", + "axes[1].set_ylabel(\"Mean |error|\")\n", + "axes[1].set_title(\"Value prediction error over time\")\n", + "axes[1].grid(True, alpha=0.3)\n", + "\n", + "# Advantage distribution\n", + "axes[2].hist(adv_stoch.flatten(), bins=50, edgecolor=\"black\", alpha=0.7)\n", + "axes[2].set_xlabel(\"Advantage\")\n", + "axes[2].set_ylabel(\"Count\")\n", + "axes[2].set_title(f\"Advantage distribution (std={adv_stoch.std():.4f})\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "print(f\"Explained variance: {explained_var:.4f}\")\n", + "print(f\"Value MSE: {np.mean((actual_r - pred_v) ** 2):.6f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "cell-16", + "metadata": {}, + "source": [ + "## Agent trajectories" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-17", + "metadata": {}, + "outputs": [], + "source": [ + "N_TRAJ = min(16, N) # number of agents to plot\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(16, 7))\n", + "\n", + "for i in range(N_TRAJ):\n", + " color = plt.cm.tab20(i % 20)\n", + " # Stochastic\n", + " axes[0].plot(buf_stoch[\"positions_x\"][:, i], buf_stoch[\"positions_y\"][:, i], alpha=0.6, color=color, linewidth=1)\n", + " axes[0].scatter(\n", + " buf_stoch[\"positions_x\"][0, i], buf_stoch[\"positions_y\"][0, i], color=color, s=30, marker=\"o\", zorder=5\n", + " ) # start\n", + " # Deterministic\n", + " axes[1].plot(buf_det[\"positions_x\"][:, i], buf_det[\"positions_y\"][:, i], alpha=0.6, color=color, linewidth=1)\n", + " axes[1].scatter(buf_det[\"positions_x\"][0, i], buf_det[\"positions_y\"][0, i], color=color, s=30, marker=\"o\", zorder=5)\n", + "\n", + "axes[0].set_title(f\"Stochastic trajectories (N={N_TRAJ})\")\n", + "axes[1].set_title(f\"Deterministic trajectories (N={N_TRAJ})\")\n", + "for ax in axes:\n", + " ax.set_xlabel(\"X\")\n", + " ax.set_ylabel(\"Y\")\n", + " ax.set_aspect(\"equal\")\n", + " ax.grid(True, alpha=0.3)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# ADE vs ground truth if scenario_length is set\n", + "if config[\"env\"].get(\"scenario_length\"):\n", + " try:\n", + " gt = env.get_ground_truth_trajectories()\n", + " # gt['x'] shape: (N, 1, T), positions shape: (T, N)\n", + " gt_x = gt[\"x\"][:, 0, :].T # (T, N)\n", + " gt_y = gt[\"y\"][:, 0, :].T\n", + " gt_valid = gt[\"valid\"][:, 0, :].T\n", + " T_gt = gt_x.shape[0]\n", + " T_use = min(T_gt, HORIZON)\n", + "\n", + " disp = np.sqrt(\n", + " (buf_stoch[\"positions_x\"][:T_use] - gt_x[:T_use]) ** 2\n", + " + (buf_stoch[\"positions_y\"][:T_use] - gt_y[:T_use]) ** 2\n", + " )\n", + " valid_mask = gt_valid[:T_use] > 0\n", + " if valid_mask.sum() > 0:\n", + " ade = disp[valid_mask].mean()\n", + " print(f\"ADE (stochastic vs ground truth): {ade:.3f}m\")\n", + " ade_per_agent = np.array(\n", + " [disp[:, i][valid_mask[:, i]].mean() for i in range(N) if valid_mask[:, i].sum() > 0]\n", + " )\n", + " plt.figure(figsize=(8, 3))\n", + " plt.hist(ade_per_agent, bins=30, edgecolor=\"black\", alpha=0.7)\n", + " plt.xlabel(\"ADE (m)\")\n", + " plt.ylabel(\"Count\")\n", + " plt.title(f\"Per-agent ADE distribution (mean={ade:.3f}m)\")\n", + " plt.tight_layout()\n", + " plt.show()\n", + " else:\n", + " print(\"No valid ground truth timesteps to compute ADE\")\n", + " except Exception as e:\n", + " print(f\"Could not compute ADE: {e}\")" + ] + }, + { + "cell_type": "markdown", + "id": "cell-18", + "metadata": {}, + "source": [ + "## LSTM hidden state analysis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-19", + "metadata": {}, + "outputs": [], + "source": [ + "if use_rnn and \"lstm_h_norm\" in buf_stoch:\n", + " h_norm = buf_stoch[\"lstm_h_norm\"]\n", + "\n", + " fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n", + "\n", + " # Hidden state norm over time\n", + " axes[0].plot(h_norm.mean(axis=1), label=\"mean\", color=\"blue\")\n", + " axes[0].fill_between(\n", + " range(HORIZON),\n", + " h_norm.mean(axis=1) - h_norm.std(axis=1),\n", + " h_norm.mean(axis=1) + h_norm.std(axis=1),\n", + " alpha=0.2,\n", + " color=\"blue\",\n", + " )\n", + " axes[0].set_xlabel(\"Step\")\n", + " axes[0].set_ylabel(\"||h||\")\n", + " axes[0].set_title(\"LSTM hidden state norm over time\")\n", + " axes[0].grid(True, alpha=0.3)\n", + "\n", + " # Histogram at different timesteps\n", + " for t, color in [(0, \"blue\"), (HORIZON // 4, \"green\"), (HORIZON // 2, \"orange\"), (HORIZON - 1, \"red\")]:\n", + " axes[1].hist(h_norm[t], bins=30, alpha=0.4, label=f\"t={t}\", color=color, edgecolor=\"black\")\n", + " axes[1].set_xlabel(\"||h||\")\n", + " axes[1].set_ylabel(\"Count\")\n", + " axes[1].set_title(\"Hidden norm distribution at different timesteps\")\n", + " axes[1].legend()\n", + "\n", + " # Correlation: hidden norm vs value\n", + " axes[2].scatter(h_norm.flatten(), buf_stoch[\"values\"].flatten(), alpha=0.1, s=3)\n", + " corr = np.corrcoef(h_norm.flatten(), buf_stoch[\"values\"].flatten())[0, 1]\n", + " axes[2].set_xlabel(\"||h||\")\n", + " axes[2].set_ylabel(\"Value\")\n", + " axes[2].set_title(f\"Hidden norm vs Value (corr={corr:.3f})\")\n", + " axes[2].grid(True, alpha=0.3)\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + "else:\n", + " print(\"No LSTM — skipping hidden state analysis\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/06_architecture.ipynb b/notebooks/06_architecture.ipynb new file mode 100644 index 0000000000..d1dda5c0ff --- /dev/null +++ b/notebooks/06_architecture.ipynb @@ -0,0 +1,912 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 06 - Neural Network Architecture\n", + "Visualize, analyze, and iterate on the DrivePolicy architecture. Covers model summary, per-encoder breakdown, forward pass shape tracing, weight distributions, and architecture comparison." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import torch\n", + "import torch.nn.functional as F\n", + "from torchinfo import summary\n", + "from pufferlib.ocean.drive.drive import Drive\n", + "from pufferlib.ocean.drive import binding\n", + "from pufferlib.ocean.torch import Drive as DrivePolicy, DriveBackbone, Recurrent\n", + "\n", + "# --- Environment configuration ---\n", + "NUM_AGENTS = 64\n", + "SIMULATION_MODE = \"gigaflow\"\n", + "DYNAMICS_MODEL = \"jerk\"\n", + "ACTION_TYPE = \"discrete\"\n", + "DT = 0.1\n", + "SCENARIO_LENGTH = 512\n", + "RESAMPLE_FREQUENCY = 0\n", + "REWARD_CONDITIONING = True\n", + "REWARD_RANDOMIZATION = False\n", + "TARGET_TYPE = \"static\"\n", + "COLLISION_BEHAVIOR = 1\n", + "OFFROAD_BEHAVIOR = 1\n", + "REACH_GOAL_BEHAVIOR = 1\n", + "SEED = 42\n", + "MAP_DIR = \"../pufferlib/resources/drive/binaries/carla\"\n", + "\n", + "# --- Observation dimensions ---\n", + "MAX_PARTNERS = 20\n", + "MAX_LANES = 100\n", + "MAX_BOUNDS = 50\n", + "MAX_TRAFFIC = 4\n", + "MAX_STOP_SIGNS = 0\n", + "\n", + "# --- Policy architecture ---\n", + "INPUT_SIZE = 64\n", + "BACKBONE_HIDDEN_SIZE = 1024\n", + "BACKBONE_NUM_LAYERS = 3\n", + "ACTOR_HIDDEN_SIZE = 128\n", + "ACTOR_NUM_LAYERS = 3\n", + "CRITIC_HIDDEN_SIZE = 64\n", + "CRITIC_NUM_LAYERS = 2\n", + "SPLIT_NETWORK = False\n", + "ENCODER_GIGAFLOW = True\n", + "DROPOUT = 0.0\n", + "\n", + "# --- Derived from binding ---\n", + "EGO_DIM = binding.EGO_FEATURES_JERK\n", + "NUM_COEFS = binding.NUM_REWARD_COEFS\n", + "PARTNER_F = binding.PARTNER_FEATURES\n", + "ROAD_F = binding.ROAD_FEATURES\n", + "TRAFFIC_F = binding.TRAFFIC_LIGHT_FEATURES\n", + "STOP_SIGN_F = binding.STOP_SIGN_FEATURES\n", + "\n", + "# --- Create environment ---\n", + "env = Drive(\n", + " num_agents=NUM_AGENTS,\n", + " num_maps=1,\n", + " min_agents_per_env=NUM_AGENTS,\n", + " max_agents_per_env=NUM_AGENTS,\n", + " simulation_mode=SIMULATION_MODE,\n", + " dynamics_model=DYNAMICS_MODEL,\n", + " action_type=ACTION_TYPE,\n", + " dt=DT,\n", + " scenario_length=SCENARIO_LENGTH,\n", + " resample_frequency=RESAMPLE_FREQUENCY,\n", + " reward_conditioning=REWARD_CONDITIONING,\n", + " reward_randomization=REWARD_RANDOMIZATION,\n", + " target_type=TARGET_TYPE,\n", + " map_dir=MAP_DIR,\n", + " collision_behavior=COLLISION_BEHAVIOR,\n", + " offroad_behavior=OFFROAD_BEHAVIOR,\n", + " reach_goal_behavior=REACH_GOAL_BEHAVIOR,\n", + " max_lane_segment_observations=MAX_LANES,\n", + " max_boundary_segment_observations=MAX_BOUNDS,\n", + " max_partner_observations=MAX_PARTNERS,\n", + " max_traffic_light_observations=MAX_TRAFFIC,\n", + " max_stop_sign_observations=MAX_STOP_SIGNS,\n", + " seed=SEED,\n", + ")\n", + "obs, info = env.reset(seed=SEED)\n", + "\n", + "MAX_TARGET = env.num_target_waypoints\n", + "TARGET_F = binding.STATIC_TARGET_FEATURES if TARGET_TYPE == \"static\" else binding.DYNAMIC_TARGET_FEATURES\n", + "TARGET_DIM = MAX_TARGET * TARGET_F\n", + "\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "policy = DrivePolicy(\n", + " env,\n", + " input_size=INPUT_SIZE,\n", + " backbone_hidden_size=BACKBONE_HIDDEN_SIZE,\n", + " backbone_num_layers=BACKBONE_NUM_LAYERS,\n", + " actor_hidden_size=ACTOR_HIDDEN_SIZE,\n", + " actor_num_layers=ACTOR_NUM_LAYERS,\n", + " critic_hidden_size=CRITIC_HIDDEN_SIZE,\n", + " critic_num_layers=CRITIC_NUM_LAYERS,\n", + " split_network=SPLIT_NETWORK,\n", + " encoder_gigaflow=ENCODER_GIGAFLOW,\n", + " dropout=DROPOUT,\n", + ").to(device)\n", + "\n", + "print(f\"Device: {device}\")\n", + "print(f\"Obs dim: {obs.shape[1]}\")\n", + "print(f\"Action dim: {policy.atn_dim}\")\n", + "print(f\"Split network: {SPLIT_NETWORK}\")\n", + "print(f\"Backbone: {BACKBONE_HIDDEN_SIZE} x {BACKBONE_NUM_LAYERS}L\")\n", + "print(f\"Actor: {ACTOR_HIDDEN_SIZE} x {ACTOR_NUM_LAYERS}L\")\n", + "print(f\"Critic: {CRITIC_HIDDEN_SIZE} x {CRITIC_NUM_LAYERS}L\")\n", + "print(f\"Encoder gigaflow: {ENCODER_GIGAFLOW}, Dropout: {DROPOUT}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Summary (torchinfo)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "obs_tensor = torch.FloatTensor(obs).to(device)\n", + "summary(policy, input_data=obs_tensor, depth=4, col_names=[\"input_size\", \"output_size\", \"num_params\", \"mult_adds\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Architecture Diagram" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "backbone = policy.actor_backbone\n", + "cond_dim = backbone.conditioning_dim\n", + "\n", + "# Collect encoder info — encoder_gigaflow adds Tanh+Dropout between LN and second Linear\n", + "# ego, partner, conditioning use encoder_gigaflow; lane, boundary, traffic_ctrl use dropout\n", + "encoders = [\n", + " (\"ego\", EGO_DIM, 1, \"direct\", ENCODER_GIGAFLOW),\n", + " (\"conditioning\", cond_dim, 1, \"direct\", ENCODER_GIGAFLOW) if cond_dim > 0 else None,\n", + " (\"partner\", PARTNER_F, MAX_PARTNERS, \"max-pool\", ENCODER_GIGAFLOW),\n", + " (\"lane\", ROAD_F, MAX_LANES, \"max-pool\", ENCODER_GIGAFLOW),\n", + " (\"boundary\", ROAD_F, MAX_BOUNDS, \"max-pool\", ENCODER_GIGAFLOW),\n", + " (\"traffic_ctrl\", TRAFFIC_F + 3, MAX_TRAFFIC, \"max-pool (onehot)\", ENCODER_GIGAFLOW),\n", + "]\n", + "encoders = [e for e in encoders if e is not None]\n", + "\n", + "fig, ax = plt.subplots(figsize=(14, 8))\n", + "ax.set_xlim(0, 10)\n", + "ax.set_ylim(0, 10)\n", + "ax.axis(\"off\")\n", + "\n", + "n_enc = len(encoders)\n", + "y_positions = np.linspace(9, 1, n_enc)\n", + "colors = plt.cm.Set2(np.linspace(0, 1, n_enc))\n", + "\n", + "# Draw encoders\n", + "for i, ((name, in_f, n_obj, agg, gigaflow), y, c) in enumerate(zip(encoders, y_positions, colors)):\n", + " # Input box\n", + " label = f\"{name}\\n{n_obj}x{in_f}\" if n_obj > 1 else f\"{name}\\n{in_f}\"\n", + " ax.add_patch(plt.Rectangle((0.2, y - 0.3), 1.6, 0.6, facecolor=c, edgecolor=\"black\", lw=1.2, alpha=0.8))\n", + " ax.text(1.0, y, label, ha=\"center\", va=\"center\", fontsize=8, fontweight=\"bold\")\n", + "\n", + " # Encoder box — show gigaflow arch vs standard\n", + " ax.add_patch(plt.Rectangle((2.5, y - 0.25), 2.0, 0.5, facecolor=\"lightyellow\", edgecolor=\"black\", lw=1))\n", + " ax.text(3.5, y + 0.05, f\"Linear({in_f},{INPUT_SIZE})\", ha=\"center\", va=\"center\", fontsize=7)\n", + " if gigaflow:\n", + " ax.text(\n", + " 3.5,\n", + " y - 0.12,\n", + " f\"LN+Tanh+Drop+Linear({INPUT_SIZE},{INPUT_SIZE})\",\n", + " ha=\"center\",\n", + " va=\"center\",\n", + " fontsize=5.5,\n", + " color=\"darkgreen\",\n", + " )\n", + " else:\n", + " drop_str = f\"+Drop({DROPOUT})\" if DROPOUT > 0 and name not in (\"ego\", \"partner\", \"conditioning\") else \"\"\n", + " ax.text(\n", + " 3.5,\n", + " y - 0.12,\n", + " f\"LN{drop_str}+Linear({INPUT_SIZE},{INPUT_SIZE})\",\n", + " ha=\"center\",\n", + " va=\"center\",\n", + " fontsize=6,\n", + " color=\"gray\",\n", + " )\n", + "\n", + " # Aggregation\n", + " if n_obj > 1:\n", + " ax.text(5.0, y, agg, ha=\"center\", va=\"center\", fontsize=7, style=\"italic\", color=\"darkblue\")\n", + " arrow_start = 5.5\n", + " else:\n", + " arrow_start = 4.6\n", + "\n", + " # Arrows\n", + " ax.annotate(\"\", xy=(2.5, y), xytext=(1.8, y), arrowprops=dict(arrowstyle=\"->\", lw=1))\n", + " ax.annotate(\"\", xy=(6.0, 5.0), xytext=(arrow_start, y), arrowprops=dict(arrowstyle=\"->\", lw=0.8, color=\"gray\"))\n", + "\n", + "# Concat box\n", + "ax.add_patch(plt.Rectangle((5.8, 4.5), 1.4, 1.0, facecolor=\"lightsalmon\", edgecolor=\"black\", lw=1.5))\n", + "ax.text(6.5, 5.2, \"Concat\", ha=\"center\", va=\"center\", fontsize=9, fontweight=\"bold\")\n", + "ax.text(6.5, 4.85, f\"{n_enc}x{INPUT_SIZE}={n_enc * INPUT_SIZE}\", ha=\"center\", va=\"center\", fontsize=7)\n", + "\n", + "# Backbone\n", + "ax.add_patch(plt.Rectangle((7.5, 4.5), 1.3, 1.0, facecolor=\"lightblue\", edgecolor=\"black\", lw=1.5))\n", + "ax.text(8.15, 5.15, f\"Backbone ({BACKBONE_NUM_LAYERS}L)\", ha=\"center\", va=\"center\", fontsize=8, fontweight=\"bold\")\n", + "ax.text(8.15, 4.85, f\"GELU+Linear\\n({n_enc * INPUT_SIZE},{BACKBONE_HIDDEN_SIZE})\", ha=\"center\", va=\"center\", fontsize=6)\n", + "ax.annotate(\"\", xy=(7.5, 5.0), xytext=(7.2, 5.0), arrowprops=dict(arrowstyle=\"->\", lw=1.5))\n", + "\n", + "# Actor / Critic heads\n", + "ax.add_patch(plt.Rectangle((9.0, 5.7), 0.9, 0.6, facecolor=\"lightgreen\", edgecolor=\"black\", lw=1.2))\n", + "actor_label = f\"Actor ({ACTOR_NUM_LAYERS}L)\\n{BACKBONE_HIDDEN_SIZE}->{sum(policy.atn_dim)}\"\n", + "if ACTOR_NUM_LAYERS > 1:\n", + " actor_label = (\n", + " f\"Actor ({ACTOR_NUM_LAYERS}L)\\n{BACKBONE_HIDDEN_SIZE}->{ACTOR_HIDDEN_SIZE}->...->{sum(policy.atn_dim)}\"\n", + " )\n", + "ax.text(9.45, 6.0, actor_label, ha=\"center\", va=\"center\", fontsize=6, fontweight=\"bold\")\n", + "\n", + "ax.add_patch(plt.Rectangle((9.0, 3.7), 0.9, 0.6, facecolor=\"plum\", edgecolor=\"black\", lw=1.2))\n", + "critic_label = f\"Critic ({CRITIC_NUM_LAYERS}L)\\n{BACKBONE_HIDDEN_SIZE}->1\"\n", + "if CRITIC_NUM_LAYERS > 1:\n", + " critic_label = f\"Critic ({CRITIC_NUM_LAYERS}L)\\n{BACKBONE_HIDDEN_SIZE}->{CRITIC_HIDDEN_SIZE}->...->1\"\n", + "ax.text(9.45, 4.0, critic_label, ha=\"center\", va=\"center\", fontsize=6, fontweight=\"bold\")\n", + "\n", + "ax.annotate(\"\", xy=(9.0, 6.0), xytext=(8.8, 5.3), arrowprops=dict(arrowstyle=\"->\", lw=1.2))\n", + "ax.annotate(\"\", xy=(9.0, 4.0), xytext=(8.8, 4.7), arrowprops=dict(arrowstyle=\"->\", lw=1.2))\n", + "\n", + "split_label = \"SPLIT\" if SPLIT_NETWORK else \"SHARED\"\n", + "ax.text(8.9, 4.55, split_label, ha=\"center\", va=\"center\", fontsize=7, color=\"red\", fontweight=\"bold\")\n", + "\n", + "gigaflow_label = \"GIGAFLOW\" if ENCODER_GIGAFLOW else \"STANDARD\"\n", + "ax.text(\n", + " 5.0,\n", + " 0.3,\n", + " f\"Encoder mode: {gigaflow_label} | Dropout: {DROPOUT}\",\n", + " ha=\"center\",\n", + " va=\"center\",\n", + " fontsize=8,\n", + " color=\"darkgreen\",\n", + " fontweight=\"bold\",\n", + ")\n", + "\n", + "ax.set_title(\n", + " f\"DrivePolicy Architecture (input_size={INPUT_SIZE}, backbone={BACKBONE_HIDDEN_SIZE})\",\n", + " fontsize=12,\n", + " fontweight=\"bold\",\n", + ")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Per-Encoder Parameter Breakdown" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def count_params(module):\n", + " return sum(p.numel() for p in module.parameters())\n", + "\n", + "\n", + "backbone = policy.actor_backbone\n", + "components = {\n", + " \"ego_encoder\": backbone.ego_encoder,\n", + " \"lane_encoder\": backbone.lane_encoder,\n", + " \"boundary_encoder\": backbone.boundary_encoder,\n", + " \"partner_encoder\": backbone.partner_encoder,\n", + " \"traffic_ctrl_encoder\": backbone.traffic_light_encoder,\n", + "}\n", + "if backbone.conditioning_dim > 0:\n", + " components[\"conditioning_encoder\"] = backbone.conditioning_encoder\n", + "components[\"backbone_mlp\"] = backbone.backbone\n", + "components[\"actor_head\"] = policy.actor_head\n", + "components[\"critic_head\"] = policy.critic_head\n", + "\n", + "names, counts = zip(*[(k, count_params(v)) for k, v in components.items()])\n", + "total = sum(counts)\n", + "\n", + "print(f\"{'Component':>25s} | {'Params':>10s} | {'%':>6s}\")\n", + "print(\"-\" * 48)\n", + "for n, c in zip(names, counts):\n", + " print(f\"{n:>25s} | {c:>10,d} | {c / total:>5.1%}\")\n", + "print(\"-\" * 48)\n", + "print(f\"{'TOTAL':>25s} | {total:>10,d}\")\n", + "if SPLIT_NETWORK:\n", + " critic_bb = count_params(policy.critic_backbone)\n", + " print(f\"{'+ critic_backbone':>25s} | {critic_bb:>10,d}\")\n", + " print(f\"{'GRAND TOTAL':>25s} | {total + critic_bb:>10,d}\")\n", + "\n", + "fig, ax = plt.subplots(figsize=(8, 5))\n", + "colors = plt.cm.Set3(np.linspace(0, 1, len(names)))\n", + "bars = ax.barh(names, counts, color=colors, edgecolor=\"black\")\n", + "for bar, c in zip(bars, counts):\n", + " ax.text(bar.get_width() + total * 0.01, bar.get_y() + bar.get_height() / 2, f\"{c:,}\", va=\"center\", fontsize=8)\n", + "ax.set_xlabel(\"Parameters\")\n", + "ax.set_title(f\"Parameter Distribution ({total:,} total)\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Forward Pass Shape Trace" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x = obs_tensor\n", + "backbone = policy.actor_backbone\n", + "\n", + "slide_idx = EGO_DIM\n", + "cond_dim = backbone.conditioning_dim\n", + "partner_dim = MAX_PARTNERS * PARTNER_F\n", + "lane_dim = MAX_LANES * ROAD_F\n", + "boundary_dim = MAX_BOUNDS * ROAD_F\n", + "traffic_dim = MAX_TRAFFIC * TRAFFIC_F\n", + "\n", + "# Slicing\n", + "ego_obs = x[:, :slide_idx]\n", + "slices = [(\"ego\", 0, slide_idx, ego_obs.shape)]\n", + "\n", + "if cond_dim > 0:\n", + " cond_obs = x[:, slide_idx : slide_idx + cond_dim]\n", + " slices.append((\"conditioning\", slide_idx, slide_idx + cond_dim, cond_obs.shape))\n", + " slide_idx += cond_dim\n", + "\n", + "partner_obs = x[:, slide_idx : slide_idx + partner_dim]\n", + "slices.append((\"partners\", slide_idx, slide_idx + partner_dim, partner_obs.shape))\n", + "slide_idx += partner_dim\n", + "\n", + "lane_obs = x[:, slide_idx : slide_idx + lane_dim]\n", + "slices.append((\"lanes\", slide_idx, slide_idx + lane_dim, lane_obs.shape))\n", + "slide_idx += lane_dim\n", + "\n", + "boundary_obs = x[:, slide_idx : slide_idx + boundary_dim]\n", + "slices.append((\"boundaries\", slide_idx, slide_idx + boundary_dim, boundary_obs.shape))\n", + "slide_idx += boundary_dim\n", + "\n", + "traffic_obs = x[:, slide_idx : slide_idx + traffic_dim]\n", + "slices.append((\"traffic_ctrl\", slide_idx, slide_idx + traffic_dim, traffic_obs.shape))\n", + "\n", + "print(f\"Obs buffer layout (total={x.shape[1]}):\")\n", + "print(f\"{'Name':>15s} | {'Start':>5s} | {'End':>5s} | {'Width':>5s} | Shape\")\n", + "print(\"-\" * 65)\n", + "for name, start, end, shape in slices:\n", + " print(f\"{name:>15s} | {start:>5d} | {end:>5d} | {end - start:>5d} | {shape}\")\n", + "\n", + "# Forward through encoders\n", + "print(\"\\nEncoder outputs:\")\n", + "with torch.no_grad():\n", + " ego_enc = backbone.ego_encoder(ego_obs)\n", + " print(f\" ego_encoder: {ego_obs.shape} -> {ego_enc.shape}\")\n", + "\n", + " if cond_dim > 0:\n", + " cond_enc = backbone.conditioning_encoder(cond_obs)\n", + " print(f\" cond_encoder: {cond_obs.shape} -> {cond_enc.shape}\")\n", + "\n", + " p_reshaped = partner_obs.view(-1, MAX_PARTNERS, PARTNER_F)\n", + " p_enc, _ = backbone.partner_encoder(p_reshaped).max(dim=1)\n", + " print(f\" partner_encoder: {partner_obs.shape} -> view {p_reshaped.shape} -> encode -> max-pool -> {p_enc.shape}\")\n", + "\n", + " l_reshaped = lane_obs.view(-1, MAX_LANES, ROAD_F)\n", + " l_enc, _ = backbone.lane_encoder(l_reshaped).max(dim=1)\n", + " print(f\" lane_encoder: {lane_obs.shape} -> view {l_reshaped.shape} -> encode -> max-pool -> {l_enc.shape}\")\n", + "\n", + " b_reshaped = boundary_obs.view(-1, MAX_BOUNDS, ROAD_F)\n", + " b_enc, _ = backbone.boundary_encoder(b_reshaped).max(dim=1)\n", + " print(f\" bound_encoder: {boundary_obs.shape} -> view {b_reshaped.shape} -> encode -> max-pool -> {b_enc.shape}\")\n", + "\n", + " t_reshaped = traffic_obs.view(-1, MAX_TRAFFIC, TRAFFIC_F)\n", + " t_cont = t_reshaped[:, :, : TRAFFIC_F - 1]\n", + " t_cat = t_reshaped[:, :, TRAFFIC_F - 1]\n", + " t_onehot = F.one_hot(t_cat.long(), num_classes=4)\n", + " t_input = torch.cat([t_cont, t_onehot], dim=2)\n", + " t_enc, _ = backbone.traffic_light_encoder(t_input).max(dim=1)\n", + " print(\n", + " f\" traffic_encoder: {traffic_obs.shape} -> view {t_reshaped.shape} -> onehot {t_input.shape} -> encode -> max-pool -> {t_enc.shape}\"\n", + " )\n", + "\n", + " # Concat + backbone\n", + " features = [ego_enc, l_enc, b_enc, p_enc, t_enc]\n", + " if cond_dim > 0:\n", + " features.append(cond_enc)\n", + " concat = torch.cat(features, dim=1)\n", + " hidden = backbone.backbone(concat)\n", + " print(f\"\\n concat: {concat.shape}\")\n", + " print(f\" backbone_mlp: {concat.shape} -> {hidden.shape}\")\n", + "\n", + " # Heads\n", + " actor_out = policy.actor_head(hidden)\n", + " critic_out = policy.critic_head(hidden)\n", + " print(f\" actor_head: {hidden.shape} -> {actor_out.shape} (split into {policy.atn_dim})\")\n", + " print(f\" critic_head: {hidden.shape} -> {critic_out.shape}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Weight Distributions by Layer" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "weight_data = [\n", + " (n, p.data.cpu().numpy().flatten()) for n, p in policy.named_parameters() if \"weight\" in n and p.dim() >= 2\n", + "]\n", + "\n", + "n_weights = len(weight_data)\n", + "cols = 4\n", + "rows = (n_weights + cols - 1) // cols\n", + "fig, axes = plt.subplots(rows, cols, figsize=(4 * cols, 3 * rows))\n", + "axes = axes.flatten()\n", + "\n", + "for i, (name, w) in enumerate(weight_data):\n", + " ax = axes[i]\n", + " ax.hist(w, bins=50, edgecolor=\"black\", alpha=0.7, density=True)\n", + " ax.set_title(name.replace(\"actor_backbone.\", \"\"), fontsize=7)\n", + " ax.axvline(0, color=\"red\", ls=\"--\", lw=0.5)\n", + " ax.text(0.95, 0.95, f\"std={w.std():.3f}\", transform=ax.transAxes, fontsize=6, ha=\"right\", va=\"top\")\n", + "\n", + "for j in range(i + 1, len(axes)):\n", + " axes[j].axis(\"off\")\n", + "\n", + "fig.suptitle(\"Weight Distributions (init)\", fontsize=12, fontweight=\"bold\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Activation Analysis (per encoder)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "policy.eval()\n", + "with torch.no_grad():\n", + " hidden = policy.actor_backbone(obs_tensor, EGO_DIM)\n", + " action_logits, value = policy.decode_actions(hidden)\n", + "\n", + "# Collect per-encoder activations\n", + "activations = {}\n", + "with torch.no_grad():\n", + " slide = EGO_DIM\n", + " activations[\"ego\"] = backbone.ego_encoder(obs_tensor[:, :EGO_DIM])\n", + "\n", + " if cond_dim > 0:\n", + " activations[\"conditioning\"] = backbone.conditioning_encoder(obs_tensor[:, slide : slide + cond_dim])\n", + " slide += cond_dim\n", + "\n", + " p_obs = obs_tensor[:, slide : slide + partner_dim].view(-1, MAX_PARTNERS, PARTNER_F)\n", + " activations[\"partner\"], _ = backbone.partner_encoder(p_obs).max(dim=1)\n", + " slide += partner_dim\n", + "\n", + " l_obs = obs_tensor[:, slide : slide + lane_dim].view(-1, MAX_LANES, ROAD_F)\n", + " activations[\"lane\"], _ = backbone.lane_encoder(l_obs).max(dim=1)\n", + " slide += lane_dim\n", + "\n", + " b_obs = obs_tensor[:, slide : slide + boundary_dim].view(-1, MAX_BOUNDS, ROAD_F)\n", + " activations[\"boundary\"], _ = backbone.boundary_encoder(b_obs).max(dim=1)\n", + " slide += boundary_dim\n", + "\n", + " t_obs = obs_tensor[:, slide : slide + traffic_dim].view(-1, MAX_TRAFFIC, TRAFFIC_F)\n", + " t_cont = t_obs[:, :, : TRAFFIC_F - 1]\n", + " t_cat = t_obs[:, :, TRAFFIC_F - 1]\n", + " t_onehot = F.one_hot(t_cat.long(), num_classes=4)\n", + " t_input = torch.cat([t_cont, t_onehot], dim=2)\n", + " activations[\"traffic_ctrl\"], _ = backbone.traffic_light_encoder(t_input).max(dim=1)\n", + "\n", + " activations[\"hidden\"] = hidden\n", + "\n", + "fig, axes = plt.subplots(2, 4, figsize=(16, 6))\n", + "axes = axes.flatten()\n", + "for i, (name, act) in enumerate(activations.items()):\n", + " if i >= len(axes):\n", + " break\n", + " vals = act.cpu().numpy().flatten()\n", + " ax = axes[i]\n", + " ax.hist(vals, bins=50, edgecolor=\"black\", alpha=0.7)\n", + " dead = (act.abs().sum(dim=0) == 0).sum().item()\n", + " ax.set_title(f\"{name} (dead={dead}/{act.shape[1]})\", fontsize=9)\n", + " ax.text(\n", + " 0.95,\n", + " 0.95,\n", + " f\"mean={vals.mean():.3f}\\nstd={vals.std():.3f}\",\n", + " transform=ax.transAxes,\n", + " fontsize=7,\n", + " ha=\"right\",\n", + " va=\"top\",\n", + " )\n", + "\n", + "for j in range(i + 1, len(axes)):\n", + " axes[j].axis(\"off\")\n", + "\n", + "fig.suptitle(\"Per-Encoder Activation Distributions\", fontsize=12, fontweight=\"bold\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Encoder Embedding Similarity (cosine)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Mean embedding per encoder (exclude hidden — different dim)\n", + "emb_names = [k for k in activations.keys() if k != \"hidden\"]\n", + "emb_means = torch.stack([activations[k].mean(dim=0) for k in emb_names])\n", + "emb_norm = F.normalize(emb_means, dim=1)\n", + "sim_matrix = (emb_norm @ emb_norm.T).cpu().numpy()\n", + "\n", + "fig, ax = plt.subplots(figsize=(7, 6))\n", + "im = ax.imshow(sim_matrix, cmap=\"RdBu_r\", vmin=-1, vmax=1)\n", + "ax.set_xticks(range(len(emb_names)))\n", + "ax.set_yticks(range(len(emb_names)))\n", + "ax.set_xticklabels(emb_names, rotation=45, ha=\"right\", fontsize=8)\n", + "ax.set_yticklabels(emb_names, fontsize=8)\n", + "for i in range(len(emb_names)):\n", + " for j in range(len(emb_names)):\n", + " ax.text(j, i, f\"{sim_matrix[i, j]:.2f}\", ha=\"center\", va=\"center\", fontsize=7)\n", + "fig.colorbar(im, ax=ax)\n", + "ax.set_title(\"Cosine Similarity Between Encoder Mean Embeddings\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Architecture Comparison\n", + "Compare different architecture configs side-by-side without training." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "configs = [\n", + " {\"name\": \"tiny\", \"input_size\": 32, \"backbone_hidden_size\": 64},\n", + " {\"name\": \"small\", \"input_size\": 64, \"backbone_hidden_size\": 128},\n", + " {\"name\": \"medium\", \"input_size\": 128, \"backbone_hidden_size\": 256, \"backbone_num_layers\": 2},\n", + " {\n", + " \"name\": \"large\",\n", + " \"input_size\": 128,\n", + " \"backbone_hidden_size\": 512,\n", + " \"backbone_num_layers\": 2,\n", + " \"actor_num_layers\": 2,\n", + " \"actor_hidden_size\": 256,\n", + " \"critic_num_layers\": 2,\n", + " \"critic_hidden_size\": 256,\n", + " },\n", + " {\n", + " \"name\": \"xlarge\",\n", + " \"input_size\": 256,\n", + " \"backbone_hidden_size\": 1024,\n", + " \"backbone_num_layers\": 3,\n", + " \"actor_num_layers\": 2,\n", + " \"actor_hidden_size\": 512,\n", + " \"critic_num_layers\": 2,\n", + " \"critic_hidden_size\": 512,\n", + " },\n", + " {\"name\": \"small+giga\", \"input_size\": 64, \"backbone_hidden_size\": 128, \"encoder_gigaflow\": True, \"dropout\": 0.1},\n", + " {\n", + " \"name\": \"medium+giga\",\n", + " \"input_size\": 128,\n", + " \"backbone_hidden_size\": 256,\n", + " \"backbone_num_layers\": 2,\n", + " \"encoder_gigaflow\": True,\n", + " \"dropout\": 0.1,\n", + " },\n", + "]\n", + "\n", + "POLICY_DEFAULTS = {\n", + " \"backbone_num_layers\": 1,\n", + " \"actor_hidden_size\": 128,\n", + " \"actor_num_layers\": 0,\n", + " \"critic_hidden_size\": 128,\n", + " \"critic_num_layers\": 0,\n", + " \"encoder_gigaflow\": False,\n", + " \"dropout\": 0.0,\n", + " \"split_network\": False,\n", + "}\n", + "\n", + "results = []\n", + "for cfg in configs:\n", + " name = cfg.pop(\"name\")\n", + " full_cfg = {**POLICY_DEFAULTS, **cfg}\n", + " p = DrivePolicy(env, **full_cfg).to(device)\n", + " n_params = sum(pp.numel() for pp in p.parameters())\n", + "\n", + " with torch.no_grad():\n", + " import time\n", + "\n", + " t0 = time.time()\n", + " for _ in range(100):\n", + " p(obs_tensor)\n", + " if device.type == \"cuda\":\n", + " torch.cuda.synchronize()\n", + " ms_per_fwd = (time.time() - t0) / 100 * 1000\n", + "\n", + " results.append({\"name\": name, \"params\": n_params, \"ms/fwd\": ms_per_fwd, **cfg})\n", + " cfg[\"name\"] = name # restore\n", + " del p\n", + "\n", + "print(\n", + " f\"{'Config':>12s} | {'input':>5s} | {'bb_h':>5s} | {'bb_L':>4s} | {'act_h':>5s} | {'act_L':>5s} | {'crt_h':>5s} | {'crt_L':>5s} | {'giga':>5s} | {'Params':>10s} | {'ms/fwd':>8s}\"\n", + ")\n", + "print(\"-\" * 105)\n", + "for r in results:\n", + " print(\n", + " f\"{r['name']:>12s} | {r['input_size']:>5d} | {r.get('backbone_hidden_size', 1024):>5d} | {r.get('backbone_num_layers', 1):>4d} | {r.get('actor_hidden_size', 1024):>5d} | {r.get('actor_num_layers', 1):>5d} | {r.get('critic_hidden_size', 1024):>5d} | {r.get('critic_num_layers', 1):>5d} | {str(r.get('encoder_gigaflow', False)):>5s} | {r['params']:>10,d} | {r['ms/fwd']:>7.2f}ms\"\n", + " )\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(14, 4))\n", + "names = [r[\"name\"] for r in results]\n", + "params = [r[\"params\"] for r in results]\n", + "times = [r[\"ms/fwd\"] for r in results]\n", + "\n", + "bar_colors = [\"coral\" if r.get(\"encoder_gigaflow\") else \"steelblue\" for r in results]\n", + "\n", + "axes[0].bar(names, params, color=bar_colors, edgecolor=\"black\")\n", + "axes[0].set_ylabel(\"Parameters\")\n", + "axes[0].set_title(\"Parameter Count (orange=gigaflow)\")\n", + "axes[0].tick_params(axis=\"x\", rotation=30)\n", + "for i, v in enumerate(params):\n", + " axes[0].text(i, v, f\"{v:,}\", ha=\"center\", va=\"bottom\", fontsize=7)\n", + "\n", + "axes[1].bar(names, times, color=bar_colors, edgecolor=\"black\")\n", + "axes[1].set_ylabel(\"ms / forward\")\n", + "axes[1].set_title(f\"Forward Pass Latency ({NUM_AGENTS} agents)\")\n", + "axes[1].tick_params(axis=\"x\", rotation=30)\n", + "for i, v in enumerate(times):\n", + " axes[1].text(i, v, f\"{v:.2f}\", ha=\"center\", va=\"bottom\", fontsize=7)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Observation Buffer Utilization\n", + "How much of each observation slot is actually filled (non-zero)?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Run a few steps to get diverse observations\n", + "actions = np.zeros((NUM_AGENTS, len(env.single_action_space.nvec)), dtype=np.int64)\n", + "all_obs = [obs]\n", + "for _ in range(20):\n", + " o, _, _, _, _ = env.step(actions)\n", + " all_obs.append(o)\n", + "stacked = np.concatenate(all_obs, axis=0)\n", + "\n", + "slide = EGO_DIM\n", + "segments = [(\"ego\", 0, EGO_DIM, 1, EGO_DIM)]\n", + "if cond_dim > 0:\n", + " segments.append((\"conditioning\", slide, slide + cond_dim, 1, cond_dim))\n", + " slide += cond_dim\n", + "segments.append((\"partners\", slide, slide + partner_dim, MAX_PARTNERS, PARTNER_F))\n", + "slide += partner_dim\n", + "segments.append((\"lanes\", slide, slide + lane_dim, MAX_LANES, ROAD_F))\n", + "slide += lane_dim\n", + "segments.append((\"boundaries\", slide, slide + boundary_dim, MAX_BOUNDS, ROAD_F))\n", + "slide += boundary_dim\n", + "segments.append((\"traffic\", slide, slide + traffic_dim, MAX_TRAFFIC, TRAFFIC_F))\n", + "\n", + "print(f\"{'Segment':>15s} | {'Slots':>5s} | {'Features':>8s} | {'Fill %':>7s} | {'Mean':>8s} | {'Std':>8s}\")\n", + "print(\"-\" * 65)\n", + "fill_rates = []\n", + "seg_names = []\n", + "for name, start, end, n_slots, n_feat in segments:\n", + " chunk = stacked[:, start:end]\n", + " if n_slots > 1:\n", + " reshaped = chunk.reshape(-1, n_slots, n_feat)\n", + " # A slot is \"filled\" if any feature is non-zero\n", + " filled = (np.abs(reshaped).sum(axis=2) > 1e-8).mean()\n", + " else:\n", + " filled = (np.abs(chunk) > 1e-8).mean()\n", + " fill_rates.append(filled * 100)\n", + " seg_names.append(name)\n", + " print(f\"{name:>15s} | {n_slots:>5d} | {n_feat:>8d} | {filled:>6.1%} | {chunk.mean():>8.4f} | {chunk.std():>8.4f}\")\n", + "\n", + "fig, ax = plt.subplots(figsize=(8, 4))\n", + "colors = [\"#2ecc71\" if f > 50 else \"#e74c3c\" if f < 10 else \"#f39c12\" for f in fill_rates]\n", + "ax.barh(seg_names, fill_rates, color=colors, edgecolor=\"black\")\n", + "ax.set_xlabel(\"Fill Rate (%)\")\n", + "ax.set_title(\"Observation Slot Utilization\")\n", + "ax.axvline(50, color=\"gray\", ls=\"--\", alpha=0.5)\n", + "for i, v in enumerate(fill_rates):\n", + " ax.text(v + 1, i, f\"{v:.1f}%\", va=\"center\", fontsize=8)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## LSTM Wrapper Architecture" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "base_policy = DrivePolicy(\n", + " env,\n", + " input_size=INPUT_SIZE,\n", + " backbone_hidden_size=BACKBONE_HIDDEN_SIZE,\n", + " backbone_num_layers=BACKBONE_NUM_LAYERS,\n", + " actor_hidden_size=ACTOR_HIDDEN_SIZE,\n", + " actor_num_layers=ACTOR_NUM_LAYERS,\n", + " critic_hidden_size=CRITIC_HIDDEN_SIZE,\n", + " critic_num_layers=CRITIC_NUM_LAYERS,\n", + " encoder_gigaflow=ENCODER_GIGAFLOW,\n", + " dropout=DROPOUT,\n", + " split_network=SPLIT_NETWORK,\n", + ").to(device)\n", + "# LSTM input_size must match backbone_hidden_size (backbone output dim)\n", + "lstm_policy = Recurrent(env, base_policy, input_size=BACKBONE_HIDDEN_SIZE, hidden_size=BACKBONE_HIDDEN_SIZE).to(device)\n", + "\n", + "base_params = sum(p.numel() for p in base_policy.parameters())\n", + "lstm_params = sum(p.numel() for p in lstm_policy.parameters())\n", + "lstm_only = lstm_params - base_params\n", + "\n", + "print(f\"Base policy params: {base_params:>10,d}\")\n", + "print(f\"LSTM wrapper params: {lstm_params:>10,d}\")\n", + "print(f\"LSTM overhead: {lstm_only:>10,d} (+{lstm_only / base_params:.1%})\")\n", + "print()\n", + "\n", + "# torchinfo can't handle LSTMWrapper (requires state dict), so manual breakdown\n", + "print(f\"{'Component':>25s} | {'Params':>10s}\")\n", + "print(\"-\" * 40)\n", + "for name, module in lstm_policy.named_children():\n", + " n = sum(p.numel() for p in module.parameters())\n", + " print(f\"{name:>25s} | {n:>10,d}\")\n", + "print()\n", + "\n", + "# Verify forward pass works with proper state\n", + "# forward_eval uses LSTMCell which expects 2D state (batch, hidden)\n", + "with torch.no_grad():\n", + " state = {\n", + " \"lstm_h\": torch.zeros(NUM_AGENTS, BACKBONE_HIDDEN_SIZE, device=device),\n", + " \"lstm_c\": torch.zeros(NUM_AGENTS, BACKBONE_HIDDEN_SIZE, device=device),\n", + " }\n", + " actions, value = lstm_policy.forward_eval(obs_tensor, state)\n", + " if isinstance(actions, (list, tuple)):\n", + " print(f\"Forward eval OK: actions={[a.shape for a in actions]}, value={value.shape}\")\n", + " else:\n", + " print(f\"Forward eval OK: actions={actions}, value={value.shape}\")\n", + "\n", + "del base_policy, lstm_policy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Effective Receptive Field\n", + "Which input features have the most influence on the hidden representation?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Jacobian-based sensitivity: d(hidden) / d(obs) magnitude\n", + "sample = obs_tensor[:1].clone().requires_grad_(True)\n", + "hidden = policy.actor_backbone(sample, EGO_DIM)\n", + "# Sum hidden to scalar for backward\n", + "hidden.sum().backward()\n", + "sensitivity = sample.grad.abs().squeeze().cpu().numpy()\n", + "\n", + "fig, axes = plt.subplots(2, 1, figsize=(14, 6), gridspec_kw={\"height_ratios\": [2, 1]})\n", + "\n", + "# Full sensitivity\n", + "axes[0].plot(sensitivity, lw=0.5, color=\"steelblue\")\n", + "axes[0].set_ylabel(\"|grad|\")\n", + "axes[0].set_title(\"Input Feature Sensitivity (|d hidden / d obs|)\")\n", + "\n", + "# Mark segments\n", + "seg_boundaries = [0, EGO_DIM]\n", + "seg_labels = [\"ego\"]\n", + "s = EGO_DIM\n", + "if cond_dim > 0:\n", + " s += cond_dim\n", + " seg_boundaries.append(s)\n", + " seg_labels.append(\"cond\")\n", + "for name, dim in [\n", + " (\"partners\", partner_dim),\n", + " (\"lanes\", lane_dim),\n", + " (\"boundaries\", boundary_dim),\n", + " (\"traffic\", traffic_dim),\n", + "]:\n", + " s += dim\n", + " seg_boundaries.append(s)\n", + " seg_labels.append(name)\n", + "\n", + "seg_colors = plt.cm.Set2(np.linspace(0, 1, len(seg_labels)))\n", + "for i, (label, c) in enumerate(zip(seg_labels, seg_colors)):\n", + " start, end = seg_boundaries[i], seg_boundaries[i + 1]\n", + " axes[0].axvspan(start, end, alpha=0.15, color=c)\n", + " axes[0].text((start + end) / 2, axes[0].get_ylim()[1] * 0.9, label, ha=\"center\", fontsize=7, color=\"black\")\n", + "\n", + "# Per-segment mean sensitivity\n", + "seg_means = []\n", + "for i in range(len(seg_labels)):\n", + " start, end = seg_boundaries[i], seg_boundaries[i + 1]\n", + " seg_means.append(sensitivity[start:end].mean())\n", + "\n", + "axes[1].bar(seg_labels, seg_means, color=seg_colors, edgecolor=\"black\")\n", + "axes[1].set_ylabel(\"Mean |grad|\")\n", + "axes[1].set_title(\"Mean Sensitivity per Observation Segment\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "policy.zero_grad()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/pufferlib/__init__.py b/pufferlib/__init__.py index 7fb2007e2b..95f71d92db 100644 --- a/pufferlib/__init__.py +++ b/pufferlib/__init__.py @@ -2,13 +2,6 @@ import os -path = __path__[0] -link_to = os.path.join(path, "resources") -try: - os.symlink(link_to, "resources") -except FileExistsError: - pass - # Silence noisy dependencies import warnings diff --git a/pufferlib/config/default.ini b/pufferlib/config/default.ini index 72ce8e0502..96ce1d06cf 100644 --- a/pufferlib/config/default.ini +++ b/pufferlib/config/default.ini @@ -22,10 +22,10 @@ name = pufferai project = ablations seed = 42 -torch_deterministic = True +torch_deterministic = False cpu_offload = False device = cuda -optimizer = muon +optimizer = adam anneal_lr = True precision = float32 total_timesteps = 10_000_000 @@ -49,7 +49,7 @@ minibatch_size = 8192 # Accumulate gradients above this size max_minibatch_size = 32768 -rollout_horizon = 64 +bptt_horizon = 64 compile = False compile_mode = max-autotune-no-cudagraphs compile_fullgraph = True @@ -60,6 +60,10 @@ vtrace_c_clip = 1.0 prio_alpha = 0.8 prio_beta0 = 0.2 +ppo_granularity = auto +adv_filter_ewma_beta = 0.25 +adv_filter_threshold_scale = 0.01 + [sweep] method = Protein metric = score @@ -81,7 +85,7 @@ downsample = 10 ; mean = 1e8 ; scale = time -; [sweep.train.rollout_horizon] +; [sweep.train.bptt_horizon] ; distribution = uniform_pow2 ; min = 16 ; max = 64 diff --git a/pufferlib/config/ocean/drive.ini b/pufferlib/config/ocean/drive.ini index e7224203d2..bf69f486b7 100644 --- a/pufferlib/config/ocean/drive.ini +++ b/pufferlib/config/ocean/drive.ini @@ -2,179 +2,355 @@ package = ocean env_name = puffer_drive policy_name = Drive -; Set to None to disable recurrence. In case this the policy will only see -; the current observation (o_t). Set to "Recurrent" to provide the policy with -; memory through an LSTM. -rnn_name = Recurrent +;Options: Recurrent, None +rnn_name = None [vec] -num_workers = 16 -num_envs = 16 -batch_size = 4 -;backend = Serial +num_envs = 8 +num_workers = auto +batch_size = auto +; backend = Serial [policy] input_size = 64 -hidden_size = 256 +backbone_hidden_size = 512 +backbone_num_layers = 4 +actor_hidden_size = 512 +actor_num_layers = 0 +critic_hidden_size = 512 +critic_num_layers = 0 +; True for GF network, False for LSTM one +split_network = False +encoder_gigaflow = True +dropout = 0.0 [rnn] -input_size = 256 -hidden_size = 256 +input_size = 512 +hidden_size = 512 [env] +; Options: "gigaflow", "replay" +simulation_mode = "gigaflow" +; Reward conditioning +reward_conditioning = False +; Reward randomization +reward_randomization = False +; Total number of agents in the environment num_agents = 1024 -; Options: discrete, continuous -action_type = discrete -; Options: classic, jerk -dynamics_model = classic -reward_vehicle_collision = -0.5 -reward_offroad_collision = -0.5 +; GIGAFLOW-specific: minimum number of agents per environment +min_agents_per_env = 1 +; GIGAFLOW-specific: maximum number of agents per environment +max_agents_per_env = 80 +; Actions type - options: "discrete", "continuous" +action_type = "discrete" +; Dynamics model - options: "classic", "jerk" +dynamics_model = "jerk" +; Time delta between steps in seconds dt = 0.1 +; Optional nonzero launch speed for gigaflow random spawns +spawn_initial_speed = 0.0 +; Target representation - options: "static", "dynamic" +target_type = "static" +; Collision behavior - options: 0 - Ignore, 1 - Stop, 2 - Remove +collision_behavior = 1 +; Offroad behavior - options: 0 - Ignore, 1 - Stop, 2 - Remove +offroad_behavior = 1 +; Traffic light behavior - options: 0 - Ignore, 1 - Stop, 2 - Remove +traffic_light_behavior = 1 +; Reward coefficients reward_goal = 1.0 -reward_goal_post_respawn = 0.25 +reward_vehicle_collision = 3.0 +reward_offroad_collision = 3.0 +reward_comfort = 0.05 +reward_lane_align = 0.025 +reward_vel_align = 1.0 +reward_lane_center = 0.0038 +reward_center_bias = 0.0 +reward_velocity = 0.0025 +reward_reverse = 0.005 +reward_traffic_light_violation = 1.0 +reward_timestep = 0.000025 +reward_overspeed = 0.05 +reward_ade = 0.0 ; Meters around goal to be considered "reached" goal_radius = 2.0 -; Max target speed in m/s for the agent to maintain towards the goal -goal_speed = 100.0 -; What to do when the goal is reached. Options: 0:"respawn", 1:"generate_new_goals", 2:"stop" -goal_behavior = 0 -; Determines the target distance to the new goal in the case of goal_behavior = generate_new_goals. -; Large numbers will select a goal point further away from the agent's current position. -goal_target_distance = 30.0 -; Options: 0 - Ignore, 1 - Stop, 2 - Remove -collision_behavior = 0 -; Options: 0 - Ignore, 1 - Stop, 2 - Remove -offroad_behavior = 0 -; Number of steps before -episode_length = 91 -resample_frequency = 910 -termination_mode = 1 # 0 - terminate at episode_length, 1 - terminate after all agents have been reset -map_dir = "resources/drive/binaries/training" -num_maps = 10000 +; Number of target waypoints in the observation (including goal) +num_target_waypoints = 3 +; Waypoint spacing range in meters when building path +min_waypoint_spacing = 20.0 +max_waypoint_spacing = 60.0 +; Number of steps before reset +scenario_length = 1024 +; Frequency of resampling scenario (in steps), 0 to disable +resample_frequency = 102_400_000 +; End of episode - options: 0 - scenario_length, 1 - early reset when too many agents are removed +termination_mode = 1 +; Ratio of inactive agents (stopped/removed) that triggers early reset (used when termination_mode = 1) +inactive_agent_threshold = 0.4 +; Path to map used for training +map_dir = "pufferlib/resources/drive/binaries/carla" +; Number of maps to load from map_dir +num_maps = 8 ; Determines which step of the trajectory to initialize the agents at upon reset init_steps = 0 -; Options: "control_vehicles", "control_agents", "control_wosac", "control_sdc_only", "control_mixed_play" -control_mode = "control_agents" +; options: "control_vehicles", "control_agents", "control_tracks_to_predict", "control_sdc_only" +control_mode = "control_vehicles" ; Options: "created_all_valid", "create_only_controlled" init_mode = "create_all_valid" -; Sets the maximum number of controllable agents per scene, ONLY used if control_mode is "control_mixed_play" -max_controlled_agents = 32 -; Render mode options: -; 0:"window" = pop-up raylib window (original) -; 1:"headless" = off-screen; frames piped to ffmpeg (recommended for training) -render_mode = 1 +; Enable computation of evaluation-only metrics +compute_eval_metrics = True +; Use rear-axle coordinates for trajectory planning/control while keeping sim_x/sim_y at vehicle center +use_rear_axle = True +; Max number of observations +max_lane_segment_observations = 64 +max_boundary_segment_observations = 32 +max_partner_observations = 20 +max_traffic_light_observations = 4 +max_stop_sign_observations = 0 [train] -seed=42 -total_timesteps = 2_000_000_000 -; learning_rate = 0.02 -; gamma = 0.985 +total_timesteps = 10_000_000_000 anneal_lr = True -anneal_entropy = False -; Needs to be: num_agents * num_workers * rollout_horizon -batch_size = 524288 -minibatch_size = 32768 -max_minibatch_size = 32768 -; bptt_horizon equals rollout_horizon at the moment. -; If you want to turn off recurrence, set rnn_name = None in [base] -rollout_horizon = 32 +; Needs to be: num_agents * num_workers * BPTT horizon +batch_size = auto +minibatch_size = 65_536 +max_minibatch_size = 65_536 +bptt_horizon = 128 adam_beta1 = 0.9 adam_beta2 = 0.999 adam_eps = 1e-8 clip_coef = 0.2 -ent_coef = 0.005 +ent_coef = 0.01 gae_lambda = 0.95 -gamma = 0.98 -learning_rate = 0.003 -max_grad_norm = 1 +gamma = 0.99 +learning_rate = 0.0005 +max_grad_norm = 0.5 prio_alpha = 0.8499999999999999 prio_beta0 = 0.8499999999999999 update_epochs = 1 vf_clip_coef = 0.1999999999999999 -vf_coef = 2 +vf_coef = 0.5 vtrace_c_clip = 1 vtrace_rho_clip = 1 -checkpoint_interval = 1000 +ppo_granularity = auto +adv_filter_ewma_beta = 0.25 +adv_filter_threshold_scale = 0.01 +checkpoint_interval = 50 +log_interval = 10 +; Rendering options +render = False +render_interval = 1000 +; If True, show exactly what the agent sees in agent observation +obs_only = True +; Show grid lines +show_grid = False +; Draws lines from ego agent observed ORUs and road elements to show detection range +show_lasers = False +; Display human xy logs in the background +show_human_logs = True +; Options: List[str to path], str to path (e.g., "resources/drive/training/binaries/map_001.bin"), None +render_map = none [eval] -; Eval frequency in epochs -eval_interval = 500 +; Set to True to enable periodic multi-scenario evaluation during training +multi_scenario_eval = False +; Frequency of evaluation during training (in epochs) +eval_interval = 25 +num_agents = 512 +; Batch size for eval_multi_scenarios (number of scenarios per batch) ; Path to dataset used for evaluation -map_dir = "resources/drive/binaries/training" -; Number of agents to evaluate -num_eval_agents = 64 -; If True, enable self-play evaluation (pair policy-controlled agent with a copy of itself) -self_play_eval = True -; If True, enable human replay evaluation (pair policy-controlled agent with human replays) -human_replay_eval = False -; Which env to render during eval. Options: "first" (by index), "worst_collision", "random" -render_select_mode = "first" -; If True, render random scenarios. Note: Doing this frequency will slow down the training. -render_human_replay_eval = False -render_self_play_eval = True -; Number of scenarios to process per batch -wosac_batch_size = 32 -; Target number of unique scenarios perform evaluation in -wosac_target_scenarios = 64 -; Total pool of scenarios to sample from -wosac_scenario_pool_size = 1000 -; Max batches, used as a timeout to prevent an infinite loop -wosac_max_batches = 100 +map_dir = "pufferlib/resources/drive/binaries/eval" +; Simulation mode for evaluation: "gigaflow" or "replay" +multi_scenario_simulation_mode = "replay" +; Total number of scenarios to evaluate +multi_scenario_num_scenarios = 250 backend = PufferEnv ; WOSAC (Waymo Open Sim Agents Challenge) evaluation settings ; If True, enables evaluation on realism metrics each time we save a checkpoint wosac_realism_eval = False -; Number of policy rollouts per scene -wosac_num_rollouts = 32 -; When to start the simulation -wosac_init_steps = 10 -; Control everything valid at init in the scene -wosac_control_mode = "control_wosac" -; Create everything in valid at init the scene -wosac_init_mode = "create_all_valid" -; Stop when reaching the goal -wosac_goal_behavior = 2 -; Can shrink goal radius for WOSAC evaluation -wosac_goal_radius = 2.0 +wosac_num_rollouts = 32 ; Number of policy rollouts per scene +wosac_init_steps = 10 ; When to start the simulation +wosac_num_agents = 256 ; Total number of WOSAC agents to evaluate +wosac_control_mode = "control_wosac" ; Control the tracks to predict +wosac_init_mode = "create_all_valid" ; Initialize from the tracks to predict +wosac_goal_radius = 2.0 ; Can shrink goal radius for WOSAC evaluation wosac_sanity_check = False -; Only return aggregate results across all scenes -wosac_aggregate_results = True -; Evaluation mode: "policy", "ground_truth" -wosac_eval_mode = "policy" - -[sweep.train.learning_rate] -distribution = log_normal -min = 0.001 -mean = 0.003 -max = 0.005 -scale = auto - -[sweep.train.ent_coef] -distribution = log_normal -min = 0.001 -mean = 0.005 -max = 0.03 -scale = auto - -[sweep.train.gamma] -distribution = log_normal -min = 0.97 -mean = 0.98 -max = 0.999 -scale = auto - -[sweep.train.gae_lambda] -distribution = log_normal -min = 0.95 -mean = 0.98 -max = 0.999 -scale = auto - -[controlled_exp.train.goal_speed] -values = [10, 20, 30, 3] +wosac_aggregate_results = True ; Only return aggregate results across all scenes +; If True, enable human replay evaluation (pair policy-controlled agent with human replays) +human_replay_eval = False +human_replay_control_mode = "control_sdc_only" ; Control only the self-driving car +human_replay_num_agents = 64 ; This equals the number of scenarios, since we control one agent in each -[controlled_exp.train.ent_coef] -values = [0.001, 0.005, 0.01] +; [sweep.train.learning_rate] +; distribution = log_normal +; min = 0.0001 +; mean = 0.001 +; max = 0.002 +; scale = auto + +; [sweep.train.ent_coef] +; distribution = log_normal +; min = 0.0005 +; mean = 0.01 +; max = 0.02 +; scale = auto + +; [sweep.train.vf_clip_coef] +; distribution = log_normal +; min = 0.1 +; mean = 0.2 +; max = 3 +; scale = auto + +; [sweep.env.inactive_agent_threshold] +; distribution = log_normal +; min = 0.1 +; mean = 0.4 +; max = 1.0 +; scale = auto + +; [sweep.train.gamma] +; distribution = logit_normal +; min = 0.90 +; mean = 0.99 +; max = 0.9999 +; scale = auto + +; [sweep.train.max_grad_norm] +; distribution = uniform +; min = 0.2 +; mean = 0.5 +; max = 2.0 +; scale = auto + +; [sweep.train.update_epochs] +; distribution = int_uniform +; min = 1 +; mean = 1 +; max = 6 +; scale = auto + +; [sweep.train.vf_coef] +; distribution = uniform +; min = 0.2 +; mean = 0.5 +; max = 3.0 +; scale = auto + +; [sweep.train.vtrace_rho_clip] +; distribution = uniform +; min = 0.0 +; max = 5.0 +; mean = 1.0 +; scale = auto + +; [sweep.train.vtrace_c_clip] +; distribution = uniform +; min = 0.0 +; max = 5.0 +; mean = 1.0 +; scale = auto + +; [sweep.train.clip_coef] +; distribution = uniform +; min = 0.01 +; max = 1.0 +; mean = 0.2 +; scale = auto -[controlled_exp.train.seed] -values = [42, 55, 1] +; [sweep.train.gae_lambda] +; distribution = logit_normal +; min = 0.8 +; mean = 0.95 +; max = 0.995 +; scale = auto + +; [sweep.policy.actor_num_layers] +; distribution = int_uniform +; min = 1 +; mean = 1 +; max = 2 +; scale = auto + +; [sweep.policy.critic_num_layers] +; distribution = int_uniform +; min = 1 +; mean = 1 +; max = 2 +; scale = auto + +; [sweep.env.reward_goal] +; distribution = uniform +; min = 0.0 +; mean = 0.5 +; max = 1.0 +; scale = auto + +; [sweep.env.reward_under_speed] +; distribution = uniform +; min = 0.0 +; mean = 0.05 +; max = 0.2 +; scale = auto + +; [sweep.env.reward_comfort] +; distribution = uniform +; min = 0.0 +; mean = 0.5 +; max = 1.0 +; scale = auto + +; [sweep.env.reward_lane_align] +; distribution = uniform +; min = 0.0 +; mean = 0.5 +; max = 1.0 +; scale = auto + +; [sweep.env.reward_lane_center] +; distribution = uniform +; min = 0.0 +; mean = 0.5 +; max = 1.0 +; scale = auto + +; [sweep.env.reward_velocity] +; distribution = uniform +; min = 0.0 +; mean = 0.5 +; max = 1.0 +; scale = auto + +; [sweep.train.adam_beta1] +; distribution = logit_normal +; min = 0.7 +; mean = 0.9 +; max = 0.999 +; scale = auto + +; [sweep.train.adam_beta2] +; distribution = logit_normal +; min = 0.99 +; mean = 0.999 +; max = 0.9999 +; scale = auto + +; [sweep.train.prio_alpha] +; distribution = logit_normal +; min = 0.7 +; mean = 0.85 +; max = 0.99 +; scale = auto + +; [sweep.train.prio_beta0] +; distribution = logit_normal +; min = 0.7 +; mean = 0.85 +; max = 0.99 +; scale = auto + +[controlled_exp.train.learning_rate] +values = [0.001, 0.003, 0.01] + +[controlled_exp.train.ent_coef] +values = [0.01, 0.005] diff --git a/pufferlib/ocean/benchmark/README.md b/pufferlib/ocean/benchmark/README.md new file mode 100644 index 0000000000..7bae42c2e8 --- /dev/null +++ b/pufferlib/ocean/benchmark/README.md @@ -0,0 +1,67 @@ +# Waymo Open Sim Agent Challenge (WOSAC) benchmark + +## Usage + +WOSAC evaluation with random policy +```bash +puffer eval puffer_drive --eval.wosac-realism-eval True +``` + +WOSAC evaluation with your checkpoint +```bash +puffer eval puffer_drive --eval.wosac-realism-eval True --load-model-path .pt +``` + +## Links + +- [Challenge and leaderboard](https://waymo.com/open/challenges/2025/sim-agents/) +- [Sim agent challenge tutorial](https://github.com/waymo-research/waymo-open-dataset/blob/master/tutorial/tutorial_sim_agents.ipynb) +- [Reference paper introducing WOSAC](https://arxiv.org/pdf/2305.12032) +- [Metrics entry point](https://github.com/waymo-research/waymo-open-dataset/blob/master/src/waymo_open_dataset/wdl_limited/sim_agents_metrics/metrics.py) +- [Log-likelihood estimators](https://github.com/waymo-research/waymo-open-dataset/blob/master/src/waymo_open_dataset/wdl_limited/sim_agents_metrics/estimators.py) +- Configurations [proto file](https://github.com/waymo-research/waymo-open-dataset/blob/99a4cb3ff07e2fe06c2ce73da001f850f628e45a/src/waymo_open_dataset/protos/sim_agents_metrics.proto#L51) [default sim agent challenge configs](https://github.com/waymo-research/waymo-open-dataset/blob/master/src/waymo_open_dataset/wdl_limited/sim_agents_metrics/challenge_2025_sim_agents_config.textproto) + + +## Implementation + +- For the sim agent challenge we compute the log likelihood with `aggregate_objects=False`, which means that we use [`_log_likelihood_estimate_timeseries_agent_level()`](https://github.com/waymo-research/waymo-open-dataset/blob/99a4cb3ff07e2fe06c2ce73da001f850f628e45a/src/waymo_open_dataset/wdl_limited/sim_agents_metrics/estimators.py#L17) +- As such, the interpretation is as follows: + +Steps [for every scene]: +1. Rollout policy in environment K times → (n_agents, n_rollouts, n_steps) +2. Obtain log data → (n_agents, 1, n_steps) +3. Obtain features from (x, y, z, heading tuples) +4. Compute log-likelihood metrics from features + - a. Flatten across time (assume independence) → (n_agents, n_rollouts * n_steps) + - b. Use the per-agent simulated features to construct a probability distribution + - c. Take the per-agent ground-truth values and find the bin that is closed for each + - d. Take log of the probability for each bin → (n_agents, n_steps) +5. Likelihood score is exp(sum(log_probs)/n_steps) → (n_agents, 1) \in [0, 1] + +## Notes + +- Currently, only kinematics realism score is implemented. Next steps would be to add the interactive realism score, and the map realism score: + + - Interactive realism score: requires grouping agents per scenario, and computing pairwise distances between agents over time. + - Map realism score: requires access to the map and computing offroad status. + + Those two scores might require heavy computations, so we will consider reimplementic all the metrics in torch. + +- About the time-independence assumption: + + 1. This is the assumption used in the official WOSAC evaluation, their argument is that it would give more flexibility to the sim agents models: + + > Given the time series nature of simulation data, two choices emerge for how to treat samples over multiple timesteps for a given object for a given run segment: to treat them as time-independent or time-dependent samples. In the latter case, users would be expected to not only reconstruct the general behaviors present in the logged data in one rollout, but also recreate those behaviors over the exact same time intervals. To allow more flexibility in agent behavior, we use the former formulation when computing NLLs, defining each component metric m as an average (in log-space) over the time-axis, masked by validity. + + 2. However this will lead to the score of a perfect logged oracle being inferior to 1.0, and makes it less interpretable. Here are the scores of a logged oracle using the time-independence assumption (setup: 1024 agents, 48 rollouts): + + ``` + Linear speed: 0.5640 + Linear acceleration: 0.4658 + Angular speed: 0.5543 + Angular acceleration: 0.6589 + Kinematics realism score: 0.5607 + ``` + These scores go to 1.0 if we use the time-dependent estimator, execpt for the smoothing factor that is used to avoid bins with 0 probability. + + Using the time-dependent estimator means generating n_steps histograms per agent, using num_rollouts samples per histogram, while time-independence means generating one histogram per agent using n_rollouts * n_steps samples. With the speed of PufferDrive, we might be able to increase n_rollouts to have more samples per histogram. diff --git a/pufferlib/ocean/benchmark/evaluator.py b/pufferlib/ocean/benchmark/evaluator.py index e1b61a731a..383384e623 100644 --- a/pufferlib/ocean/benchmark/evaluator.py +++ b/pufferlib/ocean/benchmark/evaluator.py @@ -1,17 +1,14 @@ """WOSAC evaluation class for PufferDrive.""" -import copy import torch import numpy as np import pandas as pd from typing import Dict import matplotlib.pyplot as plt -from tqdm import tqdm import configparser import os -import pufferlib -# WOSAC eval +import pufferlib from pufferlib.ocean.benchmark import metrics from pufferlib.ocean.benchmark import estimators @@ -39,106 +36,11 @@ def __init__(self, config: Dict): self.sim_steps = self.num_steps - self.init_steps self.num_rollouts = config.get("eval", {}).get("wosac_num_rollouts", 32) self.device = config.get("train", {}).get("device", "cuda") - self.eval_mode = config.get("eval", {}).get("wosac_eval_mode", "policy") wosac_metrics_path = os.path.join(os.path.dirname(__file__), "wosac.ini") self.metrics_config = configparser.ConfigParser() self.metrics_config.read(wosac_metrics_path) - def evaluate(self, args, vecenv, policy=None, drop_scene_duplicates=True): - """Run full WOSAC evaluation with batched iteration over target scenarios. - - Args: - args: Configuration dictionary - vecenv: Vectorized environment - policy: Policy to evaluate - drop_scene_duplicates: Whether to drop duplicate scenarios - - Returns: - DataFrame: Full results aggregated by scenario. - """ - num_target_maps = args["eval"]["wosac_target_scenarios"] - max_batches = args["eval"].get("wosac_max_batches", 100) - - unique_files_sampled = set() - combined_results = [] - - with tqdm(total=100, desc="Processing batches", unit="%", colour="cyan") as pbar: - batch_idx = 0 - while batch_idx < max_batches: - # Resample maps for each batch (except first) - if batch_idx > 0: - vecenv.driver_env.resample_maps() - - # Obtain ground truth trajectories - gt_trajectories = self.collect_ground_truth_trajectories(vecenv) - - # Collect simulated trajectories - if policy is not None and self.eval_mode == "policy": - simulated_trajectories = self.collect_simulated_trajectories(args, vecenv, policy) - elif self.eval_mode == "ground_truth": - # Create fake simulated trajectories by repeating ground truth - simulated_trajectories = gt_trajectories.copy() - for key in ["x", "y", "heading", "id"]: - simulated_trajectories[key] = np.repeat( - gt_trajectories[key], args["eval"]["wosac_num_rollouts"], axis=1 - ) - simulated_trajectories["id"] = simulated_trajectories["id"][..., np.newaxis] - else: - raise ValueError(f"Policy is None or unknown evaluation mode: {self.eval_mode}") - - # Compute metrics for this batch - agent_state = vecenv.driver_env.get_global_agent_state() - road_edge_polylines = vecenv.driver_env.get_road_edge_polylines() - batch_results = self.compute_metrics( - gt_trajectories, - simulated_trajectories, - agent_state, - road_edge_polylines, - aggregate_results=False, - ) - - # Optional: sanity check on first batch - if args["eval"].get("wosac_sanity_check", False) and batch_idx == 0: - self._quick_sanity_check(gt_trajectories, simulated_trajectories) - - # Track coverage - unique_files_sampled.update(str(s) for s in np.unique(gt_trajectories["scenario_id"])) - combined_results.append(batch_results) - - # Update progress - coverage = len(unique_files_sampled) / num_target_maps - pbar.n = int(coverage * 100) - pbar.set_postfix({"n": len(unique_files_sampled), "batch": batch_idx + 1}) - pbar.refresh() - - batch_idx += 1 - - # Stop if we've covered all target scenarios - if len(unique_files_sampled) >= num_target_maps: - break - - # Check if we didn't reach target coverage - if len(unique_files_sampled) < num_target_maps: - print( - f"\nWarning: Only covered {len(unique_files_sampled)}/{num_target_maps} scenarios after {batch_idx} batches" - ) - - # Combine batch results into single dataframe - df_combined = pd.concat(combined_results) - - # Optionally drop duplicate scenarios (keep first occurrence) - if drop_scene_duplicates: - initial_count = len(df_combined) - df_combined = df_combined[~df_combined.index.duplicated(keep="first")] - dropped = initial_count - len(df_combined) - if dropped > 0: - print(f"\nDropped {dropped} duplicate scenarios.") - - print(f"\nCollected {len(df_combined)} agent records from {batch_idx} batches") - - return df_combined - def _compute_metametric(self, metrics: pd.Series) -> float: metametric = 0.0 for field_name in _METRIC_FIELD_NAMES: @@ -146,7 +48,9 @@ def _compute_metametric(self, metrics: pd.Series) -> float: weight = self.metrics_config.getfloat(field_name, "metametric_weight") metric_score = metrics[likelihood_field_name] metametric += weight * metric_score - return metametric + + weight_sum = sum(self.metrics_config.getfloat(fn, "metametric_weight") for fn in _METRIC_FIELD_NAMES) + return metametric / weight_sum def _get_histogram_params(self, metric_name: str): return ( @@ -217,53 +121,6 @@ def collect_simulated_trajectories(self, args, puffer_env, policy): return trajectories - def collect_wosac_random_baseline(self, puffer_env): - """ - Random Baseline from Wosac 2023 paper - """ - driver = puffer_env.driver_env - num_agents = puffer_env.observation_space.shape[0] - - trajectories = { - "x": np.zeros((num_agents, self.num_rollouts, self.sim_steps), dtype=np.float32), - "y": np.zeros((num_agents, self.num_rollouts, self.sim_steps), dtype=np.float32), - "heading": np.zeros((num_agents, self.num_rollouts, self.sim_steps), dtype=np.float32), - "id": np.zeros((num_agents, self.num_rollouts, self.sim_steps), dtype=np.int32), - } - - for rollout_idx in range(self.num_rollouts): - obs, info = puffer_env.reset() - - # Do Initialization - agent_state = driver.get_global_agent_state() - trajectories["x"][:, rollout_idx, 0] = agent_state["x"] - trajectories["y"][:, rollout_idx, 0] = agent_state["y"] - trajectories["heading"][:, rollout_idx, 0] = agent_state["heading"] - trajectories["id"][:, rollout_idx, 0] = agent_state["id"] - - # Update using Gaussian: - samples = np.random.normal(loc=1, scale=0.1, size=(num_agents, self.sim_steps, 3)) - for time_idx in range(1, self.sim_steps): - dx, dy, d_heading = samples[:, time_idx, 0], samples[:, time_idx, 1], samples[:, time_idx, 2] - x, y, heading = ( - trajectories["x"][:, rollout_idx, time_idx - 1], - trajectories["y"][:, rollout_idx, time_idx - 1], - trajectories["heading"][:, rollout_idx, time_idx - 1], - ) - - cos_h = np.cos(heading) - sin_h = np.sin(heading) - - x += dx * cos_h - dy * sin_h - y += dx * sin_h + dy * cos_h - heading += d_heading - - trajectories["x"][:, rollout_idx, time_idx] = x - trajectories["y"][:, rollout_idx, time_idx] = y - trajectories["heading"][:, rollout_idx, time_idx] = heading - - return trajectories - def compute_metrics( self, ground_truth_trajectories: Dict, @@ -290,7 +147,7 @@ def compute_metrics( "Agent IDs don't match between simulated and ground truth trajectories" ) - eval_mask = ground_truth_trajectories["is_track_to_predict"][:, 0] + eval_mask = ground_truth_trajectories["id"][:, 0] >= 0 # Extract trajectories sim_x = simulated_trajectories["x"] @@ -302,11 +159,8 @@ def compute_metrics( ref_valid = ground_truth_trajectories["valid"] agent_length = agent_state["length"] agent_width = agent_state["width"] - is_vehicle = ground_truth_trajectories["is_vehicle"] scenario_ids = ground_truth_trajectories["scenario_id"] - last_scenario_id = str(scenario_ids[-1][0]) - # We evaluate the metrics only for the Tracks to Predict. eval_sim_x = sim_x[eval_mask] eval_sim_y = sim_y[eval_mask] @@ -318,7 +172,6 @@ def compute_metrics( eval_agent_length = agent_length[eval_mask] eval_agent_width = agent_width[eval_mask] eval_scenario_ids = scenario_ids[eval_mask] - eval_is_vehicle = is_vehicle[eval_mask] # Compute features # Kinematics-related features @@ -484,48 +337,60 @@ def compute_metrics( sanity_check=False, ) - speed_log_likelihood = metrics._reduce_average_with_validity( - linear_speed_log_likelihood, - speed_validity[:, 0, :], - axis=1, + speed_likelihood = np.exp( + metrics._reduce_average_with_validity( + linear_speed_log_likelihood, + speed_validity[:, 0, :], + axis=1, + ) ) - accel_log_likelihood = metrics._reduce_average_with_validity( - linear_accel_log_likelihood, - acceleration_validity[:, 0, :], - axis=1, + accel_likelihood = np.exp( + metrics._reduce_average_with_validity( + linear_accel_log_likelihood, + acceleration_validity[:, 0, :], + axis=1, + ) ) - angular_speed_log_likelihood = metrics._reduce_average_with_validity( - angular_speed_log_likelihood, - speed_validity[:, 0, :], - axis=1, + angular_speed_likelihood = np.exp( + metrics._reduce_average_with_validity( + angular_speed_log_likelihood, + speed_validity[:, 0, :], + axis=1, + ) ) - angular_accel_log_likelihood = metrics._reduce_average_with_validity( - angular_accel_log_likelihood, - acceleration_validity[:, 0, :], - axis=1, + angular_accel_likelihood = np.exp( + metrics._reduce_average_with_validity( + angular_accel_log_likelihood, + acceleration_validity[:, 0, :], + axis=1, + ) ) - distance_to_nearest_object_log_likelihood = metrics._reduce_average_with_validity( - distance_to_nearest_object_log_likelihood, - eval_ref_valid[:, 0, :], - axis=1, + distance_to_nearest_object_likelihood = np.exp( + metrics._reduce_average_with_validity( + distance_to_nearest_object_log_likelihood, + eval_ref_valid[:, 0, :], + axis=1, + ) ) - # TTC is computed only for vehicles - ttc_valid = eval_ref_valid & eval_is_vehicle[..., None] - time_to_collision_log_likelihood = metrics._reduce_average_with_validity( - time_to_collision_log_likelihood, - ttc_valid[:, 0, :], - axis=1, + time_to_collision_likelihood = np.exp( + metrics._reduce_average_with_validity( + time_to_collision_log_likelihood, + eval_ref_valid[:, 0, :], + axis=1, + ) ) - distance_to_road_edge_log_likelihood = metrics._reduce_average_with_validity( - distance_to_road_edge_log_likelihood, - eval_ref_valid[:, 0, :], - axis=1, + distance_to_road_edge_likelihood = np.exp( + metrics._reduce_average_with_validity( + distance_to_road_edge_log_likelihood, + eval_ref_valid[:, 0, :], + axis=1, + ) ) # Collision likelihood is computed by aggregating in time. For invalid objects @@ -535,8 +400,8 @@ def compute_metrics( sim_collision_indication = np.any(np.where(eval_ref_valid, sim_collision_per_step, False), axis=2) ref_collision_indication = np.any(np.where(eval_ref_valid, ref_collision_per_step, False), axis=2) - sim_num_collisions = np.mean(sim_collision_indication, axis=1) - ref_num_collisions = np.mean(ref_collision_indication, axis=1) + sim_num_collisions = np.sum(sim_collision_indication, axis=1) + ref_num_collisions = np.sum(ref_collision_indication, axis=1) collision_log_likelihood = estimators.log_likelihood_estimate_scenario_level( log_values=ref_collision_indication[:, 0], @@ -546,13 +411,14 @@ def compute_metrics( num_bins=2, use_bernoulli=True, ) + collision_likelihood = np.exp(collision_log_likelihood) # Offroad likelihood (same pattern as collision) sim_offroad_indication = np.any(np.where(eval_ref_valid, sim_offroad_per_step, False), axis=2) ref_offroad_indication = np.any(np.where(eval_ref_valid, ref_offroad_per_step, False), axis=2) - sim_num_offroad = np.mean(sim_offroad_indication, axis=1) - ref_num_offroad = np.mean(ref_offroad_indication, axis=1) + sim_num_offroad = np.sum(sim_offroad_indication, axis=1) + ref_num_offroad = np.sum(ref_offroad_indication, axis=1) offroad_log_likelihood = estimators.log_likelihood_estimate_scenario_level( log_values=ref_offroad_indication[:, 0], @@ -562,6 +428,7 @@ def compute_metrics( num_bins=2, use_bernoulli=True, ) + offroad_likelihood = np.exp(offroad_log_likelihood) # Get agent IDs eval_agent_ids = ground_truth_trajectories["id"][eval_mask] @@ -576,70 +443,60 @@ def compute_metrics( "num_offroad_ref": ref_num_offroad.flatten(), "ade": ade, "min_ade": min_ade, - "likelihood_linear_speed": speed_log_likelihood, - "likelihood_linear_acceleration": accel_log_likelihood, - "likelihood_angular_speed": angular_speed_log_likelihood, - "likelihood_angular_acceleration": angular_accel_log_likelihood, - "likelihood_distance_to_nearest_object": distance_to_nearest_object_log_likelihood, - "likelihood_time_to_collision": time_to_collision_log_likelihood, - "likelihood_collision_indication": collision_log_likelihood, - "likelihood_distance_to_road_edge": distance_to_road_edge_log_likelihood, - "likelihood_offroad_indication": offroad_log_likelihood, + "likelihood_linear_speed": speed_likelihood, + "likelihood_linear_acceleration": accel_likelihood, + "likelihood_angular_speed": angular_speed_likelihood, + "likelihood_angular_acceleration": angular_accel_likelihood, + "likelihood_distance_to_nearest_object": distance_to_nearest_object_likelihood, + "likelihood_time_to_collision": time_to_collision_likelihood, + "likelihood_collision_indication": collision_likelihood, + "likelihood_distance_to_road_edge": distance_to_road_edge_likelihood, + "likelihood_offroad_indication": offroad_likelihood, } ) - # Aggregate along agent dimenision: Obtain one score per scenario - df_scene_level = df.groupby("scenario_id", as_index=True).mean().drop(columns=["agent_id"]).dropna() - - # Exponentiate the averaged log-likelihoods to get final likelihoods - likelihood_columns = [col for col in df_scene_level.columns if col.startswith("likelihood_")] - df_scene_level[likelihood_columns] = np.exp(df_scene_level[likelihood_columns]) - - df_scene_level["realism_meta_score"] = df_scene_level.apply(self._compute_metametric, axis=1) - df_scene_level["num_agents_per_scene"] = df.groupby("scenario_id").size() - df_scene_level = df_scene_level.round(3) - - # Get group summary metrics - kinematic_metrics = np.mean( + scene_level_results = df.groupby("scenario_id")[ [ - df_scene_level["likelihood_linear_speed"], - df_scene_level["likelihood_linear_acceleration"], - df_scene_level["likelihood_angular_speed"], - df_scene_level["likelihood_angular_acceleration"], + "ade", + "min_ade", + "num_collisions_sim", + "num_collisions_ref", + "num_offroad_sim", + "num_offroad_ref", + "likelihood_linear_speed", + "likelihood_linear_acceleration", + "likelihood_angular_speed", + "likelihood_angular_acceleration", + "likelihood_distance_to_nearest_object", + "likelihood_time_to_collision", + "likelihood_collision_indication", + "likelihood_distance_to_road_edge", + "likelihood_offroad_indication", ] - ) + ].mean() - interactive_metrics = np.mean( - [ - df_scene_level["likelihood_collision_indication"], - df_scene_level["likelihood_distance_to_nearest_object"], - df_scene_level["likelihood_time_to_collision"], - ] - ) - - map_metrics = np.mean( - [ - df_scene_level["likelihood_distance_to_road_edge"], - df_scene_level["likelihood_offroad_indication"], - ] - ) - - df_scene_level["kinematic_metrics"] = kinematic_metrics - df_scene_level["interactive_metrics"] = interactive_metrics - df_scene_level["map_based_metrics"] = map_metrics - - # Safety: drop the last scenario (potentially incomplete) from the scene-level results - if last_scenario_id in df_scene_level.index: - df_scene_level = df_scene_level.drop(last_scenario_id) + scene_level_results["realism_meta_score"] = scene_level_results.apply(self._compute_metametric, axis=1) + scene_level_results["num_agents"] = df.groupby("scenario_id").size() + scene_level_results = scene_level_results[ + ["num_agents"] + [col for col in scene_level_results.columns if col != "num_agents"] + ] if aggregate_results: - # Aggregate over scenarios - aggregate_metrics = df_scene_level.mean().to_dict() - aggregate_metrics["total_num_agents"] = df_scene_level["num_agents_per_scene"].sum() - aggregate_metrics["realism_score_std"] = df_scene_level["realism_meta_score"].std() - return aggregate_metrics + aggregate_metrics = scene_level_results.mean().to_dict() + aggregate_metrics["total_num_agents"] = scene_level_results["num_agents"].sum() + # Convert numpy types to Python native types + return {k: v.item() if hasattr(v, "item") else v for k, v in aggregate_metrics.items()} else: - return df_scene_level + print("\n Scene-level results:\n") + print(scene_level_results) + + print(f"\n Overall realism meta score: {scene_level_results['realism_meta_score'].mean():.4f}") + print(f"\n Overall minADE: {scene_level_results['min_ade'].mean():.4f}") + print(f"\n Overall ADE: {scene_level_results['ade'].mean():.4f}") + + # print(f"\n Full agent-level results:\n") + # print(df) + return scene_level_results def _quick_sanity_check(self, gt_trajectories, simulated_trajectories, agent_idx=None, max_agents_to_plot=10): if agent_idx is None: @@ -763,183 +620,58 @@ def _quick_sanity_check(self, gt_trajectories, simulated_trajectories, agent_idx plt.savefig(f"trajectory_comparison_agent_{agent_idx}.png") -class Evaluator: - """Evaluates policies in self_play or human_replay mode, with optional rendering. - - Initializes the eval envs needed based on eval config flags: - - human_replay_eval: creates sp_env + hr_env - - render_eval: creates sp_env (if not already created) - """ - - RENDER_FIRST = "first" - RENDER_RANDOM = "random" - RENDER_WORST_SCORE = "worst_score" - RENDER_WORST_COLLISION = "worst_collision" - - def __init__(self, configs, logger=None): - self.configs = configs - self.logger = logger - self.sim_steps = 90 - self.self_play_stats = None - self.human_replay_stats = None - self.sp_env = None - self.hr_env = None - - self._unpack_eval_configs(configs) - - def _unpack_eval_configs(self, configs): - eval_config = copy.deepcopy(configs) - # Create separate evaluation environments based on specified configs - eval_config["env"]["termination_mode"] = 0 - backend = eval_config["eval"].get("backend", "PufferEnv") - eval_config["env"]["map_dir"] = eval_config["eval"]["map_dir"] - eval_config["env"]["num_agents"] = eval_config["eval"]["num_eval_agents"] - eval_config["env"]["episode_length"] = 91 # WOMD scenario length - eval_config["vec"] = dict(backend=backend, num_envs=1) - - self.hr_eval_config = copy.deepcopy(eval_config) - self.hr_eval_config["env"]["control_mode"] = "control_sdc_only" - self.sp_eval_config = copy.deepcopy(eval_config) - self.sp_eval_config["env"]["control_mode"] = "control_agents" - self.render_select_mode = self.configs["eval"]["render_select_mode"] - self.render_sp_rollout = self.configs["eval"]["render_self_play_eval"] - self.render_hr_rollout = self.configs["eval"]["render_human_replay_eval"] - - def select_render_env(self, env_logs): - """Select which environment to render based on per-env rollout statistics. - Args: - env_logs: List of dicts, one per environment. Each dict contains - aggregated agent statistics (score, collision_rate, offroad_rate, etc.) - with 'n' being the number of controlled agents in that env. - Empty dicts indicate no data was collected for that env. +class HumanReplayEvaluator: + """Evaluates policies against human replays in PufferDrive.""" - Returns: - int: Index of the environment to render. - """ - mode = self.render_select_mode - if mode == self.RENDER_FIRST: - return 0 - if mode == self.RENDER_RANDOM: - return np.random.randint(len(env_logs)) - - populated = [(i, log) for i, log in enumerate(env_logs) if log] - - if not populated: - return 0 - - if mode == self.RENDER_WORST_SCORE: - return min(populated, key=lambda x: x[1].get("score", 1.0))[0] - elif mode == self.RENDER_WORST_COLLISION: - return max(populated, key=lambda x: x[1].get("collision_rate", 0.0))[0] - # Add other modes based on desiderata here - return 0 - - def rollout(self, policy, mode="self_play"): - env = self.hr_env if mode == "human_replay" else self.sp_env - render_eval = self.render_sp_rollout if mode == "self_play" else self.render_hr_rollout - driver = env.driver_env - - needs_stats_first = render_eval and self.render_select_mode not in (self.RENDER_FIRST, self.RENDER_RANDOM) - - if needs_stats_first: - env_logs = self._run_rollout(policy, env, per_env_logs=True) - render_env_idx = self.select_render_env(env_logs) - else: - render_env_idx = self.select_render_env([{}] * driver.num_envs) + def __init__(self, config: Dict): + self.config = config + self.sim_steps = 91 - self.config["env"]["init_steps"] - info_list = self._run_rollout(policy, env, render_env_idx if render_eval else None) + def rollout(self, args, puffer_env, policy): + """Roll out policy in env with human replays. Store statistics. - final_info = info_list[0] if info_list else {} - if mode == "self_play": - self.self_play_stats = final_info - self.self_play_stats["render_env_idx"] = render_env_idx - elif mode == "human_replay": - self.human_replay_stats = final_info - self.human_replay_stats["render_env_idx"] = render_env_idx + In human replay mode, only the SDC (self-driving car) is controlled by the policy + while all other agents replay their human trajectories. This tests how compatible + the policy is with (static) human partners. - def _run_rollout(self, policy, env, render_env_idx=None, per_env_logs=False): - """Run a single rollout. If render_env_idx is not None, render that env.""" - driver = env.driver_env - num_agents = env.observation_space.shape[0] - device = self.configs["train"]["device"] + Args: + args: Config dict with train settings (device, use_rnn, etc.) + puffer_env: PufferLib environment wrapper + policy: Trained policy to evaluate - # Reset environment - obs, info = env.reset() + Returns: + dict: Aggregated metrics including: + - avg_collisions_per_agent: Average collisions per agent + - avg_offroad_per_agent: Average offroad events per agent + """ + import numpy as np + import torch + import pufferlib - # Initialize RNN state if needed + num_agents = puffer_env.observation_space.shape[0] + device = args["train"]["device"] + + obs, info = puffer_env.reset() state = {} - if self.configs["train"]["use_rnn"]: + if args["train"]["use_rnn"]: state = dict( lstm_h=torch.zeros(num_agents, policy.hidden_size, device=device), lstm_c=torch.zeros(num_agents, policy.hidden_size, device=device), ) - info_list = [] for time_idx in range(self.sim_steps): - if render_env_idx is not None: - driver.render(env_id=render_env_idx) - - # Get action from policy + # Step policy with torch.no_grad(): ob_tensor = torch.as_tensor(obs).to(device) logits, value = policy.forward_eval(ob_tensor, state) action, logprob, _ = pufferlib.pytorch.sample_logits(logits) - action_np = action.cpu().numpy().reshape(env.action_space.shape) + action_np = action.cpu().numpy().reshape(puffer_env.action_space.shape) - # Clip continuous actions to valid range if isinstance(logits, torch.distributions.Normal): - action_np = np.clip(action_np, env.action_space.low, env.action_space.high) - - # Step environment - obs, rewards, dones, truncs, info_list = env.step(action_np, per_env_logs=per_env_logs) + action_np = np.clip(action_np, puffer_env.action_space.low, puffer_env.action_space.high) - if truncs.all(): - break - - return info_list - - def log_videos(self, eval_mode, epoch): - """Log all mp4s in local path to wandb after env close has flushed ffmpeg pipes.""" - import os - import glob - - if not (self.logger and hasattr(self.logger, "wandb") and self.logger.wandb): - # Still clean up even if not logging - for p in glob.glob("*.mp4"): - os.remove(p) - return - - import wandb - - video_files = glob.glob("*.mp4") - if not video_files: - print("Warning: no render videos found in local path") - return - - render_mode = self.render_select_mode - for p in video_files: - scenario_id = os.path.splitext(os.path.basename(p))[0] - caption = f"scene_{scenario_id}_epoch_{epoch}_select_{render_mode}" - self.logger.wandb.log({f"render/{eval_mode}": wandb.Video(p, format="mp4", caption=caption)}) - - # Clean up - for p in video_files: - os.remove(p) - - def log_stats(self): - if not (self.logger and hasattr(self.logger, "wandb") and self.logger.wandb): - return - - eval_stats = {} - - if self.human_replay_stats is not None: - eval_stats["eval/hr_collision_rate"] = self.human_replay_stats["collision_rate"] - eval_stats["eval/hr_score"] = self.human_replay_stats["score"] - if self.self_play_stats is not None: - eval_stats["eval/sp_collision_rate"] = self.self_play_stats["collision_rate"] - eval_stats["eval/sp_score"] = self.self_play_stats["score"] - eval_stats["eval/num_agents"] = self.self_play_stats["n"] - else: - return + obs, rewards, dones, truncs, info_list = puffer_env.step(action_np) - self.logger.wandb.log(eval_stats) + if len(info_list) > 0: # Happens at the end of episode + results = info_list[0] + return results diff --git a/pufferlib/ocean/benchmark/metrics.py b/pufferlib/ocean/benchmark/metrics.py index 7f34da1c6c..cf1af358ee 100644 --- a/pufferlib/ocean/benchmark/metrics.py +++ b/pufferlib/ocean/benchmark/metrics.py @@ -249,7 +249,7 @@ def compute_interaction_features( scenario_mask = torch.as_tensor(scenario_mask_np, dtype=torch.bool, device=x_t.device) scenario_x = x_t[scenario_mask] scenario_y = y_t[scenario_mask] - episode_length = length_broadcast[scenario_mask] + scenario_length = length_broadcast[scenario_mask] scenario_width = width_broadcast[scenario_mask] scenario_heading = heading_t[scenario_mask] scenario_valid = valid_t[scenario_mask] @@ -260,7 +260,7 @@ def compute_interaction_features( distances_to_objects = interaction_features.compute_distance_to_nearest_object( center_x=scenario_x, center_y=scenario_y, - length=episode_length, + length=scenario_length, width=scenario_width, heading=scenario_heading, valid=scenario_valid, @@ -273,7 +273,7 @@ def compute_interaction_features( times_to_collision = interaction_features.compute_time_to_collision( center_x=scenario_x, center_y=scenario_y, - length=episode_length, + length=scenario_length, width=scenario_width, heading=scenario_heading, valid=scenario_valid, diff --git a/pufferlib/ocean/benchmark/metrics_sanity_check.py b/pufferlib/ocean/benchmark/metrics_sanity_check.py index 9cabc42377..dcda26b691 100644 --- a/pufferlib/ocean/benchmark/metrics_sanity_check.py +++ b/pufferlib/ocean/benchmark/metrics_sanity_check.py @@ -26,11 +26,11 @@ def replace_rollouts_with_gt(simulated_traj, gt_traj, num_replacements): return modified -def run_validation_experiment(config, vecenv): +def run_validation_experiment(config, vecenv, policy): evaluator = WOSACEvaluator(config) gt_trajectories = evaluator.collect_ground_truth_trajectories(vecenv) - simulated_trajectories = evaluator.collect_wosac_random_baseline(vecenv) + simulated_trajectories = evaluator.collect_simulated_trajectories(config, vecenv, policy) agent_state = vecenv.driver_env.get_global_agent_state() road_edge_polylines = vecenv.driver_env.get_road_edge_polylines() @@ -89,16 +89,17 @@ def main(): config["vec"]["num_envs"] = 1 config["eval"]["enabled"] = True config["eval"]["wosac_num_rollouts"] = 32 - config["env"]["map_dir"] = config["eval"]["map_dir"] + config["env"]["num_agents"] = config["eval"]["wosac_num_agents"] config["env"]["init_mode"] = config["eval"]["wosac_init_mode"] config["env"]["control_mode"] = config["eval"]["wosac_control_mode"] config["env"]["init_steps"] = config["eval"]["wosac_init_steps"] config["env"]["goal_behavior"] = config["eval"]["wosac_goal_behavior"] vecenv = load_env(args.env, config) + policy = load_policy(config, vecenv, args.env) - results = run_validation_experiment(config, vecenv) + results = run_validation_experiment(config, vecenv, policy) print("\n" + format_results_table(results)) diff --git a/pufferlib/ocean/benchmark/visual_sanity_check.py b/pufferlib/ocean/benchmark/visual_sanity_check.py index 33f9380e46..ca46574418 100644 --- a/pufferlib/ocean/benchmark/visual_sanity_check.py +++ b/pufferlib/ocean/benchmark/visual_sanity_check.py @@ -92,22 +92,17 @@ def main(): args = parser.parse_args() config = load_config(args.env) - - config["load_model_path"] = "resources/drive/pufferdrive_weights.pt" - config["vec"]["backend"] = "PufferEnv" config["vec"]["num_envs"] = 1 config["eval"]["enabled"] = True config["eval"]["wosac_num_rollouts"] = 1 + config["env"]["num_agents"] = config["eval"]["wosac_num_agents"] config["env"]["init_mode"] = config["eval"]["wosac_init_mode"] config["env"]["control_mode"] = config["eval"]["wosac_control_mode"] config["env"]["init_steps"] = config["eval"]["wosac_init_steps"] config["env"]["goal_behavior"] = config["eval"]["wosac_goal_behavior"] - config["env"]["map_dir"] = config["eval"]["map_dir"] - config["env"]["num_maps"] = config["eval"]["wosac_num_maps"] - vecenv = load_env(args.env, config) policy = load_policy(config, vecenv, args.env) diff --git a/pufferlib/ocean/benchmark/wosac.ini b/pufferlib/ocean/benchmark/wosac.ini index a63f11f98c..a7632e65d3 100644 --- a/pufferlib/ocean/benchmark/wosac.ini +++ b/pufferlib/ocean/benchmark/wosac.ini @@ -1,6 +1,5 @@ -# Taken from WOSAC 2024 Sim Agents Challenge configuration -# Note: 2024 challenge does not include traffic_light_violation metric (weight = 0.0) -# Traffic light violation was added in 2025 challenge with weight 0.05 +# Taken from WOSAC 2025 Sim Agents Challenge configuration +# Link: https://github.com/waymo-research/waymo-open-dataset/blob/master/src/waymo_open_dataset/wdl_limited/sim_agents_metrics/challenge_2025_sim_agents_config.textproto [linear_speed] histogram.min_val = 0.0 @@ -52,7 +51,7 @@ histogram.max_val = 40 histogram.num_bins = 10 histogram.additive_smoothing_pseudocount = 0.1 independent_timesteps = true -metametric_weight = 0.1 +metametric_weight = 0.05 [offroad_indication] bernoulli = true @@ -68,4 +67,4 @@ metametric_weight = 0.1 [traffic_light_violation] bernoulli = true -metametric_weight = 0.0 +metametric_weight = 0.05 diff --git a/pufferlib/ocean/drive/README.md b/pufferlib/ocean/drive/README.md new file mode 100644 index 0000000000..a012eddd33 --- /dev/null +++ b/pufferlib/ocean/drive/README.md @@ -0,0 +1,108 @@ +# PufferDrive + +This readme contains several important assumptions and definions about the `PufferDrive` environment. + +## Agent initialization and control + +### `init_mode` + +Determines which agents are **created** in the environment. + +| Option | Description | +| ------------------------ | ---------------------------------------------------------------------------- | +| `create_all_valid` | Create all entities valid at initialization (`traj_valid[init_steps] == 1`). | +| `create_only_controlled` | Create only those agents that are controlled by the policy. | + +### `control_mode` + +Determines which created agents are **controlled** by the policy. + +| Option | Description | +| ----------------------------------------- | ------------------------------------------------------------------------------------------------- | +| `control_vehicles` (default) | Control only valid **vehicles** (not experts, beyond `MIN_DISTANCE_TO_GOAL`, under `MAX_AGENTS`). | +| `control_agents` | Control all valid **agent types** (vehicles, cyclists, pedestrians). | +| `control_wosac` *(WOMD only)* | Control all agents with their valid flag to `True` at the `init_step`. | + + +## Termination conditions (`done`) + +Episodes are never truncated before reaching `episode_len`. The `reach_goal_behavior` argument controls agent behavior after reaching a goal early: + +* **`reach_goal_behavior=0` (default):** Agents respawn at their initial position after reaching their goal (last valid log position). +* **`reach_goal_behavior=1`:** Agents receive new goals indefinitely after reaching each goal. +* **`reach_goal_behavior=2`:** Agents stop after reaching their goal. + +## Logged performance metrics + +We record multiple performance metrics during training, aggregated over all *active agents* (alive and controlled). Key metrics include: + +- `score`: Goals reached cleanly (goal was achieved without collision or going off-road) +- `collision_rate`: Binary flag (0 or 1) if agent hit another vehicle. +- `offroad_rate`: Binary flag (0 or 1) if agent left road bounds. +- `completion_rate`: Whether the agent reached its goal in this episode (even if it collided or went off-road). + + +### Metric aggregation + +The `num_agents` parameter in `drive.ini` defines the total number of agents used to collect experience. +At runtime, **Puffer** uses `num_maps` to create enough environments to populate the buffer with `num_agents`, distributing them evenly across `num_envs`. + +Because agents are respawned immediately after reaching their goal, they remain active throughout the episode. + +At the end of each episode (i.e., when `timestep == TRAJECTORY_LENGTH`), metrics are logged once via: + +```C +if (env->timestep == TRAJECTORY_LENGTH) { + add_log(env); + c_reset(env); + return; +} +``` + +Metrics are normalized and aggregated in `vec_log` (`pufferlib/ocean/env_binding.h`). They are averaged over all active agents across all environments. For example, the aggregated collision rate is computed as: + +$$ +r^{agg}_{\text{collision}} = \frac{\mathbb{I}[\text{collided in episode}]}{N} +$$ + +where $N$ is the number of controlled agents. +This value represents the fraction of agents that collided at least once during the episode. So, cases **A** and **B** below would yield identical off-road and collision rates: + +![alt text](../../resources/drive/examples_a_b.png) + +Since these metrics do not capture *multiple* events per agent, we additionally log the **average number of collision and off-road events per episode**. This is computed as: + +$$ +c^{avg}_{\text{collision}} = \frac{\text{total number of collision events across all agents and environments}}{N} +$$ + +where $N$ is the total number of controlled agents. +For example, an `avg_collisions_per_agent` value of 4 indicates that, on average, each agent collides four times per episode. + +### Effect of respawning on metrics + +By default, agents are reset to their initial position when they reach their goal before the episode ends. Upon respawn, `respawn_timestep` is updated from `-1` to the current step index. + +This raises the question: **how does repeated respawning affect aggregated metrics?** + +To begin, note that the environment is a bit different before and after respawn. After an agent respawns, all other agents are "removed" from the environment. As a result, collisions with other agents cannot occur post-respawn. + +This effectively transforms the scenario into a single-agent environment, simplifying the task since the agent no longer needs to coordinate with others. + +![alt text](../../resources/drive/pre_and_post_respawn.png) + +#### `score` + +Consider an episode of 91 steps where an agent is initialized relatively close to the goal position and reaches its goal three times: + +1. **First attempt:** reaches the goal without collisions +2. **Second attempt:** reaches the goal without collisions +3. **Third attempt:** reaches the goal but goes off-road along the way + +![alt text](../../resources/drive/realistic_collision_event_post_respawn.png) + +The highlighted trajectory shows the first attempt. In this case, the recorded score is `0.0` — a single off-road event invalidates the score for the entire episode. This behavior is desired: the score metric is unforgiving. + +#### `offroad_rate` and `collision_rate` + +Same logic holds as above. diff --git a/pufferlib/ocean/drive/binding.c b/pufferlib/ocean/drive/binding.c index e86dad727a..109eee702b 100644 --- a/pufferlib/ocean/drive/binding.c +++ b/pufferlib/ocean/drive/binding.c @@ -2,6 +2,7 @@ #define Env Drive #define MY_SHARED #define MY_PUT +#define MY_GET #include "../env_binding.h" static int my_put(Env *env, PyObject *args, PyObject *kwargs) { @@ -67,81 +68,1655 @@ static int my_put(Env *env, PyObject *args, PyObject *kwargs) { return 0; } +static PyObject *my_get(PyObject *dict, Env *env) { + PyObject *v; + if (!env) { + PyErr_SetString(PyExc_ValueError, "env is NULL"); + return NULL; + } + + /* Validate main array pointers before accessing */ + if (env->num_total_agents > 0 && !env->agents) { + PyErr_SetString(PyExc_ValueError, "agents is NULL but count > 0"); + return NULL; + } + if (env->num_road_elements > 0 && !env->road_elements) { + PyErr_SetString(PyExc_ValueError, "road_elements is NULL but count > 0"); + return NULL; + } + if (env->num_traffic_elements > 0 && !env->traffic_elements) { + PyErr_SetString(PyExc_ValueError, "traffic_elements is NULL but count > 0"); + return NULL; + } + + v = PyLong_FromLong(env->active_agent_count); + if (!v) + return NULL; + if (PyDict_SetItemString(dict, "active_agent_count", v) < 0) { + Py_DECREF(v); + return NULL; + } + Py_DECREF(v); + + v = PyLong_FromLong(env->num_total_agents); + if (!v) + return NULL; + if (PyDict_SetItemString(dict, "num_total_agents", v) < 0) { + Py_DECREF(v); + return NULL; + } + Py_DECREF(v); + + v = PyLong_FromLong(env->num_road_elements); + if (!v) + return NULL; + if (PyDict_SetItemString(dict, "num_road_elements", v) < 0) { + Py_DECREF(v); + return NULL; + } + Py_DECREF(v); + + v = PyLong_FromLong(env->num_traffic_elements); + if (!v) + return NULL; + if (PyDict_SetItemString(dict, "num_traffic_elements", v) < 0) { + Py_DECREF(v); + return NULL; + } + Py_DECREF(v); + + /* Map name / string fields */ + if (env->map_name) { + PyObject *s = PyUnicode_FromString(env->map_name); + if (!s) + return NULL; + if (PyDict_SetItemString(dict, "map_name", s) < 0) { + Py_DECREF(s); + return NULL; + } + Py_DECREF(s); + } else { + if (PyDict_SetItemString(dict, "map_name", Py_None) < 0) + return NULL; + } + + /* Metadata fields */ + if (env->scenario_id[0] != '\0') { + PyObject *s = PyUnicode_FromString(env->scenario_id); + if (!s) + return NULL; + if (PyDict_SetItemString(dict, "scenario_id", s) < 0) { + Py_DECREF(s); + return NULL; + } + Py_DECREF(s); + } else { + if (PyDict_SetItemString(dict, "scenario_id", Py_None) < 0) + return NULL; + } + + if (env->dataset_name[0] != '\0') { + PyObject *s = PyUnicode_FromString(env->dataset_name); + if (!s) + return NULL; + if (PyDict_SetItemString(dict, "dataset_name", s) < 0) { + Py_DECREF(s); + return NULL; + } + Py_DECREF(s); + } else { + if (PyDict_SetItemString(dict, "dataset_name", Py_None) < 0) + return NULL; + } + + v = PyLong_FromLong(env->log_length); + if (!v) + return NULL; + if (PyDict_SetItemString(dict, "length", v) < 0) { + Py_DECREF(v); + return NULL; + } + Py_DECREF(v); + + v = PyLong_FromLong(env->dynamics_model); + if (!v) + return NULL; + if (PyDict_SetItemString(dict, "dynamics_model", v) < 0) { + Py_DECREF(v); + return NULL; + } + Py_DECREF(v); + + /* objects_of_interest array */ + if (env->objects_of_interest && env->num_objects_of_interest > 0) { + PyObject *lst = PyList_New(env->num_objects_of_interest); + if (!lst) + return NULL; + for (int i = 0; i < env->num_objects_of_interest; i++) { + PyObject *it = PyLong_FromLong(env->objects_of_interest[i]); + if (!it) { + Py_DECREF(lst); + return NULL; + } + PyList_SetItem(lst, i, it); + } + if (PyDict_SetItemString(dict, "objects_of_interest", lst) < 0) { + Py_DECREF(lst); + return NULL; + } + Py_DECREF(lst); + } else { + if (PyDict_SetItemString(dict, "objects_of_interest", Py_None) < 0) + return NULL; + } + + /* tracks_to_predict array */ + if (env->tracks_to_predict && env->num_tracks_to_predict > 0) { + PyObject *lst = PyList_New(env->num_tracks_to_predict); + if (!lst) + return NULL; + for (int i = 0; i < env->num_tracks_to_predict; i++) { + PyObject *it = PyLong_FromLong(env->tracks_to_predict[i]); + if (!it) { + Py_DECREF(lst); + return NULL; + } + PyList_SetItem(lst, i, it); + } + if (PyDict_SetItemString(dict, "tracks_to_predict", lst) < 0) { + Py_DECREF(lst); + return NULL; + } + Py_DECREF(lst); + } else { + if (PyDict_SetItemString(dict, "tracks_to_predict", Py_None) < 0) + return NULL; + } + + /* Lists (active agent indices) */ + if (env->active_agent_indices && env->active_agent_count > 0) { + PyObject *lst = PyList_New(env->active_agent_count); + if (!lst) + return NULL; + for (int i = 0; i < env->active_agent_count; i++) { + PyObject *it = PyLong_FromLong(env->active_agent_indices[i]); + if (!it) { + Py_DECREF(lst); + return NULL; + } + /* PyList_SetItem steals reference */ + PyList_SetItem(lst, i, it); + } + if (PyDict_SetItemString(dict, "active_agent_indices", lst) < 0) { + Py_DECREF(lst); + return NULL; + } + Py_DECREF(lst); + } else { + if (PyDict_SetItemString(dict, "active_agent_indices", Py_None) < 0) + return NULL; + } + + /* Optionally expose static car indices if present */ + if (env->static_agent_indices && env->static_agent_count > 0) { + PyObject *lst = PyList_New(env->static_agent_count); + if (!lst) + return NULL; + for (int i = 0; i < env->static_agent_count; i++) { + PyObject *it = PyLong_FromLong(env->static_agent_indices[i]); + if (!it) { + Py_DECREF(lst); + return NULL; + } + PyList_SetItem(lst, i, it); + } + if (PyDict_SetItemString(dict, "static_agent_indices", lst) < 0) { + Py_DECREF(lst); + return NULL; + } + Py_DECREF(lst); + } else { + if (PyDict_SetItemString(dict, "static_agent_indices", Py_None) < 0) + return NULL; + } + + /* Expose agents array as a list of dicts */ + if (env->agents && env->num_total_agents > 0) { + PyObject *agents_list = PyList_New(env->num_total_agents); + if (!agents_list) + return NULL; + for (int i = 0; i < env->num_total_agents; i++) { + Agent *a = &env->agents[i]; + + PyObject *agent = PyDict_New(); + if (!agent) { + Py_DECREF(agents_list); + return NULL; + } + + /* ID and type */ + PyObject *tmp = PyLong_FromLong(a->id); + if (!tmp) { + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + PyDict_SetItemString(agent, "id", tmp); + Py_DECREF(tmp); + + tmp = PyLong_FromLong(a->type); + if (!tmp) { + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + PyDict_SetItemString(agent, "type", tmp); + Py_DECREF(tmp); + + /* Validate trajectory_length before using it */ + int traj_len = (a->trajectory_length > 0 && a->trajectory_length < 10000) ? a->trajectory_length : 0; + tmp = PyLong_FromLong(traj_len); + if (!tmp) { + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + PyDict_SetItemString(agent, "trajectory_length", tmp); + Py_DECREF(tmp); + + /* Log trajectory arrays - validate each pointer individually before access */ + if (a->log_trajectory_x && traj_len > 0) { + PyObject *lx = PyList_New(traj_len); + if (!lx) { + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + for (int j = 0; j < traj_len; j++) { + PyObject *fv = PyFloat_FromDouble((double)a->log_trajectory_x[j]); + if (!fv) { + Py_DECREF(lx); + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + PyList_SetItem(lx, j, fv); + } + PyDict_SetItemString(agent, "log_trajectory_x", lx); + Py_DECREF(lx); + } else { + PyDict_SetItemString(agent, "log_trajectory_x", Py_None); + } + if (a->log_trajectory_y && traj_len > 0) { + PyObject *ly = PyList_New(traj_len); + if (!ly) { + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + for (int j = 0; j < traj_len; j++) { + PyObject *fv = PyFloat_FromDouble((double)a->log_trajectory_y[j]); + if (!fv) { + Py_DECREF(ly); + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + PyList_SetItem(ly, j, fv); + } + PyDict_SetItemString(agent, "log_trajectory_y", ly); + Py_DECREF(ly); + } else { + PyDict_SetItemString(agent, "log_trajectory_y", Py_None); + } + if (a->log_trajectory_z && traj_len > 0) { + PyObject *lz = PyList_New(traj_len); + if (!lz) { + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + for (int j = 0; j < traj_len; j++) { + PyObject *fv = PyFloat_FromDouble((double)a->log_trajectory_z[j]); + if (!fv) { + Py_DECREF(lz); + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + PyList_SetItem(lz, j, fv); + } + PyDict_SetItemString(agent, "log_trajectory_z", lz); + Py_DECREF(lz); + } else { + PyDict_SetItemString(agent, "log_trajectory_z", Py_None); + } + if (a->log_heading && traj_len > 0) { + PyObject *lh = PyList_New(traj_len); + if (!lh) { + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + for (int j = 0; j < traj_len; j++) { + PyObject *fv = PyFloat_FromDouble((double)a->log_heading[j]); + if (!fv) { + Py_DECREF(lh); + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + PyList_SetItem(lh, j, fv); + } + PyDict_SetItemString(agent, "log_heading", lh); + Py_DECREF(lh); + } else { + PyDict_SetItemString(agent, "log_heading", Py_None); + } + if (a->log_velocity_x && traj_len > 0) { + PyObject *lvx = PyList_New(traj_len); + if (!lvx) { + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + for (int j = 0; j < traj_len; j++) { + PyObject *fv = PyFloat_FromDouble((double)a->log_velocity_x[j]); + if (!fv) { + Py_DECREF(lvx); + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + PyList_SetItem(lvx, j, fv); + } + PyDict_SetItemString(agent, "log_velocity_x", lvx); + Py_DECREF(lvx); + } else { + PyDict_SetItemString(agent, "log_velocity_x", Py_None); + } + if (a->log_velocity_y && traj_len > 0) { + PyObject *lvy = PyList_New(traj_len); + if (!lvy) { + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + for (int j = 0; j < traj_len; j++) { + PyObject *fv = PyFloat_FromDouble((double)a->log_velocity_y[j]); + if (!fv) { + Py_DECREF(lvy); + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + PyList_SetItem(lvy, j, fv); + } + PyDict_SetItemString(agent, "log_velocity_y", lvy); + Py_DECREF(lvy); + } else { + PyDict_SetItemString(agent, "log_velocity_y", Py_None); + } + if (a->log_valid && traj_len > 0) { + PyObject *lv = PyList_New(traj_len); + if (!lv) { + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + for (int j = 0; j < traj_len; j++) { + PyObject *iv = PyLong_FromLong(a->log_valid[j]); + if (!iv) { + Py_DECREF(lv); + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + PyList_SetItem(lv, j, iv); + } + PyDict_SetItemString(agent, "log_valid", lv); + Py_DECREF(lv); + } else { + PyDict_SetItemString(agent, "log_valid", Py_None); + } + + /* Simulation state (current) */ + PyObject *pf = PyFloat_FromDouble((double)a->sim_x); + if (!pf) { + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + if (PyDict_SetItemString(agent, "sim_x", pf) < 0) { + Py_DECREF(pf); + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + Py_DECREF(pf); + + pf = PyFloat_FromDouble((double)a->sim_y); + if (!pf) { + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + if (PyDict_SetItemString(agent, "sim_y", pf) < 0) { + Py_DECREF(pf); + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + Py_DECREF(pf); + + pf = PyFloat_FromDouble((double)a->sim_z); + if (!pf) { + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + if (PyDict_SetItemString(agent, "sim_z", pf) < 0) { + Py_DECREF(pf); + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + Py_DECREF(pf); + + pf = PyFloat_FromDouble((double)a->sim_heading); + if (!pf) { + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + if (PyDict_SetItemString(agent, "sim_heading", pf) < 0) { + Py_DECREF(pf); + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + Py_DECREF(pf); + + pf = PyFloat_FromDouble((double)a->sim_vx); + if (!pf) { + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + if (PyDict_SetItemString(agent, "sim_vx", pf) < 0) { + Py_DECREF(pf); + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + Py_DECREF(pf); + + pf = PyFloat_FromDouble((double)a->sim_vy); + if (!pf) { + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + if (PyDict_SetItemString(agent, "sim_vy", pf) < 0) { + Py_DECREF(pf); + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + Py_DECREF(pf); + + pf = PyFloat_FromDouble((double)a->sim_speed); + if (!pf) { + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + if (PyDict_SetItemString(agent, "sim_speed", pf) < 0) { + Py_DECREF(pf); + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + Py_DECREF(pf); + + pf = PyFloat_FromDouble((double)a->sim_length); + if (!pf) { + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + if (PyDict_SetItemString(agent, "sim_length", pf) < 0) { + Py_DECREF(pf); + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + Py_DECREF(pf); + + pf = PyFloat_FromDouble((double)a->sim_width); + if (!pf) { + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + if (PyDict_SetItemString(agent, "sim_width", pf) < 0) { + Py_DECREF(pf); + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + Py_DECREF(pf); + + pf = PyFloat_FromDouble((double)a->sim_height); + if (!pf) { + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + if (PyDict_SetItemString(agent, "sim_height", pf) < 0) { + Py_DECREF(pf); + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + Py_DECREF(pf); + + pf = PyFloat_FromDouble((double)a->wheelbase); + if (!pf) { + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + if (PyDict_SetItemString(agent, "wheelbase", pf) < 0) { + Py_DECREF(pf); + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + Py_DECREF(pf); + + tmp = PyLong_FromLong(a->sim_valid); + if (!tmp) { + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + if (PyDict_SetItemString(agent, "sim_valid", tmp) < 0) { + Py_DECREF(tmp); + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + Py_DECREF(tmp); + + tmp = PyLong_FromLong(a->closest_path_idx_wp); + if (!tmp) { + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + if (PyDict_SetItemString(agent, "closest_path_idx_wp", tmp) < 0) { + Py_DECREF(tmp); + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + Py_DECREF(tmp); + + /* Goal position */ + pf = PyFloat_FromDouble((double)a->goal_position_x); + if (!pf) { + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + if (PyDict_SetItemString(agent, "goal_position_x", pf) < 0) { + Py_DECREF(pf); + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + Py_DECREF(pf); + + pf = PyFloat_FromDouble((double)a->goal_position_y); + if (!pf) { + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + if (PyDict_SetItemString(agent, "goal_position_y", pf) < 0) { + Py_DECREF(pf); + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + Py_DECREF(pf); + + pf = PyFloat_FromDouble((double)a->goal_position_z); + if (!pf) { + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + if (PyDict_SetItemString(agent, "goal_position_z", pf) < 0) { + Py_DECREF(pf); + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + Py_DECREF(pf); + + /* Status flags */ + tmp = PyLong_FromLong(a->stopped); + if (!tmp) { + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + if (PyDict_SetItemString(agent, "stopped", tmp) < 0) { + Py_DECREF(tmp); + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + Py_DECREF(tmp); + + tmp = PyLong_FromLong(a->current_lane_index); + if (!tmp) { + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + if (PyDict_SetItemString(agent, "current_lane_index", tmp) < 0) { + Py_DECREF(tmp); + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + Py_DECREF(tmp); + + tmp = PyLong_FromLong(a->active_agent); + if (!tmp) { + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + if (PyDict_SetItemString(agent, "active_agent", tmp) < 0) { + Py_DECREF(tmp); + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + Py_DECREF(tmp); + + tmp = PyLong_FromLong(a->mark_as_expert); + if (!tmp) { + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + if (PyDict_SetItemString(agent, "mark_as_expert", tmp) < 0) { + Py_DECREF(tmp); + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + Py_DECREF(tmp); + + tmp = PyLong_FromLong(a->reached_goal_this_episode); + if (!tmp) { + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + if (PyDict_SetItemString(agent, "reached_goal_this_episode", tmp) < 0) { + Py_DECREF(tmp); + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + Py_DECREF(tmp); + + /* Debug metrics */ + tmp = PyLong_FromLong(a->num_waypoints_reached); + if (!tmp) { + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + if (PyDict_SetItemString(agent, "num_waypoints_reached", tmp) < 0) { + Py_DECREF(tmp); + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + Py_DECREF(tmp); + + tmp = PyLong_FromLong(a->current_route_index); + if (!tmp) { + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + if (PyDict_SetItemString(agent, "current_route_index", tmp) < 0) { + Py_DECREF(tmp); + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + Py_DECREF(tmp); + + pf = PyFloat_FromDouble((double)a->cumulative_displacement); + if (!pf) { + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + if (PyDict_SetItemString(agent, "cumulative_displacement", pf) < 0) { + Py_DECREF(pf); + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + Py_DECREF(pf); + + tmp = PyLong_FromLong(a->displacement_sample_count); + if (!tmp) { + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + if (PyDict_SetItemString(agent, "displacement_sample_count", tmp) < 0) { + Py_DECREF(tmp); + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + Py_DECREF(tmp); + + /* Metrics array: [collision, offroad, reached_goal, lane_aligned, avg_displacement_error, + * red_light_violation] */ + PyObject *metrics = PyList_New(NUM_METRICS); + if (!metrics) { + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + for (int j = 0; j < NUM_METRICS; j++) { + PyObject *metric_val = PyFloat_FromDouble((double)a->metrics_array[j]); + if (!metric_val) { + Py_DECREF(metrics); + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + PyList_SetItem(metrics, j, metric_val); + } + if (PyDict_SetItemString(agent, "metrics_array", metrics) < 0) { + Py_DECREF(metrics); + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + Py_DECREF(metrics); + + /* Export route information */ + tmp = PyLong_FromLong(a->route_length); + if (!tmp) { + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + if (PyDict_SetItemString(agent, "route_length", tmp) < 0) { + Py_DECREF(tmp); + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + Py_DECREF(tmp); + + if (a->route && a->route_length > 0) { + PyObject *route_list = PyList_New(a->route_length); + if (!route_list) { + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + + for (int j = 0; j < a->route_length; j++) { + PyObject *lane_id = PyLong_FromLong(a->route[j]); + if (!lane_id) { + Py_DECREF(route_list); + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + PyList_SetItem(route_list, j, lane_id); + } + + if (PyDict_SetItemString(agent, "route", route_list) < 0) { + Py_DECREF(route_list); + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + Py_DECREF(route_list); + } else { + if (PyDict_SetItemString(agent, "route", Py_None) < 0) { + Py_DECREF(agent); + Py_DECREF(agents_list); + return NULL; + } + } + + PyList_SetItem(agents_list, i, agent); + } + + if (PyDict_SetItemString(dict, "agents", agents_list) < 0) { + Py_DECREF(agents_list); + return NULL; + } + Py_DECREF(agents_list); + } else { + if (PyDict_SetItemString(dict, "agents", Py_None) < 0) + return NULL; + } + + /* SDC Paths */ + if (env->agents && env->active_agent_count > 0) { + PyObject *sdc_list = PyList_New(env->active_agent_count); + if (!sdc_list) + return NULL; + + for (int i = 0; i < env->active_agent_count; i++) { + Agent *a = &env->agents[env->active_agent_indices[i]]; + if (a->path) { + struct Path *path = a->path; + PyObject *path_dict = PyDict_New(); + if (!path_dict) { + Py_DECREF(sdc_list); + return NULL; + } + + PyObject *tmp_val = PyLong_FromLong(path->num_waypoints); + if (!tmp_val) { + Py_DECREF(path_dict); + Py_DECREF(sdc_list); + return NULL; + } + if (PyDict_SetItemString(path_dict, "num_waypoints", tmp_val) < 0) { + Py_DECREF(tmp_val); + Py_DECREF(path_dict); + Py_DECREF(sdc_list); + return NULL; + } + Py_DECREF(tmp_val); + + PyObject *wp_list = PyList_New(path->num_waypoints); + if (!wp_list) { + Py_DECREF(path_dict); + Py_DECREF(sdc_list); + return NULL; + } + + for (int j = 0; j < path->num_waypoints; j++) { + struct Waypoint *wp = &path->waypoints[j]; + PyObject *wp_dict = PyDict_New(); + if (!wp_dict) { + Py_DECREF(wp_list); + Py_DECREF(path_dict); + Py_DECREF(sdc_list); + return NULL; + } + +#define SET_WAYPOINT_FLOAT(key, val) \ + tmp_val = PyFloat_FromDouble((double)val); \ + if (!tmp_val) { \ + Py_DECREF(wp_dict); \ + Py_DECREF(wp_list); \ + Py_DECREF(path_dict); \ + Py_DECREF(sdc_list); \ + return NULL; \ + } \ + if (PyDict_SetItemString(wp_dict, key, tmp_val) < 0) { \ + Py_DECREF(tmp_val); \ + Py_DECREF(wp_dict); \ + Py_DECREF(wp_list); \ + Py_DECREF(path_dict); \ + Py_DECREF(sdc_list); \ + return NULL; \ + } \ + Py_DECREF(tmp_val) + + SET_WAYPOINT_FLOAT("s", wp->s); + SET_WAYPOINT_FLOAT("x", wp->x); + SET_WAYPOINT_FLOAT("y", wp->y); + SET_WAYPOINT_FLOAT("heading", wp->heading); + SET_WAYPOINT_FLOAT("kappa", wp->kappa); + + tmp_val = PyLong_FromLong(wp->lane_id); + if (!tmp_val) { + Py_DECREF(wp_dict); + Py_DECREF(wp_list); + Py_DECREF(path_dict); + Py_DECREF(sdc_list); + return NULL; + } + if (PyDict_SetItemString(wp_dict, "lane_id", tmp_val) < 0) { + Py_DECREF(tmp_val); + Py_DECREF(wp_dict); + Py_DECREF(wp_list); + Py_DECREF(path_dict); + Py_DECREF(sdc_list); + return NULL; + } + Py_DECREF(tmp_val); + + PyList_SetItem(wp_list, j, wp_dict); + } + + if (PyDict_SetItemString(path_dict, "waypoints", wp_list) < 0) { + Py_DECREF(wp_list); + Py_DECREF(path_dict); + Py_DECREF(sdc_list); + return NULL; + } + Py_DECREF(wp_list); + PyList_SetItem(sdc_list, i, path_dict); + } else { + Py_INCREF(Py_None); + PyList_SetItem(sdc_list, i, Py_None); + } + } + + if (PyDict_SetItemString(dict, "sdc_paths", sdc_list) < 0) { + Py_DECREF(sdc_list); + return NULL; + } + Py_DECREF(sdc_list); + } else { + if (PyDict_SetItemString(dict, "sdc_paths", Py_None) < 0) + return NULL; + } + + /* Expose road_elements array as a list of dicts */ + if (env->road_elements && env->num_road_elements > 0) { + PyObject *road_list = PyList_New(env->num_road_elements); + if (!road_list) + return NULL; + for (int i = 0; i < env->num_road_elements; i++) { + RoadMapElement *r = &env->road_elements[i]; + PyObject *road = PyDict_New(); + if (!road) { + Py_DECREF(road_list); + return NULL; + } + + PyObject *tmp = PyLong_FromLong(r->id); + if (!tmp) { + Py_DECREF(road); + Py_DECREF(road_list); + return NULL; + } + if (PyDict_SetItemString(road, "id", tmp) < 0) { + Py_DECREF(tmp); + Py_DECREF(road); + Py_DECREF(road_list); + return NULL; + } + Py_DECREF(tmp); + + tmp = PyLong_FromLong(r->type); + if (!tmp) { + Py_DECREF(road); + Py_DECREF(road_list); + return NULL; + } + if (PyDict_SetItemString(road, "type", tmp) < 0) { + Py_DECREF(tmp); + Py_DECREF(road); + Py_DECREF(road_list); + return NULL; + } + Py_DECREF(tmp); + + /* Validate segment_length before using it */ + int seg_len = (r->segment_length > 0 && r->segment_length < 100000) ? r->segment_length : 0; + tmp = PyLong_FromLong(seg_len); + if (!tmp) { + Py_DECREF(road); + Py_DECREF(road_list); + return NULL; + } + if (PyDict_SetItemString(road, "segment_length", tmp) < 0) { + Py_DECREF(tmp); + Py_DECREF(road); + Py_DECREF(road_list); + return NULL; + } + Py_DECREF(tmp); + + /* Geometry arrays - validate each pointer individually before access */ + if (r->x && seg_len > 0) { + PyObject *lx = PyList_New(seg_len); + if (!lx) { + Py_DECREF(road); + Py_DECREF(road_list); + return NULL; + } + for (int j = 0; j < seg_len; j++) { + PyObject *fv = PyFloat_FromDouble((double)r->x[j]); + if (!fv) { + Py_DECREF(lx); + Py_DECREF(road); + Py_DECREF(road_list); + return NULL; + } + PyList_SetItem(lx, j, fv); + } + if (PyDict_SetItemString(road, "x", lx) < 0) { + Py_DECREF(lx); + Py_DECREF(road); + Py_DECREF(road_list); + return NULL; + } + Py_DECREF(lx); + } else { + if (PyDict_SetItemString(road, "x", Py_None) < 0) { + Py_DECREF(road); + Py_DECREF(road_list); + return NULL; + } + } + if (r->y && seg_len > 0) { + PyObject *ly = PyList_New(seg_len); + if (!ly) { + Py_DECREF(road); + Py_DECREF(road_list); + return NULL; + } + for (int j = 0; j < seg_len; j++) { + PyObject *fv = PyFloat_FromDouble((double)r->y[j]); + if (!fv) { + Py_DECREF(ly); + Py_DECREF(road); + Py_DECREF(road_list); + return NULL; + } + PyList_SetItem(ly, j, fv); + } + if (PyDict_SetItemString(road, "y", ly) < 0) { + Py_DECREF(ly); + Py_DECREF(road); + Py_DECREF(road_list); + return NULL; + } + Py_DECREF(ly); + } else { + if (PyDict_SetItemString(road, "y", Py_None) < 0) { + Py_DECREF(road); + Py_DECREF(road_list); + return NULL; + } + } + if (r->z && seg_len > 0) { + PyObject *lz = PyList_New(seg_len); + if (!lz) { + Py_DECREF(road); + Py_DECREF(road_list); + return NULL; + } + for (int j = 0; j < seg_len; j++) { + PyObject *fv = PyFloat_FromDouble((double)r->z[j]); + if (!fv) { + Py_DECREF(lz); + Py_DECREF(road); + Py_DECREF(road_list); + return NULL; + } + PyList_SetItem(lz, j, fv); + } + if (PyDict_SetItemString(road, "z", lz) < 0) { + Py_DECREF(lz); + Py_DECREF(road); + Py_DECREF(road_list); + return NULL; + } + Py_DECREF(lz); + } else { + if (PyDict_SetItemString(road, "z", Py_None) < 0) { + Py_DECREF(road); + Py_DECREF(road_list); + return NULL; + } + } + + /* Lane-specific fields */ + if (is_road_lane(r->type) && r->entry_lanes != NULL && r->num_entries > 0) { + tmp = PyList_New(r->num_entries); + } else { + tmp = PyList_New(0); + } + if (!tmp) { + Py_DECREF(road); + Py_DECREF(road_list); + return NULL; + } + if (is_road_lane(r->type) && r->entry_lanes != NULL && r->num_entries > 0) { + for (int k = 0; k < r->num_entries; k++) { + PyList_SET_ITEM(tmp, k, PyLong_FromLong(r->entry_lanes[k])); + } + } + if (PyDict_SetItemString(road, "entry_lanes", tmp) < 0) { + Py_DECREF(tmp); + Py_DECREF(road); + Py_DECREF(road_list); + return NULL; + } + Py_DECREF(tmp); + + if (is_road_lane(r->type) && r->exit_lanes != NULL && r->num_exits > 0) { + tmp = PyList_New(r->num_exits); + } else { + tmp = PyList_New(0); + } + if (!tmp) { + Py_DECREF(road); + Py_DECREF(road_list); + return NULL; + } + if (is_road_lane(r->type) && r->exit_lanes != NULL && r->num_exits > 0) { + for (int k = 0; k < r->num_exits; k++) { + PyList_SET_ITEM(tmp, k, PyLong_FromLong(r->exit_lanes[k])); + } + } + if (PyDict_SetItemString(road, "exit_lanes", tmp) < 0) { + Py_DECREF(tmp); + Py_DECREF(road); + Py_DECREF(road_list); + return NULL; + } + Py_DECREF(tmp); + + PyObject *pf = PyFloat_FromDouble((double)r->speed_limit); + if (!pf) { + Py_DECREF(road); + Py_DECREF(road_list); + return NULL; + } + if (PyDict_SetItemString(road, "speed_limit", pf) < 0) { + Py_DECREF(pf); + Py_DECREF(road); + Py_DECREF(road_list); + return NULL; + } + Py_DECREF(pf); + + PyList_SetItem(road_list, i, road); + } + if (PyDict_SetItemString(dict, "road_elements", road_list) < 0) { + Py_DECREF(road_list); + return NULL; + } + Py_DECREF(road_list); + } else { + if (PyDict_SetItemString(dict, "road_elements", Py_None) < 0) + return NULL; + } + + /* Expose traffic_elements array as a list of dicts */ + if (env->traffic_elements && env->num_traffic_elements > 0) { + PyObject *traffic_list = PyList_New(env->num_traffic_elements); + if (!traffic_list) + return NULL; + for (int i = 0; i < env->num_traffic_elements; i++) { + TrafficControlElement *t = &env->traffic_elements[i]; + PyObject *traffic = PyDict_New(); + if (!traffic) { + Py_DECREF(traffic_list); + return NULL; + } + + PyObject *tmp = PyLong_FromLong(t->id); + if (!tmp) { + Py_DECREF(traffic); + Py_DECREF(traffic_list); + return NULL; + } + if (PyDict_SetItemString(traffic, "id", tmp) < 0) { + Py_DECREF(tmp); + Py_DECREF(traffic); + Py_DECREF(traffic_list); + return NULL; + } + Py_DECREF(tmp); + + tmp = PyLong_FromLong(t->type); + if (!tmp) { + Py_DECREF(traffic); + Py_DECREF(traffic_list); + return NULL; + } + if (PyDict_SetItemString(traffic, "type", tmp) < 0) { + Py_DECREF(tmp); + Py_DECREF(traffic); + Py_DECREF(traffic_list); + return NULL; + } + Py_DECREF(tmp); + + /* Validate state_length before using it */ + int state_len = (t->state_length > 0 && t->state_length < 10000) ? t->state_length : 0; + tmp = PyLong_FromLong(state_len); + if (!tmp) { + Py_DECREF(traffic); + Py_DECREF(traffic_list); + return NULL; + } + if (PyDict_SetItemString(traffic, "state_length", tmp) < 0) { + Py_DECREF(tmp); + Py_DECREF(traffic); + Py_DECREF(traffic_list); + return NULL; + } + Py_DECREF(tmp); + + /* States array - validate pointer before access */ + if (t->states && state_len > 0) { + PyObject *ls = PyList_New(state_len); + if (!ls) { + Py_DECREF(traffic); + Py_DECREF(traffic_list); + return NULL; + } + for (int j = 0; j < state_len; j++) { + PyObject *iv = PyLong_FromLong(t->states[j]); + if (!iv) { + Py_DECREF(ls); + Py_DECREF(traffic); + Py_DECREF(traffic_list); + return NULL; + } + PyList_SetItem(ls, j, iv); + } + if (PyDict_SetItemString(traffic, "states", ls) < 0) { + Py_DECREF(ls); + Py_DECREF(traffic); + Py_DECREF(traffic_list); + return NULL; + } + Py_DECREF(ls); + } else { + if (PyDict_SetItemString(traffic, "states", Py_None) < 0) { + Py_DECREF(traffic); + Py_DECREF(traffic_list); + return NULL; + } + } + + /* Stop line endpoints */ + PyObject *sl = PyList_New(6); + if (!sl) { + Py_DECREF(traffic); + Py_DECREF(traffic_list); + return NULL; + } + for (int k = 0; k < 6; k++) { + PyList_SetItem(sl, k, PyFloat_FromDouble((double)t->stop_line[k])); + } + if (PyDict_SetItemString(traffic, "stop_line", sl) < 0) { + Py_DECREF(sl); + Py_DECREF(traffic); + Py_DECREF(traffic_list); + return NULL; + } + Py_DECREF(sl); + + /* Position (stop_line midpoint for backward compat) */ + float mid_x = (t->stop_line[0] + t->stop_line[3]) * 0.5f; + float mid_y = (t->stop_line[1] + t->stop_line[4]) * 0.5f; + PyObject *pf = PyFloat_FromDouble((double)mid_x); + if (!pf) { + Py_DECREF(traffic); + Py_DECREF(traffic_list); + return NULL; + } + if (PyDict_SetItemString(traffic, "x", pf) < 0) { + Py_DECREF(pf); + Py_DECREF(traffic); + Py_DECREF(traffic_list); + return NULL; + } + Py_DECREF(pf); + + pf = PyFloat_FromDouble((double)mid_y); + if (!pf) { + Py_DECREF(traffic); + Py_DECREF(traffic_list); + return NULL; + } + if (PyDict_SetItemString(traffic, "y", pf) < 0) { + Py_DECREF(pf); + Py_DECREF(traffic); + Py_DECREF(traffic_list); + return NULL; + } + Py_DECREF(pf); + + /* Controlled lanes array - validate pointer before access */ + if (t->controlled_lanes && t->num_controlled_lanes > 0) { + PyObject *ll = PyList_New(t->num_controlled_lanes); + if (!ll) { + Py_DECREF(traffic); + Py_DECREF(traffic_list); + return NULL; + } + for (int j = 0; j < t->num_controlled_lanes; j++) { + PyObject *lane_id = PyLong_FromLong(t->controlled_lanes[j]); + if (!lane_id) { + Py_DECREF(ll); + Py_DECREF(traffic); + Py_DECREF(traffic_list); + return NULL; + } + PyList_SetItem(ll, j, lane_id); + } + if (PyDict_SetItemString(traffic, "controlled_lanes", ll) < 0) { + Py_DECREF(ll); + Py_DECREF(traffic); + Py_DECREF(traffic_list); + return NULL; + } + Py_DECREF(ll); + } else { + if (PyDict_SetItemString(traffic, "controlled_lanes", Py_None) < 0) { + Py_DECREF(traffic); + Py_DECREF(traffic_list); + return NULL; + } + } + + PyList_SetItem(traffic_list, i, traffic); + } + if (PyDict_SetItemString(dict, "traffic_elements", traffic_list) < 0) { + Py_DECREF(traffic_list); + return NULL; + } + Py_DECREF(traffic_list); + } else { + if (PyDict_SetItemString(dict, "traffic_elements", Py_None) < 0) + return NULL; + } + + /* Map corners (bounding box) from GridMap */ + if (env->grid_map) { + PyObject *corners_list = PyList_New(4); + if (!corners_list) + return NULL; + + PyObject *brx = PyFloat_FromDouble((double)env->grid_map->bottom_right_x); + PyObject *bry = PyFloat_FromDouble((double)env->grid_map->bottom_right_y); + PyObject *tlx = PyFloat_FromDouble((double)env->grid_map->top_left_x); + PyObject *tly = PyFloat_FromDouble((double)env->grid_map->top_left_y); + + if (!tlx || !tly || !brx || !bry) { + Py_XDECREF(tlx); + Py_XDECREF(tly); + Py_XDECREF(brx); + Py_XDECREF(bry); + Py_DECREF(corners_list); + return NULL; + } + + PyList_SetItem(corners_list, 0, tlx); // min_x = top_left_x + PyList_SetItem(corners_list, 1, bry); // min_y = bottom_right_y + PyList_SetItem(corners_list, 2, brx); // max_x = bottom_right_x + PyList_SetItem(corners_list, 3, tly); // max_y = top_left_y + + if (PyDict_SetItemString(dict, "map_corners", corners_list) < 0) { + Py_DECREF(corners_list); + return NULL; + } + Py_DECREF(corners_list); + } else { + if (PyDict_SetItemString(dict, "map_corners", Py_None) < 0) + return NULL; + } + if (env->observations && env->active_agent_count > 0) { + /* Agent observations */ + int max_obs = compute_observation_size(env); + PyObject *obs_data = PyList_New(env->active_agent_count); + if (!obs_data) + return NULL; + + float (*observations)[max_obs] = (float (*)[max_obs])env->observations; + + for (int i = 0; i < env->active_agent_count; i++) { + PyObject *agent_obs = PyList_New(max_obs); + if (!agent_obs) { + Py_DECREF(obs_data); + return NULL; + } + + for (int j = 0; j < max_obs; j++) { + PyObject *obs_val = PyFloat_FromDouble((double)observations[i][j]); + if (!obs_val) { + Py_DECREF(agent_obs); + Py_DECREF(obs_data); + return NULL; + } + PyList_SetItem(agent_obs, j, obs_val); + } + + PyList_SetItem(obs_data, i, agent_obs); + } + + if (PyDict_SetItemString(dict, "agent_observations", obs_data) < 0) { + Py_DECREF(obs_data); + return NULL; + } + Py_DECREF(obs_data); + } else { + if (PyDict_SetItemString(dict, "agent_observations", Py_None) < 0) + return NULL; + } + + return dict; +} + static PyObject *my_shared(PyObject *self, PyObject *args, PyObject *kwargs) { - char *map_dir = unpack_str(kwargs, "map_dir"); + PyObject *map_files = PyDict_GetItemString(kwargs, "map_files"); + if (!map_files || !PyList_Check(map_files)) { + PyErr_SetString(PyExc_TypeError, "map_files must be a list of strings"); + return NULL; + } int num_agents = unpack(kwargs, "num_agents"); int num_maps = unpack(kwargs, "num_maps"); + int starting_map_counter = unpack(kwargs, "starting_map_counter"); + int eval_mode = unpack(kwargs, "eval_mode"); + int s_map_counter = starting_map_counter; int init_mode = unpack(kwargs, "init_mode"); int control_mode = unpack(kwargs, "control_mode"); + int simulation_mode = unpack(kwargs, "simulation_mode"); int init_steps = unpack(kwargs, "init_steps"); - int goal_behavior = unpack(kwargs, "goal_behavior"); - float goal_target_distance = unpack(kwargs, "goal_target_distance"); - int max_controlled_agents = unpack(kwargs, "max_controlled_agents"); + int seed = unpack(kwargs, "seed"); + int min_agents_per_env = unpack(kwargs, "min_agents_per_env"); + int max_agents_per_env = unpack(kwargs, "max_agents_per_env"); + int num_eval_scenarios = unpack(kwargs, "num_eval_scenarios"); + + if (min_agents_per_env <= 0 || max_agents_per_env <= 0) { + PyErr_SetString(PyExc_ValueError, "min_agents_per_env and max_agents_per_env must be > 0"); + return NULL; + } + if (min_agents_per_env > max_agents_per_env) { + PyErr_SetString(PyExc_ValueError, "min_agents_per_env must be <= max_agents_per_env"); + return NULL; + } + if (num_agents < min_agents_per_env) { + PyErr_SetString(PyExc_ValueError, "num_agents must be >= min_agents_per_env"); + return NULL; + } + + srand(seed); + + // GIGAFLOW mode: use random sampling for agent counts per env + if (simulation_mode == SIMULATION_GIGAFLOW) { + if (eval_mode) { + // Eval mode: fixed agent count, sequential map cycling + int agents_per_env = max_agents_per_env; + int env_count = (num_agents + agents_per_env - 1) / agents_per_env; + env_count--; + + env_count = env_count > num_eval_scenarios ? num_eval_scenarios : env_count; + + PyObject *agent_offsets = PyList_New(env_count + 1); + PyObject *map_ids_list = PyList_New(env_count); + + int offset = 0; + for (int i = 0; i < env_count; i++) { + PyList_SetItem(agent_offsets, i, PyLong_FromLong(offset)); + PyList_SetItem(map_ids_list, i, PyLong_FromLong((s_map_counter + i) % num_maps)); + offset += agents_per_env; + } + PyList_SetItem(agent_offsets, env_count, PyLong_FromLong(offset)); + + PyObject *tuple = PyTuple_New(3); + PyTuple_SetItem(tuple, 0, agent_offsets); + PyTuple_SetItem(tuple, 1, map_ids_list); + PyTuple_SetItem(tuple, 2, PyLong_FromLong(env_count)); + return tuple; + } + + // Training mode: random agent counts per env + int *agent_counts = malloc((num_agents / min_agents_per_env + 1) * sizeof(int)); + int remaining = num_agents; + int env_count = 0; + + while (remaining > 0) { + int count; + if (remaining <= max_agents_per_env) { + count = remaining; + } else { + // 1. We must leave at least min_agents_per_env for the future. + int absolute_max_allowed = remaining - min_agents_per_env; + + // 2. We cannot take more than max_agents_per_env right now. + int current_upper_bound = + (absolute_max_allowed < max_agents_per_env) ? absolute_max_allowed : max_agents_per_env; + + // 3. We must take at least min_agents_per_env right now. + int current_lower_bound = min_agents_per_env; + + // Safety check: if constraints are tight, lower might equal upper. + // If absolute_max_allowed < min_lower_bound for example leading to + // current_upper_bound < current_lower_bound + if (current_upper_bound <= current_lower_bound) { + count = current_lower_bound; + } else { + // Now the range is guaranteed to be positive. + int range = current_upper_bound - current_lower_bound + 1; + count = current_lower_bound + (rand() % range); + } + } + agent_counts[env_count++] = count; + remaining -= count; + } + + // Build Python return lists + PyObject *agent_offsets = PyList_New(env_count + 1); + PyObject *map_ids_list = PyList_New(env_count); + + int offset = 0; + for (int i = 0; i < env_count; i++) { + PyList_SetItem(agent_offsets, i, PyLong_FromLong(offset)); + PyList_SetItem(map_ids_list, i, PyLong_FromLong(rand() % num_maps)); + offset += agent_counts[i]; + } + PyList_SetItem(agent_offsets, env_count, PyLong_FromLong(num_agents)); + + free(agent_counts); - clock_gettime(CLOCK_REALTIME, &ts); - srand(ts.tv_nsec); // Always use random sampling with replacement + PyObject *tuple = PyTuple_New(3); + PyTuple_SetItem(tuple, 0, agent_offsets); + PyTuple_SetItem(tuple, 1, map_ids_list); + PyTuple_SetItem(tuple, 2, PyLong_FromLong(env_count)); + return tuple; + } + // REPLAY mode - existing logic with max_agents_per_env cap int total_agent_count = 0; + int map_id = 0; int env_count = 0; - int max_envs = num_agents; - int maps_checked = 0; + + if (eval_mode) { + max_envs = num_eval_scenarios; + } + // Define the upper boundary for the map window + int end_map_index = starting_map_counter + num_eval_scenarios; + PyObject *agent_offsets = PyList_New(max_envs + 1); PyObject *map_ids = PyList_New(max_envs); - // Getting env count - while (total_agent_count < num_agents && env_count < max_envs) { - char map_file[512]; + // Added condition: s_map_counter < end_map_index + while (total_agent_count < num_agents && env_count < max_envs && (!eval_mode || s_map_counter < end_map_index)) { - // Always sample randomly with replacement - int map_id = rand() % num_maps; + if (eval_mode) { + map_id = s_map_counter % num_maps; + s_map_counter += 1; // This increments towards end_map_index + } else { + map_id = rand() % num_maps; + } - // printf("Sampling map_id: %d\n", map_id); + const char *map_file = PyUnicode_AsUTF8(PyList_GetItem(map_files, map_id)); Drive *env = calloc(1, sizeof(Drive)); env->init_mode = init_mode; env->control_mode = control_mode; + env->simulation_mode = simulation_mode; env->init_steps = init_steps; - env->goal_behavior = goal_behavior; - env->goal_target_distance = goal_target_distance; - env->max_controlled_agents = max_controlled_agents; - snprintf(map_file, sizeof(map_file), "%s/map_%03d.bin", map_dir, map_id); - env->entities = load_map_binary(map_file, env); - // Count the number of controllable agents in map + env->num_max_agents = max_agents_per_env; + load_map_binary(map_file, env); + set_active_agents(env); // Skip map if it doesn't contain any controllable agents if (env->active_agent_count == 0) { maps_checked++; - + // Fix the safeguard, the maps_checked is increased even when the same map is picked. // Safeguard: if we've checked all available maps and found no active agents, raise an error - if (maps_checked >= num_maps) { - for (int j = 0; j < env->num_entities; j++) { - free_entity(&env->entities[j]); - } - free(env->entities); - free(env->active_agent_indices); - free(env->static_agent_indices); - free(env->expert_static_agent_indices); - free(env->tracks_to_predict_indices); - free(env); - Py_DECREF(agent_offsets); - Py_DECREF(map_ids); - char error_msg[256]; - sprintf(error_msg, "No controllable agents found in any of the %d available maps", num_maps); - PyErr_SetString(PyExc_ValueError, error_msg); - return NULL; - } + // if(maps_checked >= num_maps) { + // for(int j=0;jnum_total_agents;j++) free_agent(&env->agents[j]); + // for (int j=0;jnum_road_elements;j++) free_road_element(&env->road_elements[j]); + // for (int j=0;jnum_traffic_elements;j++) free_traffic_element(&env->traffic_elements[j]); + // free(env->agents); + // free(env->road_elements); + // free(env->traffic_elements); + // free(env->active_agent_indices); + // free(env->static_agent_indices); + // free(env->expert_static_agent_indices); + // free(env); + // Py_DECREF(agent_offsets); + // Py_DECREF(map_ids); + // char error_msg[256]; + // sprintf(error_msg, "No controllable agents found in any of the %d available maps", num_maps); + // PyErr_SetString(PyExc_ValueError, error_msg); + // return NULL; + // } - for (int j = 0; j < env->num_entities; j++) { - free_entity(&env->entities[j]); - } - free(env->entities); + for (int j = 0; j < env->num_total_agents; j++) + free_agent(&env->agents[j]); + for (int j = 0; j < env->num_road_elements; j++) + free_road_element(&env->road_elements[j]); + for (int j = 0; j < env->num_traffic_elements; j++) + free_traffic_element(&env->traffic_elements[j]); + free(env->agents); + free(env->road_elements); + free(env->traffic_elements); free(env->active_agent_indices); free(env->static_agent_indices); free(env->expert_static_agent_indices); - free(env->tracks_to_predict_indices); free(env); continue; } @@ -154,25 +1729,28 @@ static PyObject *my_shared(PyObject *self, PyObject *args, PyObject *kwargs) { PyList_SetItem(agent_offsets, env_count, offset); total_agent_count += env->active_agent_count; env_count++; - for (int j = 0; j < env->num_entities; j++) { - free_entity(&env->entities[j]); - } - free(env->entities); + for (int j = 0; j < env->num_total_agents; j++) + free_agent(&env->agents[j]); + for (int j = 0; j < env->num_road_elements; j++) + free_road_element(&env->road_elements[j]); + for (int j = 0; j < env->num_traffic_elements; j++) + free_traffic_element(&env->traffic_elements[j]); + free(env->agents); + free(env->road_elements); + free(env->traffic_elements); free(env->active_agent_indices); free(env->static_agent_indices); - free(env->tracks_to_predict_indices); free(env->expert_static_agent_indices); free(env); } - + // printf("Generated %d environments to cover %d agents (requested %d agents)\n", env_count, total_agent_count, + // num_agents); if (total_agent_count >= num_agents) { total_agent_count = num_agents; } - PyObject *final_total_agent_count = PyLong_FromLong(total_agent_count); PyList_SetItem(agent_offsets, env_count, final_total_agent_count); PyObject *final_env_count = PyLong_FromLong(env_count); - // resize lists PyObject *resized_agent_offsets = PyList_GetSlice(agent_offsets, 0, env_count + 1); PyObject *resized_map_ids = PyList_GetSlice(map_ids, 0, env_count); @@ -184,102 +1762,116 @@ static PyObject *my_shared(PyObject *self, PyObject *args, PyObject *kwargs) { } static int my_init(Env *env, PyObject *args, PyObject *kwargs) { - env->human_agent_idx = unpack(kwargs, "human_agent_idx"); env->ini_file = unpack_str(kwargs, "ini_file"); - env_init_config conf = {0}; - if (ini_parse(env->ini_file, handler, &conf) < 0) { - printf("Error while loading %s", env->ini_file); - } - if (kwargs && PyDict_GetItemString(kwargs, "episode_length")) { - conf.episode_length = (int)unpack(kwargs, "episode_length"); - } - if (conf.episode_length <= 0) { - PyErr_SetString(PyExc_ValueError, "episode_length must be > 0 (set in INI or kwargs)"); - return -1; - } - -// Allow all settings to be overridden via kwargs (ini provides defaults) -#define OVERRIDE_INT(field) \ - if (kwargs && PyDict_GetItemString(kwargs, #field)) { \ - conf.field = (int)unpack(kwargs, #field); \ - } -#define OVERRIDE_FLOAT(field) \ - if (kwargs && PyDict_GetItemString(kwargs, #field)) { \ - conf.field = (float)unpack(kwargs, #field); \ - } - - OVERRIDE_INT(render_mode); - OVERRIDE_INT(action_type); - OVERRIDE_INT(dynamics_model); - OVERRIDE_FLOAT(reward_vehicle_collision); - OVERRIDE_FLOAT(reward_offroad_collision); - OVERRIDE_FLOAT(reward_goal); - OVERRIDE_FLOAT(reward_goal_post_respawn); - OVERRIDE_INT(collision_behavior); - OVERRIDE_INT(offroad_behavior); - OVERRIDE_FLOAT(dt); - OVERRIDE_INT(termination_mode); - OVERRIDE_INT(init_mode); - OVERRIDE_INT(control_mode); - OVERRIDE_INT(goal_behavior); - OVERRIDE_FLOAT(goal_target_distance); - OVERRIDE_FLOAT(goal_radius); - OVERRIDE_FLOAT(goal_speed); - OVERRIDE_INT(max_controlled_agents); - -#undef OVERRIDE_INT -#undef OVERRIDE_FLOAT - - env->action_type = conf.action_type; - env->dynamics_model = conf.dynamics_model; - env->reward_vehicle_collision = conf.reward_vehicle_collision; - env->reward_offroad_collision = conf.reward_offroad_collision; - env->reward_goal = conf.reward_goal; - env->reward_goal_post_respawn = conf.reward_goal_post_respawn; - env->episode_length = conf.episode_length; - env->termination_mode = conf.termination_mode; - env->collision_behavior = conf.collision_behavior; - env->offroad_behavior = conf.offroad_behavior; - env->max_controlled_agents = unpack(kwargs, "max_controlled_agents"); - env->dt = conf.dt; - env->init_mode = (int)unpack(kwargs, "init_mode"); - env->control_mode = (int)unpack(kwargs, "control_mode"); - env->goal_behavior = (int)unpack(kwargs, "goal_behavior"); - env->goal_target_distance = (float)unpack(kwargs, "goal_target_distance"); + // env_init_config conf = {0}; + // if (ini_parse(env->ini_file, handler, &conf) < 0) { + // printf("Error while loading %s", env->ini_file); + // } + // if (kwargs && PyDict_GetItemString(kwargs, "scenario_length")) { + // conf.scenario_length = (int)unpack(kwargs, "scenario_length"); + // } + // if (kwargs && PyDict_GetItemString(kwargs, "termination_mode")) { + // conf.termination_mode = (int)unpack(kwargs, "termination_mode"); + // } + // if (conf.scenario_length <= 0) { + // PyErr_SetString(PyExc_ValueError, "scenario_length must be > 0 (set in INI or kwargs)"); + // return -1; + // } + env->action_type = (int)unpack(kwargs, "action_type"); + env->dynamics_model = (int)unpack(kwargs, "dynamics_model"); + env->reward_goal = (float)unpack(kwargs, "reward_goal"); + env->reward_vehicle_collision = (float)unpack(kwargs, "reward_vehicle_collision"); + env->reward_offroad_collision = (float)unpack(kwargs, "reward_offroad_collision"); + env->reward_comfort = (float)unpack(kwargs, "reward_comfort"); + env->reward_lane_align = (float)unpack(kwargs, "reward_lane_align"); + env->reward_vel_align = (float)unpack(kwargs, "reward_vel_align"); + env->reward_lane_center = (float)unpack(kwargs, "reward_lane_center"); + env->reward_center_bias = (float)unpack(kwargs, "reward_center_bias"); + env->reward_velocity = (float)unpack(kwargs, "reward_velocity"); + env->reward_reverse = (float)unpack(kwargs, "reward_reverse"); + env->reward_traffic_light_violation = (float)unpack(kwargs, "reward_traffic_light_violation"); + env->reward_timestep = (float)unpack(kwargs, "reward_timestep"); + env->reward_overspeed = (float)unpack(kwargs, "reward_overspeed"); + env->reward_ade = (float)unpack(kwargs, "reward_ade"); + env->collision_behavior = (int)unpack(kwargs, "collision_behavior"); + env->offroad_behavior = (int)unpack(kwargs, "offroad_behavior"); + env->traffic_light_behavior = (int)unpack(kwargs, "traffic_light_behavior"); env->goal_radius = (float)unpack(kwargs, "goal_radius"); - env->goal_speed = (float)unpack(kwargs, "goal_speed"); - env->render_mode = (int)unpack(kwargs, "render_mode"); - char *map_dir = unpack_str(kwargs, "map_dir"); - int map_id = unpack(kwargs, "map_id"); - int max_agents = unpack(kwargs, "max_agents"); - int init_steps = unpack(kwargs, "init_steps"); - char map_file[512]; - snprintf(map_file, sizeof(map_file), "%s/map_%03d.bin", map_dir, map_id); - env->num_agents = max_agents; - env->map_name = strdup(map_file); + env->min_waypoint_spacing = (float)unpack(kwargs, "min_waypoint_spacing"); + env->max_waypoint_spacing = (float)unpack(kwargs, "max_waypoint_spacing"); + env->num_target_waypoints = (int)unpack(kwargs, "num_target_waypoints"); + if (env->num_target_waypoints > MAX_TARGET_WAYPOINTS) { + env->num_target_waypoints = MAX_TARGET_WAYPOINTS; + } + env->target_type = (int)unpack(kwargs, "target_type"); + env->use_rear_axle = (int)unpack(kwargs, "use_rear_axle"); + env->max_boundary_segment_observations = (int)unpack(kwargs, "max_boundary_segment_observations"); + env->max_lane_segment_observations = (int)unpack(kwargs, "max_lane_segment_observations"); + env->max_partner_observations = (int)unpack(kwargs, "max_partner_observations"); + env->max_traffic_light_observations = (int)unpack(kwargs, "max_traffic_light_observations"); + env->max_stop_sign_observations = (int)unpack(kwargs, "max_stop_sign_observations"); + env->dt = (float)unpack(kwargs, "dt"); + env->spawn_initial_speed = (float)unpack(kwargs, "spawn_initial_speed"); + env->goal_speed_threshold = (float)unpack(kwargs, "goal_speed_threshold"); + env->scenario_length = (int)unpack(kwargs, "scenario_length"); + env->termination_mode = (int)unpack(kwargs, "termination_mode"); + env->inactive_agent_threshold = (float)unpack(kwargs, "inactive_agent_threshold"); + char *map_file = unpack_str(kwargs, "map_file"); + env->map_name = map_file; + int max_agents = (int)unpack(kwargs, "max_agents"); + env->num_controllable_agents = max_agents; + int max_agents_per_env = (int)unpack(kwargs, "max_agents_per_env"); + env->num_max_agents = max_agents_per_env; + + int init_steps = (int)unpack(kwargs, "init_steps"); env->init_steps = init_steps; env->timestep = init_steps; + env->init_mode = (int)unpack(kwargs, "init_mode"); + env->control_mode = (int)unpack(kwargs, "control_mode"); + env->simulation_mode = (int)unpack(kwargs, "simulation_mode"); + env->reward_conditioning = (bool)unpack(kwargs, "reward_conditioning"); + env->reward_randomization = (bool)unpack(kwargs, "reward_randomization"); + env->compute_eval_metrics = (bool)unpack(kwargs, "compute_eval_metrics"); + env->eval_mode = (int)unpack(kwargs, "eval_mode"); + init(env); return 0; } -static int my_log(PyObject *dict, Log *log) { +static int my_log(PyObject *dict, Env *env, Log *log, float n) { + float total_distance_travelled = log->total_distance_travelled * n; + float total_infractions = log->total_infractions * n; + float avg_distance_per_infraction = total_distance_travelled / fmaxf(1.0f, total_infractions); + assign_to_dict(dict, "n", log->n); - assign_to_dict(dict, "score", log->score); assign_to_dict(dict, "offroad_rate", log->offroad_rate); - assign_to_dict(dict, "collision_rate", log->collision_rate); assign_to_dict(dict, "episode_length", log->episode_length); + assign_to_dict(dict, "collision_rate", log->collision_rate); assign_to_dict(dict, "episode_return", log->episode_return); - assign_to_dict(dict, "dnf_rate", log->dnf_rate); - assign_to_dict(dict, "completion_rate", log->completion_rate); - assign_to_dict(dict, "lane_alignment_rate", log->lane_alignment_rate); - assign_to_dict(dict, "perc_controlled", log->perc_controlled); - assign_to_dict(dict, "perc_other", log->perc_other); - assign_to_dict(dict, "offroad_per_agent", log->offroad_per_agent); - assign_to_dict(dict, "collisions_per_agent", log->collisions_per_agent); - assign_to_dict(dict, "goals_sampled_this_episode", log->goals_sampled_this_episode); - assign_to_dict(dict, "goals_reached_this_episode", log->goals_reached_this_episode); - assign_to_dict(dict, "speed_at_goal", log->speed_at_goal); + assign_to_dict(dict, "red_light_violation_rate", log->red_light_violation_rate); + assign_to_dict(dict, "comfort_violation_count", log->comfort_violation_count); // assign_to_dict(dict, "avg_displacement_error", log->avg_displacement_error); + assign_to_dict(dict, "velocity_progress_sum", log->velocity_progress_sum); + assign_to_dict(dict, "num_goals_reached", log->num_goals_reached); + assign_to_dict(dict, "lane_center_rate", log->lane_center_rate); + assign_to_dict(dict, "dnf_rate", log->dnf_rate); + assign_to_dict(dict, "score", log->score); + assign_to_dict(dict, "avg_speed_per_agent", log->avg_speed_per_agent); + assign_to_dict(dict, "avg_distance_per_infraction", avg_distance_per_infraction); + + if (env->compute_eval_metrics) { + // Puffer score components + assign_to_dict(dict, "at_fault_collision_rate", log->at_fault_collision_rate); + assign_to_dict(dict, "puffer_score", log->puffer_score); + assign_to_dict(dict, "ttc_within_bound_rate", log->ttc_within_bound_rate); + assign_to_dict(dict, "driving_direction_score", log->driving_direction_score); + assign_to_dict(dict, "speed_limit_compliance", log->speed_limit_compliance); + assign_to_dict(dict, "making_progress_rate", log->making_progress_rate); + assign_to_dict(dict, "progress_ratio", log->progress_ratio); + assign_to_dict(dict, "comfort_score", log->comfort_score); + assign_to_dict(dict, "multi_lane_time", log->multi_lane_time); + assign_to_dict(dict, "multi_lane_score", log->multi_lane_score); + } + return 0; } diff --git a/pufferlib/ocean/drive/datatypes.h b/pufferlib/ocean/drive/datatypes.h new file mode 100644 index 0000000000..fc2361cd74 --- /dev/null +++ b/pufferlib/ocean/drive/datatypes.h @@ -0,0 +1,351 @@ +#define UNKNOWN 0 + +// -- REWARD CONDITIONING COEFFICIENTS +#define REWARD_COEF_GOAL_RADIUS 0 +#define REWARD_COEF_COLLISION 1 +#define REWARD_COEF_OFFROAD 2 +#define REWARD_COEF_COMFORT 3 +#define REWARD_COEF_LANE_ALIGN 4 +#define REWARD_COEF_VEL_ALIGN 5 +#define REWARD_COEF_LANE_CENTER 6 +#define REWARD_COEF_CENTER_BIAS 7 +#define REWARD_COEF_VELOCITY 8 +#define REWARD_COEF_REVERSE 9 +#define REWARD_COEF_TRAFFIC_LIGHT 10 +#define REWARD_COEF_TIMESTEP 11 +#define REWARD_COEF_OVERSPEED 12 +// Dynamic conditioning coefficients +#define REWARD_COEF_THROTTLE 13 +#define REWARD_COEF_STEER 14 +#define REWARD_COEF_ACC 15 +#define NUM_REWARD_COEFS 16 + +// -- AGENT TYPE +#define VEHICLE 1 +#define PEDESTRIAN 2 +#define CYCLIST 3 +#define OTHER 4 + +// -- ROAD TYPE +#define LANE_FREEWAY 1 +#define LANE_SURFACE_STREET 2 +#define LANE_BIKE_LANE 3 + +#define ROAD_LINE_UNKNOWN 10 +#define ROAD_LINE_BROKEN_SINGLE_WHITE 11 +#define ROAD_LINE_SOLID_SINGLE_WHITE 12 +#define ROAD_LINE_SOLID_DOUBLE_WHITE 13 +#define ROAD_LINE_BROKEN_SINGLE_YELLOW 14 +#define ROAD_LINE_BROKEN_DOUBLE_YELLOW 15 +#define ROAD_LINE_SOLID_SINGLE_YELLOW 16 +#define ROAD_LINE_SOLID_DOUBLE_YELLOW 17 +#define ROAD_LINE_PASSING_DOUBLE_YELLOW 18 + +#define ROAD_EDGE_UNKNOWN 20 +#define ROAD_EDGE_BOUNDARY 21 +#define ROAD_EDGE_MEDIAN 22 +#define ROAD_EDGE_SIDEWALK 23 + +#define CROSSWALK 31 +#define DRIVEWAY 32 + +// -- TRAFFIC CONTROL TYPE +#define TRAFFIC_LIGHT 1 +#define STOP_SIGN 2 +#define YIELD_SIGN 3 +#define SPEED_LIMIT_SIGN 4 + +#define TL_STATE_DISABLED 0 +#define TL_STATE_YELLOW 2 +#define TL_STATE_GREEN 3 +#define TL_STATE_RED 4 +// Number of normalized traffic light states: disabled, red, yellow, green +#define NUM_TRAFFIC_LIGHT_STATES 4 + +// Metrics array indices +#define NUM_METRICS 18 +#define COLLISION_IDX 0 +#define OFFROAD_IDX 1 +#define RED_LIGHT_IDX 2 +#define STOP_SIGN_IDX 3 +#define REACHED_GOAL_IDX 4 +#define LANE_DIST_IDX 5 +#define LANE_ANGLE_IDX 6 +#define COMFORT_VIOLATION_IDX 7 +#define VELOCITY_PROGRESS_IDX 8 +#define SPEED_LIMIT_IDX 9 +#define AVG_DISPLACEMENT_ERROR_IDX 10 +#define PROGRESSION_IDX 11 +// Evaluation metrics +#define AT_FAULT_COLLISION_IDX 12 +#define TTC_IDX 13 +#define TTC_TFL_IDX 14 +#define PROGRESS_RATIO_IDX 15 +#define MULTI_LANE_TIME_IDX 16 +#define MULTI_LANE_SCORE_IDX 17 + +// Path +#define MAX_NUM_WP_PATH 200 +#define MAX_TARGET_WAYPOINTS 20 + +struct Waypoint { + float s; // Arc length (cumulative distance from the start) - init position + float x; // Global x-coordinate + float y; // Global y-coordinate + float z; // Global z-coordinate + float heading; // Global heading (tangent angle) in radians + float cos_heading; // Cached cosf(heading) - set in build_path + float sin_heading; // Cached sinf(heading) - set in build_path + float kappa; // Curvature at this point + int lane_id; // Lane id of the waypoint +}; +struct Path { + struct Waypoint waypoints[MAX_NUM_WP_PATH]; + int num_waypoints; +}; + +struct ttc_result { + float min_ttc; + int other_idx; + float distance_to_collision; + float closing_speed; +}; + +static inline int is_road_lane(int type) { return (type >= 1 && type <= 9); } + +static inline int is_drivable_road_lane(int type) { return (type == LANE_FREEWAY || type == LANE_SURFACE_STREET); } + +static inline int is_road_line(int type) { return (type >= 10 && type <= 19); } + +static inline int is_road_edge(int type) { return (type >= 20 && type <= 29); } + +static inline int is_road(int type) { return is_road_lane(type) || is_road_line(type) || is_road_edge(type); } + +static inline int is_controllable_agent(int type) { return (type == VEHICLE || type == PEDESTRIAN || type == CYCLIST); } + +static inline int is_traffic_light_red(int state) { return state == 1 || state == TL_STATE_RED || state == 7; } + +static inline int is_traffic_light_green(int state) { return state == TL_STATE_GREEN || state == 6 || state == 9; } + +static inline int is_traffic_light_yellow(int state) { return state == TL_STATE_YELLOW || state == 5 || state == 8; } + +static inline int normalize_road_type(int type) { + if (is_road_lane(type)) { + return 0; + } else if (is_road_line(type)) { + return 1; + } else if (is_road_edge(type)) { + return 2; + } else { + return -1; + } +} + +static inline int unnormalize_road_type(int norm_type) { + if (norm_type == 0) { + return LANE_SURFACE_STREET; + } else if (norm_type == 1) { + return ROAD_LINE_BROKEN_SINGLE_WHITE; + } else if (norm_type == 2) { + return ROAD_EDGE_BOUNDARY; + } else { + return -1; // Invalid + } +} + +static inline int normalize_traffic_light_state(int state) { + if (is_traffic_light_red(state)) { + return 1; + } else if (is_traffic_light_yellow(state)) { + return 2; + } else if (is_traffic_light_green(state)) { + return 3; + } else { + return TL_STATE_DISABLED; // Invalid or disabled + } +} + +static inline int unnormalize_traffic_light_state(int norm_state) { + if (norm_state == 1) { + return TL_STATE_RED; + } else if (norm_state == 2) { + return TL_STATE_YELLOW; + } else if (norm_state == 3) { + return TL_STATE_GREEN; + } else { + return TL_STATE_DISABLED; // Invalid or disabled + } +} + +struct Agent { + int id; + int type; + + // Log trajectory + int trajectory_length; + float *log_trajectory_x; + float *log_trajectory_y; + float *log_trajectory_z; + float *log_heading; + float *log_velocity_x; + float *log_velocity_y; + float *log_length; + float *log_width; + float *log_height; + int *log_valid; + + // Simulation state (always center-based, even when trajectory control uses the rear axle) + float sim_x; // Vehicle center x-coordinate + float sim_y; // Vehicle center y-coordinate + float sim_z; + float sim_heading; + float cos_heading; // Cached cosf(sim_heading) - updated in move_dynamics + float sin_heading; // Cached sinf(sim_heading) - updated in move_dynamics + float sim_vx; // Vehicle center velocity x-component + float sim_vy; // Vehicle center velocity y-component + float yaw_rate; // Angular velocity used to derive rear-axle velocity + float sim_speed; + float sim_speed_signed; + float sim_length; + float sim_width; + float sim_height; + int sim_valid; + + // Route information + int route_length; + int *route; + int route_gt_len; // Number of leading route lanes supported by GT before extension + int current_route_index; // Tracks progress through route array + + // Path + struct Path *path; + int closest_path_idx_wp; + + // Metrics and status tracking (size must match NUM_METRICS in drive.h) + float metrics_array[NUM_METRICS]; // [collision, offroad, red_light, stop_sign, reached_goal, lane_dist, lane_angle, + // comfort_violation, velocity_progress, speed_limit, avg_displacement_error, + // progression, at_fault_collision, ttc, ttc_tfl, progress_ratio, + // multi_lane_time, multi_lane_score] + int current_lane_index; + int previous_lane_index; + int current_lane_geometry_idx; + int reached_goal_this_episode; + int num_waypoints_reached; + int num_goals_reached; + int active_agent; + int mark_as_expert; + float cumulative_displacement; + int displacement_sample_count; + float path_progression; + float distance_since_spawn; + + // Goal positions (N sequential waypoints) + float goal_positions_x[MAX_TARGET_WAYPOINTS]; + float goal_positions_y[MAX_TARGET_WAYPOINTS]; + float goal_positions_z[MAX_TARGET_WAYPOINTS]; + float goal_position_x; // alias = goal_positions_x[current_goal_idx] + float goal_position_y; // alias = goal_positions_y[current_goal_idx] + float goal_position_z; // alias = goal_positions_z[current_goal_idx] + int current_goal_idx; // index of next goal to reach (0..N-1) + + int stopped; // 0/1 -> freeze if set + int removed; // 0/1 -> remove from sim if set + + // Jerk dynamics + float a_long; + float a_lat; + float jerk_long; + float jerk_lat; + float steering_angle; + float wheelbase; + + // Reward conditioning coefficients (per-agent, randomized at spawn) + float reward_coefs[NUM_REWARD_COEFS]; + + // Puffer score tracking (per-episode accumulators) + struct ttc_result cached_ttc; // Filled once per step before reward computation + float wrong_way_distance; // Accumulated wrong-way distance + float speed_violation_sum; // For nuPlan speed compliance formula + int ttc_violations; // Count of TTC < 0.95s violations + int ttc_samples; // Total TTC samples for rate + int at_fault_collision; // 1 if at-fault collision occurred this episode + float multi_lane_time; // Accumulated time (s) on multiple lanes +}; + +struct RoadMapElement { + int id; + int type; + + int segment_length; + float *x; + float *y; + float *z; + float *headings; // Pre-computed heading for each segment + + // Lane specific info + int num_entries; + int *entry_lanes; + int num_exits; + int *exit_lanes; + float speed_limit; +}; + +struct TrafficControlElement { + int id; + int type; + + int state_length; + int *states; + float stop_line[6]; // Two 3D endpoints: [x1,y1,z1, x2,y2,z2] + float heading; + int num_controlled_lanes; + int *controlled_lanes; +}; + +typedef struct { + float z_dis; + float euclidean_dis; + float z; +} DepthPoint; + +struct LaneGraph { + int n_lanes; + int *lane_ids; + float *lane_lengths; + float *distances; // n_lanes * n_lanes row-major +}; + +void free_agent(struct Agent *agent) { + free(agent->log_trajectory_x); + free(agent->log_trajectory_y); + free(agent->log_trajectory_z); + free(agent->log_heading); + free(agent->log_velocity_x); + free(agent->log_velocity_y); + free(agent->log_length); + free(agent->log_width); + free(agent->log_height); + free(agent->log_valid); + free(agent->route); + free(agent->path); +} + +void free_road_element(struct RoadMapElement *element) { + free(element->x); + free(element->y); + free(element->z); + free(element->headings); + free(element->entry_lanes); + free(element->exit_lanes); +} + +void free_traffic_element(struct TrafficControlElement *element) { + free(element->states); + free(element->controlled_lanes); +} + +void free_lane_graph(struct LaneGraph *graph) { + free(graph->lane_ids); + free(graph->lane_lengths); + free(graph->distances); +} diff --git a/pufferlib/ocean/drive/drive.c b/pufferlib/ocean/drive/drive.c index 7a714ac4a1..f99c4b0a2e 100644 --- a/pufferlib/ocean/drive/drive.c +++ b/pufferlib/ocean/drive/drive.c @@ -1,8 +1,7 @@ +#include "drive.h" #include "drivenet.h" -#include "error.h" -#include "libgen.h" -#include "../env_config.h" #include +#include "../env_config.h" // Use this test if the network changes to ensure that the forward pass // matches the torch implementation to the 3rd or ideally 4th decimal place @@ -34,122 +33,76 @@ void test_drivenet() { free(weights); } -int demo(const char *map_name, const char *policy_name, int show_grid, int obs_only, int lasers, int show_human_logs, - int frame_skip, const char *view_mode, const char *output_topdown, const char *output_agent, int num_maps, - int zoom_in) { - - // Parse configuration from INI file +void demo() { + // Read configuration from INI file env_init_config conf = {0}; const char *ini_file = "pufferlib/config/ocean/drive.ini"; - if (ini_parse(ini_file, handler, &conf) < 0) { + if (load_env_config(ini_file, &conf) < 0) { fprintf(stderr, "Error: Could not load %s. Cannot determine environment configuration.\n", ini_file); - return -1; - } - - char map_buffer[100]; - if (map_name == NULL) { - srand(time(NULL)); - int random_map = rand() % num_maps; - sprintf(map_buffer, "%s/map_%03d.bin", conf.map_dir, random_map); - map_name = map_buffer; + exit(1); } - // Initialize environment with all config values from INI [env] section Drive env = { - .action_type = conf.action_type, + .human_agent_idx = 0, .dynamics_model = conf.dynamics_model, .reward_vehicle_collision = conf.reward_vehicle_collision, .reward_offroad_collision = conf.reward_offroad_collision, - .reward_goal = conf.reward_goal, - .reward_goal_post_respawn = conf.reward_goal_post_respawn, + .reward_ade = conf.reward_ade, .goal_radius = conf.goal_radius, - .goal_behavior = conf.goal_behavior, - .goal_target_distance = conf.goal_target_distance, - .goal_speed = conf.goal_speed, .dt = conf.dt, - .episode_length = conf.episode_length, - .termination_mode = conf.termination_mode, + .map_name = "resources/drive/binaries/map_000.bin", + .init_steps = conf.init_steps, .collision_behavior = conf.collision_behavior, .offroad_behavior = conf.offroad_behavior, - .init_steps = conf.init_steps, - .init_mode = conf.init_mode, - .control_mode = conf.control_mode, - .map_name = (char *)map_name, + .compute_eval_metrics = conf.compute_eval_metrics, }; allocate(&env); - if (env.active_agent_count == 0) { - fprintf(stderr, "Error: No active agents found in map '%s' with init_mode=%d. Cannot run demo.\n", env.map_name, - conf.init_mode); - free_allocated(&env); - return -1; - } c_reset(&env); c_render(&env); - Weights *weights = load_weights((char *)policy_name); + Weights *weights = load_weights("resources/drive/puffer_drive_weights.bin"); DriveNet *net = init_drivenet(weights, env.active_agent_count, env.dynamics_model); - + // Client* client = make_client(&env); int accel_delta = 2; int steer_delta = 4; while (!WindowShouldClose()) { - int *actions = (int *)env.actions; // Single integer per agent - - forward(net, env.observations, actions); - + // Handle camera controls + int (*actions)[2] = (int (*)[2])env.actions; + forward(net, env.observations, env.actions); if (IsKeyDown(KEY_LEFT_SHIFT)) { - if (env.dynamics_model == CLASSIC) { - // Classic dynamics: acceleration and steering - int accel_idx = 3; // neutral (0 m/s²) - int steer_idx = 6; // neutral (0.0 steering) - - if (IsKeyDown(KEY_UP) || IsKeyDown(KEY_W)) { - accel_idx += accel_delta; - if (accel_idx > 6) - accel_idx = 6; - } - if (IsKeyDown(KEY_DOWN) || IsKeyDown(KEY_S)) { - accel_idx -= accel_delta; - if (accel_idx < 0) - accel_idx = 0; - } - if (IsKeyDown(KEY_LEFT) || IsKeyDown(KEY_A)) { - steer_idx += steer_delta; // Increase steering index for left turn - if (steer_idx > 12) - steer_idx = 12; + actions[env.human_agent_idx][0] = 3; + actions[env.human_agent_idx][1] = 6; + if (IsKeyDown(KEY_UP) || IsKeyDown(KEY_W)) { + actions[env.human_agent_idx][0] += accel_delta; + // Cap acceleration to maximum of 6 + if (actions[env.human_agent_idx][0] > 6) { + actions[env.human_agent_idx][0] = 6; } - if (IsKeyDown(KEY_RIGHT) || IsKeyDown(KEY_D)) { - steer_idx -= steer_delta; // Decrease steering index for right turn - if (steer_idx < 0) - steer_idx = 0; - } - - // Encode into single integer: action = accel_idx * 13 + steer_idx - actions[env.human_agent_idx] = accel_idx * 13 + steer_idx; - - } else if (env.dynamics_model == JERK) { - // Jerk dynamics: longitudinal and lateral jerk - // JERK_LONG[4] = {-15.0f, -4.0f, 0.0f, 4.0f} - // JERK_LAT[3] = {-4.0f, 0.0f, 4.0f} - int jerk_long_idx = 2; // neutral (0.0) - int jerk_lat_idx = 1; // neutral (0.0) - - if (IsKeyDown(KEY_UP) || IsKeyDown(KEY_W)) { - jerk_long_idx = 3; // acceleration (4.0) - } - if (IsKeyDown(KEY_DOWN) || IsKeyDown(KEY_S)) { - jerk_long_idx = 0; // hard braking (-15.0) + } + if (IsKeyDown(KEY_DOWN) || IsKeyDown(KEY_S)) { + actions[env.human_agent_idx][0] -= accel_delta; + // Cap acceleration to minimum of 0 + if (actions[env.human_agent_idx][0] < 0) { + actions[env.human_agent_idx][0] = 0; } - if (IsKeyDown(KEY_LEFT) || IsKeyDown(KEY_A)) { - jerk_lat_idx = 2; // left turn (4.0) + } + if (IsKeyDown(KEY_LEFT) || IsKeyDown(KEY_A)) { + actions[env.human_agent_idx][1] += steer_delta; + // Cap steering to minimum of 0 + if (actions[env.human_agent_idx][1] < 0) { + actions[env.human_agent_idx][1] = 0; } - if (IsKeyDown(KEY_RIGHT) || IsKeyDown(KEY_D)) { - jerk_lat_idx = 0; // right turn (-4.0) + } + if (IsKeyDown(KEY_RIGHT) || IsKeyDown(KEY_D)) { + actions[env.human_agent_idx][1] -= steer_delta; + // Cap steering to maximum of 12 + if (actions[env.human_agent_idx][1] > 12) { + actions[env.human_agent_idx][1] = 12; } - - // Encode into single integer: action = jerk_long_idx * 3 + jerk_lat_idx - actions[env.human_agent_idx] = jerk_long_idx * 3 + jerk_lat_idx; + } + if (IsKeyPressed(KEY_TAB)) { + env.human_agent_idx = (env.human_agent_idx + 1) % env.active_agent_count; } } - c_step(&env); c_render(&env); } @@ -158,137 +111,114 @@ int demo(const char *map_name, const char *policy_name, int show_grid, int obs_o free_allocated(&env); free_drivenet(net); free(weights); - return 0; } void performance_test() { + // Read configuration from INI file + env_init_config conf = {0}; + const char *ini_file = "pufferlib/config/ocean/drive.ini"; + if (load_env_config(ini_file, &conf) < 0) { + fprintf(stderr, "Error: Could not load %s. Cannot determine environment configuration.\n", ini_file); + exit(1); + } long test_time = 10; Drive env = { .human_agent_idx = 0, - .dynamics_model = CLASSIC, // Classic dynamics - .action_type = 0, // Discrete - .map_name = "resources/drive/binaries/map_000.bin", - .dt = 0.1f, - .init_steps = 0, + .map_name = strdup("pufferlib/resources/drive/binaries/carla/map_000.bin"), + .ini_file = strdup(ini_file), + .num_controllable_agents = conf.max_agents_per_env, + // From conf + .action_type = conf.action_type, + .dynamics_model = conf.dynamics_model, + .reward_vehicle_collision = conf.reward_vehicle_collision, + .reward_offroad_collision = conf.reward_offroad_collision, + .reward_traffic_light_violation = conf.reward_traffic_light_violation, + .reward_goal = conf.reward_goal, + .reward_ade = conf.reward_ade, + .reward_overspeed = conf.reward_overspeed, + .reward_comfort = conf.reward_comfort, + .reward_velocity = conf.reward_velocity, + .reward_lane_align = conf.reward_lane_align, + .reward_vel_align = conf.reward_vel_align, + .reward_lane_center = conf.reward_lane_center, + .reward_center_bias = conf.reward_center_bias, + .reward_reverse = conf.reward_reverse, + .reward_timestep = conf.reward_timestep, + .goal_radius = conf.goal_radius, + .collision_behavior = conf.collision_behavior, + .offroad_behavior = conf.offroad_behavior, + .traffic_light_behavior = conf.traffic_light_behavior, + .dt = conf.dt, + .target_type = conf.target_type, + .scenario_length = conf.scenario_length, + .termination_mode = conf.termination_mode, + .init_steps = conf.init_steps, + .init_mode = conf.init_mode, + .control_mode = conf.control_mode, + .simulation_mode = conf.simulation_mode, + .min_waypoint_spacing = conf.min_waypoint_spacing, + .max_waypoint_spacing = conf.max_waypoint_spacing, + .num_target_waypoints = conf.num_target_waypoints, + .reward_conditioning = conf.reward_conditioning, + .reward_randomization = conf.reward_randomization, + .compute_eval_metrics = conf.compute_eval_metrics, + .num_max_agents = conf.max_agents_per_env, + .use_rear_axle = conf.use_rear_axle, + .max_lane_segment_observations = conf.max_lane_segment_observations, + .max_boundary_segment_observations = conf.max_boundary_segment_observations, + .max_partner_observations = conf.max_partner_observations, + .max_traffic_light_observations = conf.max_traffic_light_observations, + .max_stop_sign_observations = conf.max_stop_sign_observations, }; - clock_t start_time, end_time; - double cpu_time_used; - start_time = clock(); + struct timespec ts_total_start, ts_total_end; + struct timespec ts_init_start, ts_init_end; + struct timespec ts_step_start, ts_step_end; + double init_time = 0, step_time = 0, total_time = 0; + + clock_gettime(CLOCK_MONOTONIC, &ts_total_start); + + clock_gettime(CLOCK_MONOTONIC, &ts_init_start); allocate(&env); c_reset(&env); - end_time = clock(); - cpu_time_used = ((double)(end_time - start_time)) / CLOCKS_PER_SEC; - printf("Init time: %f\n", cpu_time_used); + clock_gettime(CLOCK_MONOTONIC, &ts_init_end); + init_time = (ts_init_end.tv_sec - ts_init_start.tv_sec) + (ts_init_end.tv_nsec - ts_init_start.tv_nsec) / 1e9; + printf("Init time: %.4f s\n", init_time); long start = time(NULL); int i = 0; - int (*actions)[2] = (int (*)[2])env.actions; + // Reallocate actions buffer for trajectory mode (needs num_trajectory_scaling_factors per agent) + + clock_gettime(CLOCK_MONOTONIC, &ts_step_start); while (time(NULL) - start < test_time) { - // Set random actions for all agents + // Set random discrete actions for all agents + int (*actions)[2] = (int (*)[2])env.actions; for (int j = 0; j < env.active_agent_count; j++) { - int accel = rand() % 7; - int steer = rand() % 13; - actions[j][0] = accel; // -1, 0, or 1 - actions[j][1] = steer; // Random steering + actions[j][0] = rand() % 7; + actions[j][1] = rand() % 13; } - c_step(&env); i++; } + clock_gettime(CLOCK_MONOTONIC, &ts_step_end); + step_time = (ts_step_end.tv_sec - ts_step_start.tv_sec) + (ts_step_end.tv_nsec - ts_step_start.tv_nsec) / 1e9; + long end = time(NULL); + printf("Steps: %d | Agents: %d\n", i, env.active_agent_count); + printf("Step loop time: %.4f s\n", step_time); printf("SPS: %ld\n", (i * env.active_agent_count) / (end - start)); - free_allocated(&env); -} - -int main(int argc, char *argv[]) { - // Visualization-only parameters (not in [env] section) - int show_grid = 0; - int obs_only = 0; - int lasers = 0; - int show_human_logs = 0; - int frame_skip = 1; - int zoom_in = 0; - const char *view_mode = "both"; - // File paths and num_maps (not in [env] section) - const char *map_name = NULL; - const char *policy_name = "resources/drive/puffer_drive_weights.bin"; - const char *output_topdown = NULL; - const char *output_agent = NULL; - int num_maps = 1; + free_allocated(&env); - // Parse command line arguments - for (int i = 1; i < argc; i++) { - if (strcmp(argv[i], "--show-grid") == 0) { - show_grid = 1; - } else if (strcmp(argv[i], "--obs-only") == 0) { - obs_only = 1; - } else if (strcmp(argv[i], "--lasers") == 0) { - lasers = 1; - } else if (strcmp(argv[i], "--log-trajectories") == 0) { - show_human_logs = 1; - } else if (strcmp(argv[i], "--frame-skip") == 0) { - if (i + 1 < argc) { - frame_skip = atoi(argv[i + 1]); - i++; - if (frame_skip <= 0) { - frame_skip = 1; - } - } - } else if (strcmp(argv[i], "--zoom-in") == 0) { - zoom_in = 1; - } else if (strcmp(argv[i], "--view") == 0) { - if (i + 1 < argc) { - view_mode = argv[i + 1]; - i++; - if (strcmp(view_mode, "both") != 0 && strcmp(view_mode, "topdown") != 0 && - strcmp(view_mode, "agent") != 0) { - fprintf(stderr, "Error: --view must be 'both', 'topdown', or 'agent'\n"); - return 1; - } - } else { - fprintf(stderr, "Error: --view option requires a value (both/topdown/agent)\n"); - return 1; - } - } else if (strcmp(argv[i], "--map-name") == 0) { - if (i + 1 < argc) { - map_name = argv[i + 1]; - i++; - } else { - fprintf(stderr, "Error: --map-name option requires a map file path\n"); - return 1; - } - } else if (strcmp(argv[i], "--policy-name") == 0) { - if (i + 1 < argc) { - policy_name = argv[i + 1]; - i++; - } else { - fprintf(stderr, "Error: --policy-name option requires a policy file path\n"); - return 1; - } - } else if (strcmp(argv[i], "--output-topdown") == 0) { - if (i + 1 < argc) { - output_topdown = argv[i + 1]; - i++; - } - } else if (strcmp(argv[i], "--output-agent") == 0) { - if (i + 1 < argc) { - output_agent = argv[i + 1]; - i++; - } - } else if (strcmp(argv[i], "--num-maps") == 0) { - if (i + 1 < argc) { - num_maps = atoi(argv[i + 1]); - i++; - } - } - } + clock_gettime(CLOCK_MONOTONIC, &ts_total_end); + total_time = (ts_total_end.tv_sec - ts_total_start.tv_sec) + (ts_total_end.tv_nsec - ts_total_start.tv_nsec) / 1e9; + printf("Total time: %.4f s\n", total_time); +} - // performance_test(); - demo(map_name, policy_name, show_grid, obs_only, lasers, show_human_logs, frame_skip, view_mode, output_topdown, - output_agent, num_maps, zoom_in); +int main() { + performance_test(); + // demo(); // test_drivenet(); return 0; } diff --git a/pufferlib/ocean/drive/drive.h b/pufferlib/ocean/drive/drive.h index 2a55867120..3ac55a5905 100644 --- a/pufferlib/ocean/drive/drive.h +++ b/pufferlib/ocean/drive/drive.h @@ -1,5 +1,3 @@ -#include -#include #include #include #include @@ -13,40 +11,10 @@ #include "rlgl.h" #include #include "error.h" - -// Render modes -#define RENDER_WINDOW 0 -#define RENDER_HEADLESS 1 - -// View modes -#define VIEW_MODE_SIM_STATE 0 -#define VIEW_MODE_BEV_AGENT_OBS 1 -#define VIEW_MODE_AGENT_PERSP 2 - -// Order of entities in rendering (lower is rendered first) -#define Z_ROAD_SURFACE 0.0f -#define Z_ROAD_MARKINGS 0.05f // Lane lines, road lines, traces -#define Z_AGENT_DETAILS 0.4f // Arrow, goal markers, obs overlays -#define Z_AGENTS 0.6f // Vehicles, cyclists, pedestrians - -// Entity Types -#define NONE 0 -#define VEHICLE 1 -#define PEDESTRIAN 2 -#define CYCLIST 3 -#define ROAD_LANE 4 -#define ROAD_LINE 5 -#define ROAD_EDGE 6 -#define STOP_SIGN 7 -#define CROSSWALK 8 -#define SPEED_BUMP 9 -#define DRIVEWAY 10 +#include "datatypes.h" #define INVALID_POSITION -10000.0f -// Trajectory Length -#define TRAJECTORY_LENGTH 91 - // Initialization modes #define INIT_ALL_VALID 0 #define INIT_ONLY_CONTROLLABLE_AGENTS 1 @@ -56,72 +24,106 @@ #define CONTROL_AGENTS 1 #define CONTROL_WOSAC 2 #define CONTROL_SDC_ONLY 3 -#define CONTROL_MIXED_PLAY 4 - -// Minimum distance to goal position -#define MIN_DISTANCE_TO_GOAL 2.0f - -// Actions -#define NOOP 0 -// Dynamics Models -#define CLASSIC 0 -#define JERK 1 +// Simulation modes +#define SIMULATION_GIGAFLOW 0 +#define SIMULATION_REPLAY 1 + +// Lane selection scoring +#define LANE_SELECTION_DISTANCE_WEIGHT 0.7f +#define LANE_SELECTION_HEADING_WEIGHT 0.3f +#define LANE_DISTANCE_NORMALIZATION 4.0f +#define LANE_SWITCH_THRESHOLD 0.05f // Hysteresis: new lane must be 5% better to switch +#define LANE_ALIGN_COS_THRESHOLD 0.5f + +// Collision and distance thresholds +#define MAX_CHECKED_LANES 32 +#define COLLISION_QUICK_CHECK_DIST 15.0f // Quick distance check before OBB SAT +#define INIT_COLLISION_SHRINK_FACTOR 0.7f // Shrink agent dims at init to prevent collisions +#define AGENT_STOPPED_SPEED_THRESHOLD 0.2f +#define TRAFFIC_LIGHT_DISTANCE_THRESHOLD 10.0f +#define STOP_LINE_EXTENSION_FACTOR 1.5f +#define RED_LIGHT_HEADING_THRESHOLD (M_PI / 4.0f) + +// TTC default value when no vehicle ahead +#define DEFAULT_TTC 5.0f +// TTC violation threshold for "within bound" rate +#define TTC_VIOLATION_THRESHOLD 0.95f +// Multi-lane detection thresholds +#define LANE_WIDTH 3.7f +#define LANE_MARGIN 0.2f +#define MULTI_LANE_THRESHOLD (LANE_WIDTH / 2.0f + LANE_MARGIN) // 2.05m +#define MULTI_LANE_FULL_SCORE_TIME 3.4f // seconds +#define MULTI_LANE_HALF_SCORE_TIME 5.7f // seconds // Collision state #define NO_COLLISION 0 #define VEHICLE_COLLISION 1 #define OFFROAD 2 +#define TRAFFIC_LIGHT_VIOLATION 3 -// Metrics array indices -#define COLLISION_IDX 0 -#define OFFROAD_IDX 1 -#define REACHED_GOAL_IDX 2 -#define LANE_ALIGNED_IDX 3 +// Collision/Infraction behaviors +#define STOP_AGENT 1 +#define REMOVE_AGENT 2 // Grid cell size #define GRID_CELL_SIZE 5.0f -#define MAX_ENTITIES_PER_CELL \ - 30 // Depends on resolution of data Formula: 3 * (2 + GRID_CELL_SIZE*sqrt(2)/resolution) => For each entity type in - // gridmap, diagonal poly-lines -> sqrt(2), include diagonal ends -> 2 - -// Observation constants -#define MAX_ROAD_SEGMENT_OBSERVATIONS 128 - -// Maximum number of agents per scene -#ifndef MAX_AGENTS -#define MAX_AGENTS 32 -#endif -#define STOP_AGENT 1 -#define REMOVE_AGENT 2 +// Depends on resolution of data Formula: 3 * (2 + GRID_CELL_SIZE*sqrt(2)/resolution) +// => For each entity type in gridmap, diagonal poly-lines -> sqrt(2), include diagonal ends -> 2 +#define MAX_ENTITIES_PER_CELL 30 +// GridMapEntity types +#define ENTITY_TYPE_ROAD_ELEMENT 1 +#define ENTITY_TYPE_TRAFFIC_CONTROL 2 + +// Max road segment observation +#define ROAD_OBS_FRONT_DIST 100.0f +#define ROAD_OBS_BEHIND_DIST 20.0f +#define ROAD_OBS_SIDE_DIST 40.0f +#define VISION_HALF_RANGE \ + ((int)ceilf(fmaxf(fmaxf(ROAD_OBS_FRONT_DIST, ROAD_OBS_BEHIND_DIST), ROAD_OBS_SIDE_DIST) / GRID_CELL_SIZE)) + +// Traffic light observations +#define MAX_TRAFFIC_LIGHT_DISTANCE 100.0f + +// Agent observations +#define AGENT_OBS_MAX_DIST 100.0f // Max distance for observing other agents + +// TARGET_TYPE modes (controls what target info is in observations) +#define TARGET_STATIC 0 +#define TARGET_DYNAMIC 1 +// Observation normalization constants +#define MAX_GOAL_POSITION 100.0f // Goal position scaling (m) +#define MAX_POSITION 100.0f // Max position for normalization (m) +#define MAX_SPEED 40.0f // Max speed for normalization (m/s) +#define MAX_VEH_LEN 15.0f // Max vehicle length for normalization (m) +#define MAX_VEH_WIDTH 10.0f // Max vehicle width for normalization (m) +#define MAX_ROAD_SCALE 100.0f // Max road feature scaling (m) +#define MAX_ROAD_SEGMENT_LENGTH 100.0f // Max road segment length (m) + +// Observation feature counts +#define EGO_FEATURES_CLASSIC 7 +#define EGO_FEATURES_JERK 9 #define ROAD_FEATURES 7 -#define ROAD_FEATURES_ONEHOT 13 -#define PARTNER_FEATURES 7 +#define PARTNER_FEATURES 8 +#define TRAFFIC_LIGHT_FEATURES 6 +#define STOP_SIGN_FEATURES 3 +#define STATIC_TARGET_FEATURES 3 +#define DYNAMIC_TARGET_FEATURES 5 + +// GIGAFLOW specific +#define MAX_ROUTE_LENGTH 64 +// Traffic light generation +#define TL_DEFAULT_RED_DURATION 2.0f +#define TL_DEFAULT_YELLOW_DURATION 3.0f +#define TL_DEFAULT_GREEN_DURATION 10.0f +#define TL_EPISODE_DISABLE_PROB 0.20f +#define TL_INDIVIDUAL_REMOVE_PROB 0.20f +#define TL_ALWAYS_GREEN_PROB 0.05f -// Ego features depend on dynamics model -#define EGO_FEATURES_CLASSIC 8 -#define EGO_FEATURES_JERK 11 - -// Observation normalization constants -#define MAX_SPEED 100.0f -#define MAX_VEH_LEN 30.0f -#define MAX_VEH_WIDTH 15.0f -#define MAX_VEH_HEIGHT 10.0f -#define MIN_REL_GOAL_COORD -1000.0f -#define MAX_REL_GOAL_COORD 1000.0f -#define MIN_REL_AGENT_POS -1000.0f -#define MAX_REL_AGENT_POS 1000.0f -#define MAX_ORIENTATION_RAD 2 * PI -#define MIN_RG_COORD -1000.0f -#define MAX_RG_COORD 1000.0f -#define MAX_ROAD_SCALE 100.0f -#define MAX_ROAD_SEGMENT_LENGTH 100.0f - -// Goal behavior -#define GOAL_RESPAWN 0 -#define GOAL_GENERATE_NEW 1 -#define GOAL_STOP 2 +// Dynamics Models +#define CLASSIC 0 +#define JERK 1 // Jerk action space (for JERK dynamics model) static const float JERK_LONG[4] = {-15.0f, -4.0f, 0.0f, 4.0f}; @@ -129,8 +131,7 @@ static const float JERK_LAT[3] = {-4.0f, 0.0f, 4.0f}; // Classic action space (for CLASSIC dynamics model) static const float ACCELERATION_VALUES[7] = {-4.0000f, -2.6670f, -1.3330f, -0.0000f, 1.3330f, 2.6670f, 4.0000f}; -static const float STEERING_VALUES[13] = {-1.000f, -0.833f, -0.667f, -0.500f, -0.333f, -0.167f, 0.000f, - 0.167f, 0.333f, 0.500f, 0.667f, 0.833f, 1.000f}; +static const float STEERING_VALUES[9] = {-0.667f, -0.500f, -0.333f, -0.167f, 0.000f, 0.167f, 0.333f, 0.500f, 0.667f}; static const float offsets[4][2] = { {-1, 1}, // top-left @@ -147,143 +148,65 @@ static const int collision_offsets[25][2] = { {-2, 2}, {-1, 2}, {0, 2}, {1, 2}, {2, 2} // Bottom row }; -const Color STONE_GRAY = (Color){80, 80, 80, 255}; -const Color PUFF_RED = (Color){187, 0, 0, 255}; -const Color PUFF_CYAN = (Color){0, 187, 187, 255}; -const Color PUFF_WHITE = (Color){241, 241, 241, 241}; -const Color PUFF_BACKGROUND = (Color){6, 24, 24, 255}; -const Color PUFF_BACKGROUND2 = (Color){18, 72, 72, 255}; -const Color LIGHTGREEN = (Color){152, 255, 152, 255}; -const Color LIGHTYELLOW = (Color){255, 255, 152, 255}; -const Color SOFT_YELLOW = (Color){245, 245, 220, 255}; -const Color ROAD_COLOR = (Color){35, 35, 37, 255}; -const Color LIGHTBLUE = (Color){167, 204, 255, 255}; -const Color DEEPBLUE = (Color){45, 112, 226, 255}; -const Color EXPERT_REPLAY = (Color){162, 220, 183, 255}; -const Color EXPERT_REPLAY_SMALL = (Color){95, 112, 93, 255}; -const Color LIGHT_ORANGE = (Color){255, 160, 80, 255}; -const Color LIGHT_PURPLE = (Color){204, 204, 255, 255}; - -struct timespec ts; +#define Z_COMPUTATION_OFFSET_COUNT 9 +#define Z_BUFFER 4.0f +#define Z_NUM_PT_AVG 30 + +static const int z_computation_offsets[Z_COMPUTATION_OFFSET_COUNT][2] = { + {-1, -1}, {0, -1}, {1, -1}, {-1, 0}, {0, 0}, {1, 0}, {-1, 1}, {0, 1}, {1, 1}, +}; typedef struct Drive Drive; typedef struct Client Client; typedef struct Log Log; +typedef struct Agent Agent; +typedef struct RoadMapElement RoadMapElement; +typedef struct TrafficControlElement TrafficControlElement; struct Log { + float n; float episode_return; float episode_length; + float expert_static_car_count; + float static_car_count; float score; - float goals_reached_this_episode; - float goals_sampled_this_episode; float offroad_rate; float collision_rate; - float completion_rate; - float offroad_per_agent; - float collisions_per_agent; + float red_light_violation_rate; + float num_waypoints_reached; + float num_goals_reached; + float comfort_violation_count; + float velocity_progress_sum; + float lane_center_rate; + float lane_heading_aligned_rate; float dnf_rate; - float n; - float lane_alignment_rate; - float speed_at_goal; - float active_agent_count; - float expert_static_agent_count; - float static_agent_count; - float perc_controlled; - float perc_other; -}; - -typedef struct Entity Entity; -struct Entity { - int scenario_id; - int type; - int id; - int array_size; - float *traj_x; - float *traj_y; - float *traj_z; - float *traj_vx; - float *traj_vy; - float *traj_vz; - float *traj_heading; - int *traj_valid; - float width; - float length; - float height; - float goal_position_x; - float goal_position_y; - float goal_position_z; - float init_goal_x; - float init_goal_y; - int mark_as_expert; - int collision_state; - float metrics_array[5]; // metrics_array: [collision, offroad, reached_goal, lane_aligned - float x; - float y; - float z; - float vx; - float vy; - float vz; - float heading; - float heading_x; - float heading_y; - int current_lane_idx; - int valid; - int respawn_timestep; - int respawn_count; - int collided_before_goal; - float goals_reached_this_episode; - float goals_sampled_this_episode; - int current_goal_reached; - int active_agent; - int stopped; - int removed; - - // Jerk dynamics - float a_long; - float a_lat; - float jerk_long; - float jerk_lat; - float steering_angle; - float wheelbase; + float avg_displacement_error; + float avg_speed_per_agent; + // Puffer score components + float at_fault_collision_rate; + float ttc_within_bound_rate; + float driving_direction_score; + float speed_limit_compliance; + float making_progress_rate; + float progress_ratio; + float comfort_score; + float puffer_score; + // Puffer score intermediate accumulators (for aggregation) + float wrong_way_distance; + float speed_violation_sum; + float ttc_violations; + float ttc_samples; + float multi_lane_time; + float multi_lane_score; + float total_distance_travelled; + float total_infractions; }; -void free_entity(Entity *entity) { - // free trajectory arrays - free(entity->traj_x); - free(entity->traj_y); - free(entity->traj_z); - free(entity->traj_vx); - free(entity->traj_vy); - free(entity->traj_vz); - free(entity->traj_heading); - free(entity->traj_valid); -} - -// Utility functions -float relative_distance(float a, float b) { - float distance = sqrtf(powf(a - b, 2)); - return distance; -} - -float relative_distance_2d(float x1, float y1, float x2, float y2) { - float dx = x2 - x1; - float dy = y2 - y1; - float distance = sqrtf(dx * dx + dy * dy); - return distance; -} - -float clip(float value, float min, float max) { - if (value < min) - return min; - if (value > max) - return max; - return value; -} - typedef struct GridMapEntity GridMapEntity; struct GridMapEntity { - int entity_idx; - int geometry_idx; + int entity_type; // Entity type: 1=Agent, 2=RoadMapElement, 3=TrafficControlElement + int entity_idx; // Index into the corresponding typed array + int geometry_idx; // Index into entity's trajectory/geometry array }; typedef struct GridMap GridMap; @@ -302,6 +225,8 @@ struct GridMap { int vision_range; int *neighbor_cache_count; // number of entities in each cells neighbor cache GridMapEntity **neighbor_cache_entities; // preallocated array to hold neighbor entities + int *grid_index_drivable; + int num_drivable_grid_cell; }; struct Drive { @@ -311,18 +236,24 @@ struct Drive { float *rewards; unsigned char *terminals; unsigned char *truncations; + unsigned char *masks; Log log; Log *logs; - int num_agents; - int active_agent_count; + int num_controllable_agents; // Max number of controllable agents + int active_agent_count; // Current number of controllable agents int *active_agent_indices; + int num_total_agents; // Total agents in a log scenario + int num_max_agents; // Max agents allocated in the environment + int num_agents; // Current number of agents in the environment int action_type; int human_agent_idx; - Entity *entities; - int num_entities; - int num_actors; + Agent *agents; + RoadMapElement *road_elements; + TrafficControlElement *traffic_elements; + int num_road_elements; + int num_traffic_elements; int num_objects; - int num_roads; + struct LaneGraph lane_graph; int static_agent_count; int *static_agent_indices; int expert_static_agent_count; @@ -332,222 +263,377 @@ struct Drive { int dynamics_model; GridMap *grid_map; int *neighbor_offsets; - int episode_length; - int termination_mode; + int scenario_length; + float reward_goal; float reward_vehicle_collision; float reward_offroad_collision; + float reward_comfort; + float reward_lane_align; + float reward_vel_align; + float reward_lane_center; + float reward_center_bias; + float reward_velocity; + float reward_reverse; + float reward_traffic_light_violation; + float reward_timestep; + float reward_overspeed; + float reward_ade; char *map_name; float world_mean_x; float world_mean_y; float dt; - float reward_goal; - float reward_goal_post_respawn; + float spawn_initial_speed; float goal_radius; - float goal_speed; + float goal_speed_threshold; + float min_waypoint_spacing; + float max_waypoint_spacing; + int num_target_waypoints; int logs_capacity; - int goal_behavior; - float goal_target_distance; + int target_type; char *ini_file; - char scenario_id[16]; - int collision_behavior; - int offroad_behavior; - int sdc_track_index; + int collision_behavior; // 0 = none, 1=stop, 2 = remove + int offroad_behavior; // 0 = none, 1=stop, 2 = remove + int traffic_light_behavior; // 0 = none, 1=stop, 2 = remove + // Metadata fields + char scenario_id[128]; + char dataset_name[32]; + int log_length; + float log_dt; + int num_objects_of_interest; + int *objects_of_interest; int num_tracks_to_predict; - int *tracks_to_predict_indices; + int *tracks_to_predict; int init_mode; int control_mode; - int max_controlled_agents; - int render_mode; + int simulation_mode; + int termination_mode; + float inactive_agent_threshold; + int reward_conditioning; + int reward_randomization; + int compute_eval_metrics; + int use_rear_axle; + int max_boundary_segment_observations; + int max_lane_segment_observations; + int max_partner_observations; + int max_traffic_light_observations; + int max_stop_sign_observations; + int eval_mode; }; -void add_log(Drive *env) { - for (int i = 0; i < env->active_agent_count; i++) { - Entity *e = &env->entities[env->active_agent_indices[i]]; +// ======================================== +// Utility Functions +// ======================================== - env->log.goals_reached_this_episode += e->goals_reached_this_episode; - env->log.goals_sampled_this_episode += e->goals_sampled_this_episode; +typedef struct { + int index; + float dist_sq; + float dx; // Store dx/dy to avoid re-calculating + float dy; + float dz; +} AgentDistance; - int offroad = env->logs[i].offroad_rate; - env->log.offroad_rate += offroad; - int collided = env->logs[i].collision_rate; - env->log.collision_rate += collided; - float offroad_per_agent = env->logs[i].offroad_per_agent; - env->log.offroad_per_agent += offroad_per_agent; - float collisions_per_agent = env->logs[i].collisions_per_agent; - env->log.collisions_per_agent += collisions_per_agent; +static float compute_euclidean_distance(float x1, float y1, float x2, float y2) { + float dx = x2 - x1; + float dy = y2 - y1; + float distance = sqrtf(dx * dx + dy * dy); + return distance; +} - float frac_goal_reached = e->goals_reached_this_episode / e->goals_sampled_this_episode; +static int compare_depthpoint(const void *a, const void *b) { + float diff = ((const DepthPoint *)a)->euclidean_dis - ((const DepthPoint *)b)->euclidean_dis; + return (diff > 0.0f) - (diff < 0.0f); +} - // Update score, which is an aggregate measure whether the agent fully solved its task - float threshold = 1.0f; // Default threshold for 1 goal (must complete it) - if (e->goals_sampled_this_episode > 1) { - // For multiple goals, require n-1 goals to be reached - threshold = (e->goals_sampled_this_episode - 1.0f) / e->goals_sampled_this_episode; - } +static float clip(float value, float min, float max) { + if (value < min) + return min; + if (value > max) + return max; + return value; +} - int collision_occurred = - (env->goal_behavior == GOAL_RESPAWN) ? e->collided_before_goal : env->logs[i].collision_rate; - if (frac_goal_reached >= threshold && !collision_occurred) { - env->log.score += 1.0f; - } - if (!offroad && !collided && frac_goal_reached < 1.0f) { - env->log.dnf_rate += 1.0f; - } - int lane_aligned = env->logs[i].lane_alignment_rate; - env->log.lane_alignment_rate += lane_aligned; - env->log.speed_at_goal += env->logs[i].speed_at_goal; - env->log.episode_length += env->logs[i].episode_length; - env->log.episode_return += env->logs[i].episode_return; - // Log composition counts per agent so vec_log averaging recovers the per-env value - env->log.active_agent_count += env->active_agent_count; - env->log.expert_static_agent_count += env->expert_static_agent_count; - env->log.static_agent_count += env->static_agent_count; - int total = env->active_agent_count + env->static_agent_count; - env->log.perc_controlled += (float)env->active_agent_count / (float)total; - env->log.perc_other += (float)env->static_agent_count / (float)total; - env->log.n += 1; - } +// Normalize heading to [-pi, pi] +static float normalize_heading(float heading) { + heading = fmodf(heading, 2.0f * M_PI); + if (heading > M_PI) + heading -= 2.0f * M_PI; + else if (heading < -M_PI) + heading += 2.0f * M_PI; + return heading; } -Entity *load_map_binary(const char *filename, Drive *env) { - FILE *file = fopen(filename, "rb"); - if (!file) - return NULL; +static float compute_heading_diff(float heading1, float heading2) { + float heading_diff = heading1 - heading2; + return normalize_heading(heading_diff); +} + +static void invalidate_agent(Agent *agent) { + agent->sim_x = INVALID_POSITION; + agent->sim_y = INVALID_POSITION; + agent->sim_z = 0.0f; + agent->sim_heading = 0.0f; + agent->cos_heading = 1.0f; + agent->sin_heading = 0.0f; + agent->sim_vx = 0.0f; + agent->sim_vy = 0.0f; + agent->yaw_rate = 0.0f; + agent->sim_speed = 0.0f; + agent->sim_speed_signed = 0.0f; + agent->a_long = 0.0f; + agent->a_lat = 0.0f; + agent->jerk_long = 0.0f; + agent->jerk_lat = 0.0f; + agent->steering_angle = 0.0f; + agent->sim_valid = 0; +} - // Read scenario_id - fread(env->scenario_id, sizeof(char), 16, file); +static void update_agent_speed(Agent *agent) { + float speed = sqrtf(agent->sim_vx * agent->sim_vx + agent->sim_vy * agent->sim_vy); + float v_dot_heading = agent->sim_vx * agent->cos_heading + agent->sim_vy * agent->sin_heading; + agent->sim_speed = speed; + agent->sim_speed_signed = copysignf(speed, v_dot_heading); +} - // Read sdc_track_index - fread(&env->sdc_track_index, sizeof(int), 1, file); +// Trajectory planning/control can operate in the rear-axle frame even though sim state stays center-based. +static inline float compute_log_yaw_rate(const Agent *agent, int timestep, float dt) { + if (dt <= 0.0f) + return 0.0f; - // Read tracks_to_predict - fread(&env->num_tracks_to_predict, sizeof(int), 1, file); - if (env->num_tracks_to_predict > 0) { - env->tracks_to_predict_indices = (int *)malloc(env->num_tracks_to_predict * sizeof(int)); + const int prev_t = timestep - 1; + const int next_t = timestep + 1; + const int has_prev = (prev_t >= 0) && (agent->log_valid[prev_t] == 1); + const int has_next = (next_t < agent->trajectory_length) && (agent->log_valid[next_t] == 1); - for (int i = 0; i < env->num_tracks_to_predict; i++) { - fread(&env->tracks_to_predict_indices[i], sizeof(int), 1, file); - } + if (has_prev && has_next) { + float dtheta = normalize_heading(agent->log_heading[next_t] - agent->log_heading[prev_t]); + return dtheta / (2.0f * dt); + } + if (has_next) { + float dtheta = normalize_heading(agent->log_heading[next_t] - agent->log_heading[timestep]); + return dtheta / dt; + } + if (has_prev) { + float dtheta = normalize_heading(agent->log_heading[timestep] - agent->log_heading[prev_t]); + return dtheta / dt; + } + + return 0.0f; +} + +// Random uniform in [min, max] +static float random_uniform(float min_val, float max_val) { + float scale = (float)rand() / (float)RAND_MAX; + return min_val + scale * (max_val - min_val); +} + +// Mixed uniform distribution X(a) = 0.5*U(1/a, 1) + 0.5*U(1, a) +static float mixed_uniform(float a) { + if ((float)rand() / (float)RAND_MAX < 0.5f) { + return random_uniform(1.0f / a, 1.0f); } else { - env->tracks_to_predict_indices = NULL; - } - - fread(&env->num_objects, sizeof(int), 1, file); - fread(&env->num_roads, sizeof(int), 1, file); - env->num_entities = env->num_objects + env->num_roads; - Entity *entities = (Entity *)malloc(env->num_entities * sizeof(Entity)); - for (int i = 0; i < env->num_entities; i++) { - // Read base entity data - fread(&entities[i].scenario_id, sizeof(int), 1, file); - fread(&entities[i].type, sizeof(int), 1, file); - fread(&entities[i].id, sizeof(int), 1, file); - fread(&entities[i].array_size, sizeof(int), 1, file); - // Allocate arrays based on type - int size = entities[i].array_size; - entities[i].traj_x = (float *)malloc(size * sizeof(float)); - entities[i].traj_y = (float *)malloc(size * sizeof(float)); - entities[i].traj_z = (float *)malloc(size * sizeof(float)); - if (entities[i].type == VEHICLE || entities[i].type == PEDESTRIAN || - entities[i].type == CYCLIST) { // Object type - // Allocate arrays for object-specific data - entities[i].traj_vx = (float *)malloc(size * sizeof(float)); - entities[i].traj_vy = (float *)malloc(size * sizeof(float)); - entities[i].traj_vz = (float *)malloc(size * sizeof(float)); - entities[i].traj_heading = (float *)malloc(size * sizeof(float)); - entities[i].traj_valid = (int *)malloc(size * sizeof(int)); - } else { - // Roads don't use these arrays - entities[i].traj_vx = NULL; - entities[i].traj_vy = NULL; - entities[i].traj_vz = NULL; - entities[i].traj_heading = NULL; - entities[i].traj_valid = NULL; - } - // Read array data - fread(entities[i].traj_x, sizeof(float), size, file); - fread(entities[i].traj_y, sizeof(float), size, file); - fread(entities[i].traj_z, sizeof(float), size, file); - if (entities[i].type == VEHICLE || entities[i].type == PEDESTRIAN || - entities[i].type == CYCLIST) { // Object type - fread(entities[i].traj_vx, sizeof(float), size, file); - fread(entities[i].traj_vy, sizeof(float), size, file); - fread(entities[i].traj_vz, sizeof(float), size, file); - fread(entities[i].traj_heading, sizeof(float), size, file); - fread(entities[i].traj_valid, sizeof(int), size, file); - } - // Read remaining scalar fields - fread(&entities[i].width, sizeof(float), 1, file); - fread(&entities[i].length, sizeof(float), 1, file); - fread(&entities[i].height, sizeof(float), 1, file); - fread(&entities[i].goal_position_x, sizeof(float), 1, file); - fread(&entities[i].goal_position_y, sizeof(float), 1, file); - fread(&entities[i].goal_position_z, sizeof(float), 1, file); - fread(&entities[i].mark_as_expert, sizeof(int), 1, file); + return random_uniform(1.0f, a); } +} - fclose(file); - return entities; +typedef struct { + float min_val; + float max_val; +} RewardBound; + +static const RewardBound REWARD_BOUNDS[NUM_REWARD_COEFS] = { + {2.0f, 12.0f}, // REWARD_COEF_GOAL_RADIUS δ_goal ~ U(2, 12) + {0.0f, 3.0f}, // REWARD_COEF_COLLISION α_collision ~ U(0, 3) + {0.0f, 3.0f}, // REWARD_COEF_OFFROAD α_boundary ~ U(0, 3) + {0.0f, 0.1f}, // REWARD_COEF_COMFORT α_comfort ~ U(0, 0.1) + {2.5e-4f, 2.5e-2f}, // REWARD_COEF_LANE_ALIGN α_l-align ~ U(2.5e-4, 2.5e-2) + {0.0f, 1.0f}, // REWARD_COEF_VEL_ALIGN α_vel-align ~ U(0, 1) + {2.5e-4f, 7.5e-3f}, // REWARD_COEF_LANE_CENTER α_l-center ~ U(2.5e-4, 7.5e-3) + {-0.5f, 0.5f}, // REWARD_COEF_CENTER_BIAS α_center-bias ~ U(-0.5, 0.5) + {0.0f, 5e-3f}, // REWARD_COEF_VELOCITY α_velocity = 2.5e-3 (fixed) + {2.5e-4f, 7.5e-3f}, // REWARD_COEF_REVERSE α_reverse ~ U(2.5e-4, 7.5e-3) + {0.0f, 1.0f}, // REWARD_COEF_TRAFFIC_LIGHT α_stop-line ~ U(0, 1) + {0.0f, 5e-5f}, // REWARD_COEF_TIMESTEP α_timestep = 2.5e-5 (fixed) + {0.0f, 1.0f}, // REWARD_COEF_OVERSPEED + {0.8f, 1.25f}, // REWARD_COEF_THROTTLE C_throttle + {0.8f, 1.25f}, // REWARD_COEF_STEER C_steer + {0.666f, 1.5f}, // REWARD_COEF_ACC C_acc +}; + +// Forward declarations +void move_expert(Drive *env, float *actions, int agent_idx); + +// Generate per-agent reward conditioning coefficients +static void generate_reward_coefs(Drive *env, Agent *agent) { + if (env->reward_randomization) { + // Standard Uniform Randomizations (referencing the bounds array) + agent->reward_coefs[REWARD_COEF_GOAL_RADIUS] = random_uniform(REWARD_BOUNDS[REWARD_COEF_GOAL_RADIUS].min_val, + REWARD_BOUNDS[REWARD_COEF_GOAL_RADIUS].max_val); + agent->reward_coefs[REWARD_COEF_COLLISION] = + random_uniform(REWARD_BOUNDS[REWARD_COEF_COLLISION].min_val, REWARD_BOUNDS[REWARD_COEF_COLLISION].max_val); + agent->reward_coefs[REWARD_COEF_OFFROAD] = + random_uniform(REWARD_BOUNDS[REWARD_COEF_OFFROAD].min_val, REWARD_BOUNDS[REWARD_COEF_OFFROAD].max_val); + agent->reward_coefs[REWARD_COEF_COMFORT] = + random_uniform(REWARD_BOUNDS[REWARD_COEF_COMFORT].min_val, REWARD_BOUNDS[REWARD_COEF_COMFORT].max_val); + agent->reward_coefs[REWARD_COEF_LANE_ALIGN] = random_uniform(REWARD_BOUNDS[REWARD_COEF_LANE_ALIGN].min_val, + REWARD_BOUNDS[REWARD_COEF_LANE_ALIGN].max_val); + agent->reward_coefs[REWARD_COEF_LANE_CENTER] = random_uniform(REWARD_BOUNDS[REWARD_COEF_LANE_CENTER].min_val, + REWARD_BOUNDS[REWARD_COEF_LANE_CENTER].max_val); + agent->reward_coefs[REWARD_COEF_TRAFFIC_LIGHT] = random_uniform( + REWARD_BOUNDS[REWARD_COEF_TRAFFIC_LIGHT].min_val, REWARD_BOUNDS[REWARD_COEF_TRAFFIC_LIGHT].max_val); + agent->reward_coefs[REWARD_COEF_CENTER_BIAS] = random_uniform(REWARD_BOUNDS[REWARD_COEF_CENTER_BIAS].min_val, + REWARD_BOUNDS[REWARD_COEF_CENTER_BIAS].max_val); + agent->reward_coefs[REWARD_COEF_VEL_ALIGN] = + random_uniform(REWARD_BOUNDS[REWARD_COEF_VEL_ALIGN].min_val, REWARD_BOUNDS[REWARD_COEF_VEL_ALIGN].max_val); + agent->reward_coefs[REWARD_COEF_OVERSPEED] = + random_uniform(REWARD_BOUNDS[REWARD_COEF_OVERSPEED].min_val, REWARD_BOUNDS[REWARD_COEF_OVERSPEED].max_val); + agent->reward_coefs[REWARD_COEF_REVERSE] = + random_uniform(REWARD_BOUNDS[REWARD_COEF_REVERSE].min_val, REWARD_BOUNDS[REWARD_COEF_REVERSE].max_val); + // Fixed values (Must fall within the bounds defined above) + agent->reward_coefs[REWARD_COEF_VELOCITY] = 2.5e-3f; + agent->reward_coefs[REWARD_COEF_TIMESTEP] = 2.5e-5f; + // Dynamic conditioning (Mixed Uniform) + agent->reward_coefs[REWARD_COEF_THROTTLE] = mixed_uniform(1.25f); + agent->reward_coefs[REWARD_COEF_STEER] = mixed_uniform(1.25f); + agent->reward_coefs[REWARD_COEF_ACC] = mixed_uniform(1.5f); + } else { + // Fixed coefficients + agent->reward_coefs[REWARD_COEF_GOAL_RADIUS] = env->goal_radius; + agent->reward_coefs[REWARD_COEF_COLLISION] = env->reward_vehicle_collision; + agent->reward_coefs[REWARD_COEF_OFFROAD] = env->reward_offroad_collision; + agent->reward_coefs[REWARD_COEF_COMFORT] = env->reward_comfort; + agent->reward_coefs[REWARD_COEF_LANE_ALIGN] = env->reward_lane_align; + agent->reward_coefs[REWARD_COEF_LANE_CENTER] = env->reward_lane_center; + agent->reward_coefs[REWARD_COEF_VELOCITY] = env->reward_velocity; + agent->reward_coefs[REWARD_COEF_TRAFFIC_LIGHT] = env->reward_traffic_light_violation; + agent->reward_coefs[REWARD_COEF_CENTER_BIAS] = env->reward_center_bias; + agent->reward_coefs[REWARD_COEF_VEL_ALIGN] = env->reward_vel_align; + agent->reward_coefs[REWARD_COEF_OVERSPEED] = env->reward_overspeed; + agent->reward_coefs[REWARD_COEF_TIMESTEP] = env->reward_timestep; + agent->reward_coefs[REWARD_COEF_REVERSE] = env->reward_reverse; + // Dynamic conditioning coefficients + agent->reward_coefs[REWARD_COEF_THROTTLE] = 1.0f; + agent->reward_coefs[REWARD_COEF_STEER] = 1.0f; + agent->reward_coefs[REWARD_COEF_ACC] = 1.0f; + } } -void set_start_position(Drive *env) { - for (int i = 0; i < env->num_entities; i++) { - int is_active = 0; - for (int j = 0; j < env->active_agent_count; j++) { - if (env->active_agent_indices[j] == i) { - is_active = 1; - break; - } - } - Entity *e = &env->entities[i]; +// Generate procedural traffic light states for GIGAFLOW mode +static void generate_traffic_light_states(Drive *env) { + int steps = env->scenario_length; + float dt = env->dt; + + // 20% chance: disable ALL lights for this episode + int disable_all = (!env->eval_mode) && (random_uniform(0.0f, 1.0f) < TL_EPISODE_DISABLE_PROB); + + for (int i = 0; i < env->num_traffic_elements; i++) { + TrafficControlElement *traffic = &env->traffic_elements[i]; + if (traffic->type != TRAFFIC_LIGHT || traffic->states == NULL || traffic->state_length <= 0) + continue; - // Clamp init_steps to ensure we don't go out of bounds - int step = env->init_steps; - if (step >= e->array_size) - step = e->array_size - 1; - if (step < 0) - step = 0; + int fill_steps = steps; + if (traffic->state_length < fill_steps) + fill_steps = traffic->state_length; - e->x = e->traj_x[step]; - e->y = e->traj_y[step]; - e->z = e->traj_z[step]; - if (e->type > CYCLIST || e->type == 0) { + if (disable_all) { + memset(traffic->states, TL_STATE_DISABLED, fill_steps * sizeof(int)); continue; } - if (is_active == 0) { - e->vx = 0; - e->vy = 0; - e->vz = 0; - e->collided_before_goal = 0; + + if (!env->eval_mode) { + // Individual removal + if (random_uniform(0.0f, 1.0f) < TL_INDIVIDUAL_REMOVE_PROB) { + memset(traffic->states, TL_STATE_DISABLED, fill_steps * sizeof(int)); + continue; + } + // Always green + if (random_uniform(0.0f, 1.0f) < TL_ALWAYS_GREEN_PROB) { + for (int t = 0; t < fill_steps; t++) + traffic->states[t] = TL_STATE_GREEN; + continue; + } + } + + // Compute phase durations + float dur_green, dur_yellow, dur_red; + if (env->eval_mode) { + dur_green = TL_DEFAULT_GREEN_DURATION; + dur_yellow = TL_DEFAULT_YELLOW_DURATION; + dur_red = TL_DEFAULT_RED_DURATION; } else { - e->vx = e->traj_vx[env->init_steps]; - e->vy = e->traj_vy[env->init_steps]; - e->vz = e->traj_vz[env->init_steps]; - } - e->heading = e->traj_heading[env->init_steps]; - e->heading_x = cosf(e->heading); - e->heading_y = sinf(e->heading); - e->valid = e->traj_valid[env->init_steps]; - e->collision_state = 0; - e->metrics_array[COLLISION_IDX] = 0.0f; // vehicle collision - e->metrics_array[OFFROAD_IDX] = 0.0f; // offroad - e->metrics_array[REACHED_GOAL_IDX] = 0.0f; // reached goal - e->metrics_array[LANE_ALIGNED_IDX] = 0.0f; // lane aligned - e->respawn_timestep = -1; - e->stopped = 0; - e->removed = 0; - e->respawn_count = 0; - - // Dynamics - e->a_long = 0.0f; - e->a_lat = 0.0f; - e->jerk_long = 0.0f; - e->jerk_lat = 0.0f; - e->steering_angle = 0.0f; - e->wheelbase = 0.6f * e->length; - } -} - -int getGridIndex(Drive *env, float x1, float y1) { + dur_green = random_uniform(0.1 * TL_DEFAULT_GREEN_DURATION, TL_DEFAULT_GREEN_DURATION); + dur_yellow = random_uniform(0.5f * TL_DEFAULT_YELLOW_DURATION, 0.75f * TL_DEFAULT_YELLOW_DURATION); + dur_red = random_uniform(0.15f * TL_DEFAULT_RED_DURATION, 5.0f * TL_DEFAULT_RED_DURATION); + } + + int steps_green = (int)(dur_green / dt); + if (steps_green < 1) + steps_green = 1; + int steps_yellow = (int)(dur_yellow / dt); + if (steps_yellow < 1) + steps_yellow = 1; + int steps_red = (int)(dur_red / dt); + if (steps_red < 1) + steps_red = 1; + int cycle_length = steps_green + steps_yellow + steps_red; + + // Random phase offset + int offset = rand() % cycle_length; + + // Fill states: GREEN -> YELLOW -> RED -> repeat + for (int t = 0; t < fill_steps; t++) { + int phase = (t + offset) % cycle_length; + if (phase < steps_green) + traffic->states[t] = TL_STATE_GREEN; + else if (phase < steps_green + steps_yellow) + traffic->states[t] = TL_STATE_YELLOW; + else + traffic->states[t] = TL_STATE_RED; + } + } +} + +// Normalize a reward coefficient to [-1, 1] range for observations +static float normalize_reward_coef(float value, int coef_idx) { + // NOTE: This prevents having coefficients outside of hardcoded bounds + // What if we want to allow that? + float min_value = REWARD_BOUNDS[coef_idx].min_val; + float max_value = REWARD_BOUNDS[coef_idx].max_val; + float range = max_value - min_value; + + // Safety: prevent division by zero if range is singular + if (range < 1e-9f) { + return 0.0f; + } + + // Normalize to [0, 1] + float normalized = (value - min_value) / range; + + // Clamp to [0, 1] to handle floating point noise + if (normalized < 0.0f) + normalized = 0.0f; + if (normalized > 1.0f) + normalized = 1.0f; + + // Scale to [-1, 1] + return 2.0f * normalized - 1.0f; +} + +// Find entity index given a lane ID +static int find_lane_index_by_id(Drive *env, int lane_id) { + // NOTE: Maps should ensure lane IDs == Indexes for efficiency + for (int i = 0; i < env->num_road_elements; i++) { + if (env->road_elements[i].id == lane_id && is_drivable_road_lane(env->road_elements[i].type)) { + return i; + } + } + return -1; +} + +// ======================================== +// Grid Map Functions +// ======================================== + +static int get_grid_index(Drive *env, float x1, float y1) { if (env->grid_map->top_left_x >= env->grid_map->bottom_right_x || env->grid_map->bottom_right_y >= env->grid_map->top_left_y) { return -1; // Invalid grid coordinates @@ -564,7 +650,8 @@ int getGridIndex(Drive *env, float x1, float y1) { return index; } -void add_entity_to_grid(Drive *env, int grid_index, int entity_idx, int geometry_idx, int *cell_entities_insert_index) { +static void add_entity_to_grid(Drive *env, int grid_index, int entity_type, int entity_idx, int geometry_idx, + int *cell_entities_insert_index) { if (grid_index == -1) { return; } @@ -577,45 +664,47 @@ void add_entity_to_grid(Drive *env, int grid_index, int entity_idx, int geometry return; } + env->grid_map->cells[grid_index][count].entity_type = entity_type; env->grid_map->cells[grid_index][count].entity_idx = entity_idx; env->grid_map->cells[grid_index][count].geometry_idx = geometry_idx; cell_entities_insert_index[grid_index] = count + 1; } -void init_grid_map(Drive *env) { +static void init_grid_map(Drive *env) { // Allocate memory for the grid map structure env->grid_map = (GridMap *)malloc(sizeof(GridMap)); + env->grid_map->num_drivable_grid_cell = 0; // Find top left and bottom right points of the map - float top_left_x; - float top_left_y; - float bottom_right_x; - float bottom_right_y; - int first_valid_point = 0; - for (int i = 0; i < env->num_entities; i++) { - if (env->entities[i].type > 3 && env->entities[i].type < 7) { - // Check all points in the trajectory for road elements - Entity *e = &env->entities[i]; - for (int j = 0; j < e->array_size; j++) { - if (e->traj_x[j] == INVALID_POSITION) - continue; - if (e->traj_y[j] == INVALID_POSITION) - continue; - if (!first_valid_point) { - top_left_x = bottom_right_x = e->traj_x[j]; - top_left_y = bottom_right_y = e->traj_y[j]; - first_valid_point = true; - continue; - } - if (e->traj_x[j] < top_left_x) - top_left_x = e->traj_x[j]; - if (e->traj_x[j] > bottom_right_x) - bottom_right_x = e->traj_x[j]; - if (e->traj_y[j] > top_left_y) - top_left_y = e->traj_y[j]; - if (e->traj_y[j] < bottom_right_y) - bottom_right_y = e->traj_y[j]; + float top_left_x = 0.0f; + float top_left_y = 0.0f; + float bottom_right_x = 0.0f; + float bottom_right_y = 0.0f; + bool first_valid_point = false; + for (int i = 0; i < env->num_road_elements; i++) { + // Check all points in the geometry for road elements (ROAD_LANE, ROAD_LINE, ROAD_EDGE) + if (!is_road(env->road_elements[i].type)) + continue; + RoadMapElement *element = &env->road_elements[i]; + for (int j = 0; j < element->segment_length; j++) { + if (element->x[j] == INVALID_POSITION) + continue; + if (element->y[j] == INVALID_POSITION) + continue; + if (!first_valid_point) { + top_left_x = bottom_right_x = element->x[j]; + top_left_y = bottom_right_y = element->y[j]; + first_valid_point = true; + continue; } + if (element->x[j] < top_left_x) + top_left_x = element->x[j]; + if (element->x[j] > bottom_right_x) + bottom_right_x = element->x[j]; + if (element->y[j] > top_left_y) + top_left_y = element->y[j]; + if (element->y[j] < bottom_right_y) + bottom_right_y = element->y[j]; } } @@ -636,18 +725,18 @@ void init_grid_map(Drive *env) { env->grid_map->cell_entities_count = (int *)calloc(grid_cell_count, sizeof(int)); // Calculate number of entities in each grid cell - for (int i = 0; i < env->num_entities; i++) { - if (env->entities[i].type > 3 && env->entities[i].type < 7) { - for (int j = 0; j < env->entities[i].array_size - 1; j++) { - float x_center = (env->entities[i].traj_x[j] + env->entities[i].traj_x[j + 1]) / 2; - float y_center = (env->entities[i].traj_y[j] + env->entities[i].traj_y[j + 1]) / 2; - int grid_index = getGridIndex(env, x_center, y_center); - env->grid_map->cell_entities_count[grid_index]++; - } + for (int i = 0; i < env->num_road_elements; i++) { + for (int j = 0; j < env->road_elements[i].segment_length - 1; j++) { + float x_center = (env->road_elements[i].x[j] + env->road_elements[i].x[j + 1]) / 2; + float y_center = (env->road_elements[i].y[j] + env->road_elements[i].y[j + 1]) / 2; + int grid_index = get_grid_index(env, x_center, y_center); + if (grid_index == -1) + continue; // Skip out-of-bounds entities + env->grid_map->cell_entities_count[grid_index]++; } } - int cell_entities_insert_index[grid_cell_count]; // Helper array for insertion index - memset(cell_entities_insert_index, 0, grid_cell_count * sizeof(int)); + + int *cell_entities_insert_index = (int *)calloc(grid_cell_count, sizeof(int)); // Initialize grid cells for (int grid_index = 0; grid_index < grid_cell_count; grid_index++) { @@ -661,21 +750,40 @@ void init_grid_map(Drive *env) { } } - // Populate grid cells - for (int i = 0; i < env->num_entities; i++) { - if (env->entities[i].type > 3 && - env->entities[i].type < 7) { // NOTE: Only Road Edges, Lines, and Lanes in grid map - for (int j = 0; j < env->entities[i].array_size - 1; j++) { - float x_center = (env->entities[i].traj_x[j] + env->entities[i].traj_x[j + 1]) / 2; - float y_center = (env->entities[i].traj_y[j] + env->entities[i].traj_y[j + 1]) / 2; - int grid_index = getGridIndex(env, x_center, y_center); - add_entity_to_grid(env, grid_index, i, j, cell_entities_insert_index); + // Track which grid cells contain drivable lanes (for spawning) + bool *drivable_grid_seen = (bool *)calloc(grid_cell_count, sizeof(bool)); + + // Populate grid cells and count unique drivable grid cells + for (int i = 0; i < env->num_road_elements; i++) { + for (int j = 0; j < env->road_elements[i].segment_length - 1; j++) { + float x_center = (env->road_elements[i].x[j] + env->road_elements[i].x[j + 1]) / 2; + float y_center = (env->road_elements[i].y[j] + env->road_elements[i].y[j + 1]) / 2; + int grid_index = get_grid_index(env, x_center, y_center); + if (grid_index == -1) + continue; // Skip out-of-bounds entities + add_entity_to_grid(env, grid_index, ENTITY_TYPE_ROAD_ELEMENT, i, j, cell_entities_insert_index); + // Count unique drivable grid cells + if (is_drivable_road_lane(env->road_elements[i].type) && !drivable_grid_seen[grid_index]) { + drivable_grid_seen[grid_index] = true; + env->grid_map->num_drivable_grid_cell++; } } } + + // Allocate and fill drivable grid index array + env->grid_map->grid_index_drivable = (int *)malloc(env->grid_map->num_drivable_grid_cell * sizeof(int)); + int drivable_idx = 0; + for (int i = 0; i < grid_cell_count; i++) { + if (drivable_grid_seen[i]) { + env->grid_map->grid_index_drivable[drivable_idx++] = i; + } + } + + free(drivable_grid_seen); + free(cell_entities_insert_index); } -void init_neighbor_offsets(Drive *env) { +static void init_neighbor_offsets(Drive *env) { // Allocate memory for the offsets env->neighbor_offsets = (int *)calloc(env->grid_map->vision_range * env->grid_map->vision_range * 2, sizeof(int)); // neighbor offsets in a spiral pattern @@ -719,7 +827,7 @@ void init_neighbor_offsets(Drive *env) { } } -void cache_neighbor_offsets(Drive *env) { +static void cache_neighbor_offsets(Drive *env) { int count = 0; int cell_count = env->grid_map->grid_cols * env->grid_map->grid_rows; env->grid_map->neighbor_cache_entities = (GridMapEntity **)calloc(cell_count, sizeof(GridMapEntity *)); @@ -775,126 +883,21 @@ void cache_neighbor_offsets(Drive *env) { } } -int get_neighbor_cache_entities(Drive *env, int cell_idx, GridMapEntity *entities, int max_entities) { +static const GridMapEntity *get_neighbor_cache_entities(Drive *env, int cell_idx, int *out_count) { GridMap *grid_map = env->grid_map; if (cell_idx < 0 || cell_idx >= (grid_map->grid_cols * grid_map->grid_rows)) { - return 0; // Invalid cell index - } - - int count = grid_map->neighbor_cache_count[cell_idx]; - // Limit to available space - if (count > max_entities) { - count = max_entities; - } - memcpy(entities, grid_map->neighbor_cache_entities[cell_idx], count * sizeof(GridMapEntity)); - return count; -} - -void set_means(Drive *env) { - float mean_x = 0.0f; - float mean_y = 0.0f; - int64_t point_count = 0; - - // Compute single mean for all entities (vehicles and roads) - for (int i = 0; i < env->num_entities; i++) { - if (env->entities[i].type == VEHICLE || env->entities[i].type == PEDESTRIAN || - env->entities[i].type == CYCLIST) { - for (int j = 0; j < env->entities[i].array_size; j++) { - // Assume a validity flag exists (e.g., valid[j]); adjust if not available - if (env->entities[i].traj_valid[j]) { // Add validity check if applicable - point_count++; - mean_x += (env->entities[i].traj_x[j] - mean_x) / point_count; - mean_y += (env->entities[i].traj_y[j] - mean_y) / point_count; - } - } - } else if (env->entities[i].type >= 4) { - for (int j = 0; j < env->entities[i].array_size; j++) { - point_count++; - mean_x += (env->entities[i].traj_x[j] - mean_x) / point_count; - mean_y += (env->entities[i].traj_y[j] - mean_y) / point_count; - } - } - } - env->world_mean_x = mean_x; - env->world_mean_y = mean_y; - for (int i = 0; i < env->num_entities; i++) { - if (env->entities[i].type == VEHICLE || env->entities[i].type == PEDESTRIAN || - env->entities[i].type == CYCLIST || env->entities[i].type >= 4) { - for (int j = 0; j < env->entities[i].array_size; j++) { - if (env->entities[i].traj_x[j] == INVALID_POSITION) - continue; - env->entities[i].traj_x[j] -= mean_x; - env->entities[i].traj_y[j] -= mean_y; - } - env->entities[i].goal_position_x -= mean_x; - env->entities[i].goal_position_y -= mean_y; - } - } -} - -void move_expert(Drive *env, float *actions, int agent_idx) { - Entity *agent = &env->entities[agent_idx]; - int t = env->timestep; - if (t < 0 || t >= agent->array_size) { - agent->x = INVALID_POSITION; - agent->y = INVALID_POSITION; - agent->z = 0.0f; - agent->heading = 0.0f; - agent->heading_x = 1.0f; - agent->heading_y = 0.0f; - return; - } - if (agent->traj_valid && agent->traj_valid[t] == 0) { - agent->x = INVALID_POSITION; - agent->y = INVALID_POSITION; - agent->z = 0.0f; - agent->heading = 0.0f; - agent->heading_x = 1.0f; - agent->heading_y = 0.0f; - return; + *out_count = 0; + return NULL; } - agent->x = agent->traj_x[t]; - agent->y = agent->traj_y[t]; - agent->z = agent->traj_z[t]; - agent->heading = agent->traj_heading[t]; - agent->heading_x = cosf(agent->heading); - agent->heading_y = sinf(agent->heading); -} - -bool check_line_intersection(float p1[2], float p2[2], float q1[2], float q2[2]) { - if (fmax(p1[0], p2[0]) < fmin(q1[0], q2[0]) || fmin(p1[0], p2[0]) > fmax(q1[0], q2[0]) || - fmax(p1[1], p2[1]) < fmin(q1[1], q2[1]) || fmin(p1[1], p2[1]) > fmax(q1[1], q2[1])) - return false; - - // Calculate vectors - float dx1 = p2[0] - p1[0]; - float dy1 = p2[1] - p1[1]; - float dx2 = q2[0] - q1[0]; - float dy2 = q2[1] - q1[1]; - - // Calculate cross products - float cross = dx1 * dy2 - dy1 * dx2; - - // If lines are parallel - if (cross == 0) - return false; - - // Calculate relative vectors between start points - float dx3 = p1[0] - q1[0]; - float dy3 = p1[1] - q1[1]; - - // Calculate parameters for intersection point - float s = (dx1 * dy3 - dy1 * dx3) / cross; - float t = (dx2 * dy3 - dy2 * dx3) / cross; - // Check if intersection point lies within both line segments - return (s >= 0 && s <= 1 && t >= 0 && t <= 1); + *out_count = grid_map->neighbor_cache_count[cell_idx]; + return grid_map->neighbor_cache_entities[cell_idx]; } -int checkNeighbors(Drive *env, float x, float y, GridMapEntity *entity_list, int max_size, - const int (*local_offsets)[2], int offset_size) { +static int get_neighbors_entities(Drive *env, float x, float y, GridMapEntity *entity_list, int max_size, + const int (*local_offsets)[2], int offset_size) { // Get the grid index for the given position (x, y) - int index = getGridIndex(env, x, y); + int index = get_grid_index(env, x, y); if (index == -1) return 0; // Return 0 size if position invalid // Calculate 2D grid coordinates @@ -915,491 +918,2268 @@ int checkNeighbors(Drive *env, float x, float y, GridMapEntity *entity_list, int for (int j = 0; j < count && entity_list_count < max_size; j++) { int entityId = env->grid_map->cells[neighborIndex][j].entity_idx; int geometry_idx = env->grid_map->cells[neighborIndex][j].geometry_idx; + int entity_type = env->grid_map->cells[neighborIndex][j].entity_type; entity_list[entity_list_count].entity_idx = entityId; entity_list[entity_list_count].geometry_idx = geometry_idx; + entity_list[entity_list_count].entity_type = entity_type; entity_list_count += 1; } } return entity_list_count; } -int check_aabb_collision(Entity *car1, Entity *car2) { - // Get car corners in world space - float cos1 = car1->heading_x; - float sin1 = car1->heading_y; - float cos2 = car2->heading_x; - float sin2 = car2->heading_y; +// ======================================== +// Map Loading Functions +// ======================================== - // Calculate half dimensions - float half_len1 = car1->length * 0.5f; - float half_width1 = car1->width * 0.5f; - float half_len2 = car2->length * 0.5f; - float half_width2 = car2->width * 0.5f; +int load_map_binary(const char *filename, Drive *drive) { + FILE *file = fopen(filename, "rb"); + if (!file) + return -1; - // Calculate car1's corners in world space - float car1_corners[4][2] = { - {car1->x + (half_len1 * cos1 - half_width1 * sin1), car1->y + (half_len1 * sin1 + half_width1 * cos1)}, - {car1->x + (half_len1 * cos1 + half_width1 * sin1), car1->y + (half_len1 * sin1 - half_width1 * cos1)}, - {car1->x + (-half_len1 * cos1 - half_width1 * sin1), car1->y + (-half_len1 * sin1 + half_width1 * cos1)}, - {car1->x + (-half_len1 * cos1 + half_width1 * sin1), car1->y + (-half_len1 * sin1 - half_width1 * cos1)}}; + int num_total_agents, num_roads, num_traffic, num_objects; + if (fread(&num_total_agents, sizeof(int), 1, file) != 1) { + fclose(file); + return -1; + } + if (fread(&num_roads, sizeof(int), 1, file) != 1) { + fclose(file); + return -1; + } + if (fread(&num_traffic, sizeof(int), 1, file) != 1) { + fclose(file); + return -1; + } + if (fread(&num_objects, sizeof(int), 1, file) != 1) { + fclose(file); + return -1; + } - // Calculate car2's corners in world space - float car2_corners[4][2] = { - {car2->x + (half_len2 * cos2 - half_width2 * sin2), car2->y + (half_len2 * sin2 + half_width2 * cos2)}, - {car2->x + (half_len2 * cos2 + half_width2 * sin2), car2->y + (half_len2 * sin2 - half_width2 * cos2)}, - {car2->x + (-half_len2 * cos2 - half_width2 * sin2), car2->y + (-half_len2 * sin2 + half_width2 * cos2)}, - {car2->x + (-half_len2 * cos2 + half_width2 * sin2), car2->y + (-half_len2 * sin2 - half_width2 * cos2)}}; + drive->num_total_agents = num_total_agents; + drive->num_road_elements = num_roads; + drive->num_traffic_elements = num_traffic; + drive->num_objects = num_objects; - // Get the axes to check (normalized vectors perpendicular to each edge) - float axes[4][2] = { - {cos1, sin1}, // Car1's length axis - {-sin1, cos1}, // Car1's width axis - {cos2, sin2}, // Car2's length axis - {-sin2, cos2} // Car2's width axis - }; + if (num_total_agents > 0) { + drive->agents = (Agent *)calloc(num_total_agents, sizeof(Agent)); + } - // Check each axis - for (int i = 0; i < 4; i++) { - float min1 = INFINITY, max1 = -INFINITY; - float min2 = INFINITY, max2 = -INFINITY; + if (num_roads > 0) { + drive->road_elements = (RoadMapElement *)calloc(num_roads, sizeof(RoadMapElement)); + } - // Project car1's corners onto the axis - for (int j = 0; j < 4; j++) { - float proj = car1_corners[j][0] * axes[i][0] + car1_corners[j][1] * axes[i][1]; - min1 = fminf(min1, proj); - max1 = fmaxf(max1, proj); + if (num_traffic > 0) { + drive->traffic_elements = (TrafficControlElement *)calloc(num_traffic, sizeof(TrafficControlElement)); + } + + for (int i = 0; i < num_total_agents; i++) { + Agent *agent = &drive->agents[i]; + + if (fread(&agent->id, sizeof(int), 1, file) != 1) { + fclose(file); + return -1; + } + if (fread(&agent->type, sizeof(int), 1, file) != 1) { + fclose(file); + return -1; + } + if (fread(&agent->trajectory_length, sizeof(int), 1, file) != 1) { + fclose(file); + return -1; } - // Project car2's corners onto the axis - for (int j = 0; j < 4; j++) { - float proj = car2_corners[j][0] * axes[i][0] + car2_corners[j][1] * axes[i][1]; - min2 = fminf(min2, proj); - max2 = fmaxf(max2, proj); + int tlen = agent->trajectory_length; + agent->log_trajectory_x = (float *)malloc(tlen * sizeof(float)); + agent->log_trajectory_y = (float *)malloc(tlen * sizeof(float)); + agent->log_trajectory_z = (float *)malloc(tlen * sizeof(float)); + agent->log_heading = (float *)malloc(tlen * sizeof(float)); + agent->log_velocity_x = (float *)malloc(tlen * sizeof(float)); + agent->log_velocity_y = (float *)malloc(tlen * sizeof(float)); + agent->log_length = (float *)malloc(tlen * sizeof(float)); + agent->log_width = (float *)malloc(tlen * sizeof(float)); + agent->log_height = (float *)malloc(tlen * sizeof(float)); + agent->log_valid = (int *)malloc(tlen * sizeof(int)); + + if ((size_t)tlen > 0 && fread(agent->log_trajectory_x, sizeof(float), tlen, file) != (size_t)tlen) { + fclose(file); + return -1; + } + if ((size_t)tlen > 0 && fread(agent->log_trajectory_y, sizeof(float), tlen, file) != (size_t)tlen) { + fclose(file); + return -1; + } + if ((size_t)tlen > 0 && fread(agent->log_trajectory_z, sizeof(float), tlen, file) != (size_t)tlen) { + fclose(file); + return -1; + } + if ((size_t)tlen > 0 && fread(agent->log_heading, sizeof(float), tlen, file) != (size_t)tlen) { + fclose(file); + return -1; + } + if ((size_t)tlen > 0 && fread(agent->log_velocity_x, sizeof(float), tlen, file) != (size_t)tlen) { + fclose(file); + return -1; + } + if ((size_t)tlen > 0 && fread(agent->log_velocity_y, sizeof(float), tlen, file) != (size_t)tlen) { + fclose(file); + return -1; + } + if ((size_t)tlen > 0 && fread(agent->log_length, sizeof(float), tlen, file) != (size_t)tlen) { + fclose(file); + return -1; + } + if ((size_t)tlen > 0 && fread(agent->log_width, sizeof(float), tlen, file) != (size_t)tlen) { + fclose(file); + return -1; + } + if ((size_t)tlen > 0 && fread(agent->log_height, sizeof(float), tlen, file) != (size_t)tlen) { + fclose(file); + return -1; + } + if ((size_t)tlen > 0 && fread(agent->log_valid, sizeof(int), tlen, file) != (size_t)tlen) { + fclose(file); + return -1; } - // If there's a gap on this axis, the boxes don't intersect - if (max1 < min2 || min1 > max2) { - return 0; // No collision + if (fread(&agent->route_length, sizeof(int), 1, file) != 1) { + fclose(file); + return -1; } - } - // If we get here, there's no separating axis, so the boxes intersect - return 1; // Collision -} + if (agent->route_length > 0) { + agent->route = (int *)malloc(agent->route_length * sizeof(int)); + if (fread(agent->route, sizeof(int), agent->route_length, file) != (size_t)agent->route_length) { + fclose(file); + return -1; + } + } else { + agent->route = NULL; + } -int collision_check(Drive *env, int agent_idx) { - Entity *agent = &env->entities[agent_idx]; + if (fread(&agent->route_gt_len, sizeof(int), 1, file) != 1) { + fclose(file); + return -1; + } - if (agent->x == INVALID_POSITION) - return -1; + if (fread(&agent->goal_position_x, sizeof(float), 1, file) != 1) { + fclose(file); + return -1; + } + if (fread(&agent->goal_position_y, sizeof(float), 1, file) != 1) { + fclose(file); + return -1; + } + if (fread(&agent->goal_position_z, sizeof(float), 1, file) != 1) { + fclose(file); + return -1; + } + if (fread(&agent->mark_as_expert, sizeof(int), 1, file) != 1) { + fclose(file); + return -1; + } + } - // Skip collision checking for pedestrians because they are often too - // close to other entities at initialization. - if (agent->type == PEDESTRIAN) - return -1; + for (int i = 0; i < num_roads; i++) { + RoadMapElement *road = &drive->road_elements[i]; - int car_collided_with_index = -1; + if (fread(&road->id, sizeof(int), 1, file) != 1) { + fclose(file); + return -1; + } + if (fread(&road->type, sizeof(int), 1, file) != 1) { + fclose(file); + return -1; + } + if (fread(&road->segment_length, sizeof(int), 1, file) != 1) { + fclose(file); + return -1; + } - if (agent->respawn_timestep != -1) - return car_collided_with_index; // Skip respawning entities + int slen = road->segment_length; - for (int i = 0; i < MAX_AGENTS; i++) { - int index = -1; - if (i < env->active_agent_count) { - index = env->active_agent_indices[i]; - } else if (i < env->num_actors && env->static_agent_count > 0) { - index = env->static_agent_indices[i - env->active_agent_count]; + road->x = (float *)malloc(slen * sizeof(float)); + road->y = (float *)malloc(slen * sizeof(float)); + road->z = (float *)malloc(slen * sizeof(float)); + + if ((size_t)slen > 0 && fread(road->x, sizeof(float), slen, file) != (size_t)slen) { + fclose(file); + return -1; } - if (index == -1) - continue; - if (index == agent_idx) - continue; - Entity *entity = &env->entities[index]; - if (entity->respawn_timestep != -1) - continue; // Skip respawning entities - float x1 = entity->x; - float y1 = entity->y; - float dist = ((x1 - agent->x) * (x1 - agent->x) + (y1 - agent->y) * (y1 - agent->y)); - if (dist > 225.0f) - continue; - if (check_aabb_collision(agent, entity)) { - car_collided_with_index = index; - break; + if ((size_t)slen > 0 && fread(road->y, sizeof(float), slen, file) != (size_t)slen) { + fclose(file); + return -1; + } + if ((size_t)slen > 0 && fread(road->z, sizeof(float), slen, file) != (size_t)slen) { + fclose(file); + return -1; } - } - return car_collided_with_index; -} + road->headings = (float *)malloc(slen * sizeof(float)); + if ((size_t)slen > 0 && fread(road->headings, sizeof(float), slen, file) != (size_t)slen) { + fclose(file); + return -1; + } -int check_lane_aligned(Entity *car, Entity *lane, int geometry_idx) { - // Validate lane geometry length - if (!lane || lane->array_size < 2) - return 0; + if (road->type >= 0 && road->type <= 9) { + if (fread(&road->num_entries, sizeof(int), 1, file) != 1) { + fclose(file); + return -1; + } + if (road->num_entries > 0) { + road->entry_lanes = (int *)malloc(road->num_entries * sizeof(int)); + if (fread(road->entry_lanes, sizeof(int), road->num_entries, file) != (size_t)road->num_entries) { + fclose(file); + return -1; + } + } else { + road->entry_lanes = NULL; + } - // Clamp geometry index to valid segment range [0, array_size-2] - if (geometry_idx < 0) - geometry_idx = 0; - if (geometry_idx >= lane->array_size - 1) - geometry_idx = lane->array_size - 2; - - // Compute local lane segment heading - float heading_x1, heading_y1; - if (geometry_idx > 0) { - heading_x1 = lane->traj_x[geometry_idx] - lane->traj_x[geometry_idx - 1]; - heading_y1 = lane->traj_y[geometry_idx] - lane->traj_y[geometry_idx - 1]; - } else { - // For first segment, just use the forward direction - heading_x1 = lane->traj_x[geometry_idx + 1] - lane->traj_x[geometry_idx]; - heading_y1 = lane->traj_y[geometry_idx + 1] - lane->traj_y[geometry_idx]; - } + if (fread(&road->num_exits, sizeof(int), 1, file) != 1) { + fclose(file); + return -1; + } + if (road->num_exits > 0) { + road->exit_lanes = (int *)malloc(road->num_exits * sizeof(int)); + if (fread(road->exit_lanes, sizeof(int), road->num_exits, file) != (size_t)road->num_exits) { + fclose(file); + return -1; + } + } else { + road->exit_lanes = NULL; + } - float heading_x2 = lane->traj_x[geometry_idx + 1] - lane->traj_x[geometry_idx]; - float heading_y2 = lane->traj_y[geometry_idx + 1] - lane->traj_y[geometry_idx]; + if (fread(&road->speed_limit, sizeof(float), 1, file) != 1) { + fclose(file); + return -1; + } + } else { + road->num_entries = 0; + road->num_exits = 0; + road->entry_lanes = NULL; + road->exit_lanes = NULL; + road->speed_limit = 0.0f; + } + } - float heading_1 = atan2f(heading_y1, heading_x1); - float heading_2 = atan2f(heading_y2, heading_x2); - float heading = (heading_1 + heading_2) / 2.0f; + for (int i = 0; i < num_traffic; i++) { + TrafficControlElement *traffic = &drive->traffic_elements[i]; - // Normalize to [-pi, pi] - if (heading > M_PI) - heading -= 2.0f * M_PI; - if (heading < -M_PI) - heading += 2.0f * M_PI; + if (fread(&traffic->id, sizeof(int), 1, file) != 1) { + fclose(file); + return -1; + } + if (fread(&traffic->type, sizeof(int), 1, file) != 1) { + fclose(file); + return -1; + } + if (fread(traffic->stop_line, sizeof(float), 6, file) != 6) { + fclose(file); + return -1; + } + if (fread(&traffic->heading, sizeof(float), 1, file) != 1) { + fclose(file); + return -1; + } + if (fread(&traffic->state_length, sizeof(int), 1, file) != 1) { + fclose(file); + return -1; + } - // Compute heading difference - float car_heading = car->heading; // radians - float heading_diff = fabsf(car_heading - heading); + int state_len = traffic->state_length; - if (heading_diff > M_PI) - heading_diff = 2.0f * M_PI - heading_diff; + traffic->states = (int *)malloc(state_len * sizeof(int)); + if ((size_t)state_len > 0 && fread(traffic->states, sizeof(int), state_len, file) != (size_t)state_len) { + fclose(file); + return -1; + } - // within 15 degrees - return (heading_diff < (M_PI / 12.0f)) ? 1 : 0; -} + if (fread(&traffic->num_controlled_lanes, sizeof(int), 1, file) != 1) { + fclose(file); + return -1; + } + if (traffic->num_controlled_lanes > 0) { + traffic->controlled_lanes = (int *)malloc(traffic->num_controlled_lanes * sizeof(int)); + if (fread(traffic->controlled_lanes, sizeof(int), traffic->num_controlled_lanes, file) != + (size_t)traffic->num_controlled_lanes) { + fclose(file); + return -1; + } + } else { + traffic->controlled_lanes = NULL; + } + } -void reset_agent_metrics(Drive *env, int agent_idx) { - Entity *agent = &env->entities[agent_idx]; - agent->metrics_array[COLLISION_IDX] = 0.0f; // vehicle collision - agent->metrics_array[OFFROAD_IDX] = 0.0f; // offroad - agent->metrics_array[LANE_ALIGNED_IDX] = 0.0f; // lane aligned - agent->collision_state = 0; -} + // Skip objects section + for (int i = 0; i < num_objects; i++) { + int obj_id, obj_type, T; + if (fread(&obj_id, sizeof(int), 1, file) != 1 || fread(&obj_type, sizeof(int), 1, file) != 1 || + fread(&T, sizeof(int), 1, file) != 1) { + fclose(file); + return -1; + } + // Skip: x,y,z,heading,vx,vy,length,width,height (9 float arrays) + valid (1 int array) + fseek(file, 9 * T * sizeof(float) + T * sizeof(int), SEEK_CUR); + } -float point_to_segment_distance_2d(float px, float py, float x1, float y1, float x2, float y2) { - float dx = x2 - x1; - float dy = y2 - y1; + // Lane graph section + int n_lanes_graph; + if (fread(&n_lanes_graph, sizeof(int), 1, file) != 1) { + fclose(file); + return -1; + } + drive->lane_graph.n_lanes = n_lanes_graph; + drive->lane_graph.lane_ids = NULL; + drive->lane_graph.lane_lengths = NULL; + drive->lane_graph.distances = NULL; + if (n_lanes_graph > 0) { + drive->lane_graph.lane_ids = (int *)malloc(n_lanes_graph * sizeof(int)); + if (fread(drive->lane_graph.lane_ids, sizeof(int), n_lanes_graph, file) != (size_t)n_lanes_graph) { + fclose(file); + return -1; + } + drive->lane_graph.lane_lengths = (float *)malloc(n_lanes_graph * sizeof(float)); + if (fread(drive->lane_graph.lane_lengths, sizeof(float), n_lanes_graph, file) != (size_t)n_lanes_graph) { + fclose(file); + return -1; + } + drive->lane_graph.distances = (float *)malloc(n_lanes_graph * n_lanes_graph * sizeof(float)); + if (fread(drive->lane_graph.distances, sizeof(float), n_lanes_graph * n_lanes_graph, file) != + (size_t)(n_lanes_graph * n_lanes_graph)) { + fclose(file); + return -1; + } + } - if (dx == 0 && dy == 0) { - // The segment is a point - return sqrtf((px - x1) * (px - x1) + (py - y1) * (py - y1)); + // Metadata + if (fread(drive->scenario_id, sizeof(char), 128, file) != 128) { + fclose(file); + return -1; + } + if (fread(drive->dataset_name, sizeof(char), 32, file) != 32) { + fclose(file); + return -1; + } + if (fread(&drive->log_length, sizeof(int), 1, file) != 1) { + fclose(file); + return -1; + } + if (fread(&drive->log_dt, sizeof(float), 1, file) != 1) { + fclose(file); + return -1; + } + if (fread(&drive->num_objects_of_interest, sizeof(int), 1, file) != 1) { + fclose(file); + return -1; } - // Calculate the t that minimizes the distance - float t = ((px - x1) * dx + (py - y1) * dy) / (dx * dx + dy * dy); + if (drive->num_objects_of_interest > 0) { + drive->objects_of_interest = (int *)malloc(drive->num_objects_of_interest * sizeof(int)); + if (fread(drive->objects_of_interest, sizeof(int), drive->num_objects_of_interest, file) != + (size_t)drive->num_objects_of_interest) { + fclose(file); + return -1; + } + } else { + drive->objects_of_interest = NULL; + } - // Clamp t to the segment - if (t < 0) - t = 0; - else if (t > 1) - t = 1; + if (fread(&drive->num_tracks_to_predict, sizeof(int), 1, file) != 1) { + fclose(file); + return -1; + } - // Find the closest point on the segment - float closestX = x1 + t * dx; - float closestY = y1 + t * dy; + if (drive->num_tracks_to_predict > 0) { + drive->tracks_to_predict = (int *)malloc(drive->num_tracks_to_predict * sizeof(int)); + if (fread(drive->tracks_to_predict, sizeof(int), drive->num_tracks_to_predict, file) != + (size_t)drive->num_tracks_to_predict) { + fclose(file); + return -1; + } + } else { + drive->tracks_to_predict = NULL; + } - // Return the distance from p to the closest point - return sqrtf((px - closestX) * (px - closestX) + (py - closestY) * (py - closestY)); + fclose(file); + return 0; } -void compute_agent_metrics(Drive *env, int agent_idx) { - Entity *agent = &env->entities[agent_idx]; +// ======================================== +// Road Utility Functions +// ======================================== - reset_agent_metrics(env, agent_idx); +// Compute multi-segment average heading around a center segment +static float compute_multi_segment_alignment(RoadMapElement *element, int center_seg_idx) { + // NOTE: This function returns the average heading in radians for a lane segment, + // with more weight given to the center segment. - if (agent->x == INVALID_POSITION) - return; // invalid agent position + float avg_heading = 0.0f; + float total_weight = 0.0f; - int collided = 0; - float half_length = agent->length / 2.0f; - float half_width = agent->width / 2.0f; - float cos_heading = cosf(agent->heading); - float sin_heading = sinf(agent->heading); - float min_distance = (float)INT16_MAX; + int start = (center_seg_idx > 0) ? (center_seg_idx - 1) : center_seg_idx; + int end = (center_seg_idx < element->segment_length - 2) ? (center_seg_idx + 1) : (element->segment_length - 2); - int closest_lane_entity_idx = -1; - int closest_lane_geometry_idx = -1; + for (int seg_idx = start; seg_idx <= end; seg_idx++) { + if (seg_idx < 0 || seg_idx >= element->segment_length - 1) + continue; - float corners[4][2]; - for (int i = 0; i < 4; i++) { - corners[i][0] = - agent->x + (offsets[i][0] * half_length * cos_heading - offsets[i][1] * half_width * sin_heading); - corners[i][1] = - agent->y + (offsets[i][0] * half_length * sin_heading + offsets[i][1] * half_width * cos_heading); - } + float seg_heading = element->headings[seg_idx]; - GridMapEntity entity_list[MAX_ENTITIES_PER_CELL * 25]; // Array big enough for all neighboring cells - int list_size = - checkNeighbors(env, agent->x, agent->y, entity_list, MAX_ENTITIES_PER_CELL * 25, collision_offsets, 25); - for (int i = 0; i < list_size; i++) { - if (entity_list[i].entity_idx == -1) - continue; - if (entity_list[i].entity_idx == agent_idx) - continue; - Entity *entity; - entity = &env->entities[entity_list[i].entity_idx]; - - // Check for offroad collision with road edges (only for vehicles and cyclists) - if (entity->type == ROAD_EDGE && agent->type != PEDESTRIAN) { - int geometry_idx = entity_list[i].geometry_idx; - float start[2] = {entity->traj_x[geometry_idx], entity->traj_y[geometry_idx]}; - float end[2] = {entity->traj_x[geometry_idx + 1], entity->traj_y[geometry_idx + 1]}; - for (int k = 0; k < 4; k++) { // Check each edge of the bounding box - int next = (k + 1) % 4; - if (check_line_intersection(corners[k], corners[next], start, end)) { - collided = OFFROAD; - break; - } - } + float weight = (seg_idx == center_seg_idx) ? 2.0f : 1.0f; + + if (total_weight == 0.0f) { + avg_heading = seg_heading; + } else { + float angle_diff = compute_heading_diff(seg_heading, avg_heading); + avg_heading += weight * angle_diff / (total_weight + weight); } + total_weight += weight; + } - if (collided == OFFROAD) - break; + return avg_heading; +} - // Find closest point on the road centerline to the agent - if (entity->type == ROAD_LANE) { - int entity_idx = entity_list[i].entity_idx; - int geometry_idx = entity_list[i].geometry_idx; +// Compute the length of a lane +static float compute_lane_length(RoadMapElement *lane) { + float length = 0.0f; + for (int i = 1; i < lane->segment_length; i++) { + float dx = lane->x[i] - lane->x[i - 1]; + float dy = lane->y[i] - lane->y[i - 1]; + length += sqrtf(dx * dx + dy * dy); + } + return length; +} + +// Compute the remaining distance on a lane from a given position to the end of the lane +static float compute_remaining_lane_distance(RoadMapElement *lane, float pos_x, float pos_y) { + // Find the closest segment to the position + int closest_seg = 0; + float closest_t = 0.0f; + float min_dist_sq = 1e30f; + + for (int i = 0; i < lane->segment_length - 1; i++) { + float x0 = lane->x[i]; + float y0 = lane->y[i]; + float x1 = lane->x[i + 1]; + float y1 = lane->y[i + 1]; + + float dx = x1 - x0; + float dy = y1 - y0; + float seg_len_sq = dx * dx + dy * dy; + + float t = 0.0f; + if (seg_len_sq > 1e-6f) { + t = ((pos_x - x0) * dx + (pos_y - y0) * dy) / seg_len_sq; + t = fmaxf(0.0f, fminf(1.0f, t)); + } - float start[2] = {entity->traj_x[geometry_idx], entity->traj_y[geometry_idx]}; - float end[2] = {entity->traj_x[geometry_idx + 1], entity->traj_y[geometry_idx + 1]}; + float proj_x = x0 + t * dx; + float proj_y = y0 + t * dy; + float dist_sq = (pos_x - proj_x) * (pos_x - proj_x) + (pos_y - proj_y) * (pos_y - proj_y); - float dist = point_to_segment_distance_2d(agent->x, agent->y, start[0], start[1], end[0], end[1]); - float heading_diff = fabsf(atan2f(end[1] - start[1], end[0] - start[0]) - agent->heading); + if (dist_sq < min_dist_sq) { + min_dist_sq = dist_sq; + closest_seg = i; + closest_t = t; + } + } - // Normalize heading difference to [0, pi] - if (heading_diff > M_PI) - heading_diff = 2.0f * M_PI - heading_diff; + // Compute remaining distance from closest point to end of lane + float remaining = 0.0f; - // Penalize if heading differs by more than 30 degrees - if (heading_diff > (M_PI / 6.0f)) - dist += 3.0f; + // Partial distance in current segment (from t to end of segment) + float dx = lane->x[closest_seg + 1] - lane->x[closest_seg]; + float dy = lane->y[closest_seg + 1] - lane->y[closest_seg]; + float seg_len = sqrtf(dx * dx + dy * dy); + remaining += (1.0f - closest_t) * seg_len; - if (dist < min_distance) { - min_distance = dist; - closest_lane_entity_idx = entity_idx; - closest_lane_geometry_idx = geometry_idx; - } - } + // Full distance of remaining segments + for (int i = closest_seg + 1; i < lane->segment_length - 1; i++) { + dx = lane->x[i + 1] - lane->x[i]; + dy = lane->y[i + 1] - lane->y[i]; + remaining += sqrtf(dx * dx + dy * dy); } - // check if aligned with closest lane and set current lane - // 4.0m threshold: agents more than 4 meters from any lane are considered off-road - if (min_distance > 4.0f || closest_lane_entity_idx == -1) { - agent->metrics_array[LANE_ALIGNED_IDX] = 0.0f; - agent->current_lane_idx = -1; - } else { - agent->current_lane_idx = closest_lane_entity_idx; - int lane_aligned = - check_lane_aligned(agent, &env->entities[closest_lane_entity_idx], closest_lane_geometry_idx); - agent->metrics_array[LANE_ALIGNED_IDX] = lane_aligned; + return remaining; +} + +static float compute_lane_end_distance_sq(RoadMapElement *lane, float origin_x, float origin_y) { + if (lane->segment_length <= 0) { + return 0.0f; } - // Check for vehicle collisions (skip for pedestrians) - int car_collided_with_index = -1; - if (agent->type != PEDESTRIAN) { - car_collided_with_index = collision_check(env, agent_idx); - if (car_collided_with_index != -1) - collided = VEHICLE_COLLISION; + int last_idx = lane->segment_length - 1; + float dx = lane->x[last_idx] - origin_x; + float dy = lane->y[last_idx] - origin_y; + return dx * dx + dy * dy; +} + +// Returns signed distance to lane (left of lane = negative, right = positive) +static float find_closest_segment_on_lane(RoadMapElement *lane, float agent_x, float agent_y, int *out_segment_idx) { + int num_segments = lane->segment_length - 1; + if (num_segments < 1) { + *out_segment_idx = 0; + return 1e9f; } - agent->collision_state = collided; + float min_dist_sq = 1e18f; + int closest_idx = 0; + float closest_cross = 0.0f; + + for (int seg_idx = 0; seg_idx < num_segments; seg_idx++) { + float seg_start_x = lane->x[seg_idx]; + float seg_start_y = lane->y[seg_idx]; + float seg_end_x = lane->x[seg_idx + 1]; + float seg_end_y = lane->y[seg_idx + 1]; + + float seg_dx = seg_end_x - seg_start_x; + float seg_dy = seg_end_y - seg_start_y; + float seg_length_sq = seg_dx * seg_dx + seg_dy * seg_dy; + + float to_agent_x = agent_x - seg_start_x; + float to_agent_y = agent_y - seg_start_y; + + // cross > 0 means agent is left of lane direction + float cross = seg_dx * to_agent_y - seg_dy * to_agent_x; + + float dist_sq; + if (seg_length_sq > 1e-6f) { + float t = (to_agent_x * seg_dx + to_agent_y * seg_dy) / seg_length_sq; + if (t <= 0.0f) { + dist_sq = to_agent_x * to_agent_x + to_agent_y * to_agent_y; + } else if (t >= 1.0f) { + float dx = agent_x - seg_end_x; + float dy = agent_y - seg_end_y; + dist_sq = dx * dx + dy * dy; + } else { + dist_sq = (cross * cross) / seg_length_sq; + } + } else { + dist_sq = to_agent_x * to_agent_x + to_agent_y * to_agent_y; + } - if (collided == VEHICLE_COLLISION) { - if (env->collision_behavior == STOP_AGENT && !agent->stopped) { - agent->stopped = 1; - agent->vx = agent->vy = 0.0f; - } else if (env->collision_behavior == REMOVE_AGENT && !agent->removed) { - Entity *agent_collided = &env->entities[car_collided_with_index]; - agent->removed = 1; - agent_collided->removed = 1; - agent->x = agent->y = -10000.0f; - agent_collided->x = agent_collided->y = -10000.0f; + if (dist_sq < min_dist_sq) { + min_dist_sq = dist_sq; + closest_idx = seg_idx; + closest_cross = cross; } } - if (collided == OFFROAD) { - agent->metrics_array[OFFROAD_IDX] = 1.0f; - if (env->offroad_behavior == STOP_AGENT && !agent->stopped) { - agent->stopped = 1; - agent->vx = agent->vy = 0.0f; - } else if (env->offroad_behavior == REMOVE_AGENT && !agent->removed) { - agent->removed = 1; - agent->x = agent->y = -10000.0f; + + *out_segment_idx = closest_idx; + float abs_dist = sqrtf(min_dist_sq); + return (closest_cross >= 0.0f) ? -abs_dist : abs_dist; +} + +static float compute_progression(Agent *agent) { + int num_wp = agent->path->num_waypoints; + if (num_wp < 2) + return agent->path->waypoints[0].s; + + int idx = agent->closest_path_idx_wp; + if (idx >= num_wp) + idx = num_wp - 1; + + // closest_path_idx_wp is the closest waypoint IN FRONT of the agent, + // so the agent is on the segment ending at that waypoint (idx-1 to idx). + // Also check the next segment (idx to idx+1) in case agent is at the waypoint. + int start_seg = (idx > 0) ? (idx - 1) : 0; + int end_seg = (idx < num_wp - 1) ? (idx + 1) : (num_wp - 1); + + float min_dist_sq = 1e18f; + float best_s = agent->path->waypoints[0].s; + + for (int i = start_seg; i < end_seg; ++i) { + const struct Waypoint wp_a = agent->path->waypoints[i]; + const struct Waypoint wp_b = agent->path->waypoints[i + 1]; + + const float seg_dx = wp_b.x - wp_a.x; + const float seg_dy = wp_b.y - wp_a.y; + const float seg_len_sq = seg_dx * seg_dx + seg_dy * seg_dy; + + float t; + if (seg_len_sq < 1e-6f) { + t = 0.0f; + } else { + const float agent_dx = agent->sim_x - wp_a.x; + const float agent_dy = agent->sim_y - wp_a.y; + t = (agent_dx * seg_dx + agent_dy * seg_dy) / seg_len_sq; + } + + const float clamped_t = fmaxf(0.0f, fminf(1.0f, t)); + const float closest_x = wp_a.x + clamped_t * seg_dx; + const float closest_y = wp_a.y + clamped_t * seg_dy; + const float dist_sq = (agent->sim_x - closest_x) * (agent->sim_x - closest_x) + + (agent->sim_y - closest_y) * (agent->sim_y - closest_y); + + if (dist_sq < min_dist_sq) { + min_dist_sq = dist_sq; + best_s = wp_a.s + clamped_t * (wp_b.s - wp_a.s); } } - return; + return best_s; } -bool should_control_agent(Drive *env, int agent_idx, int control_limit) { - // Check if we have room for more agents or are already at capacity - if (env->active_agent_count >= control_limit) { - return false; - } +static float score_lane_candidate(Drive *env, float agent_heading, int *checked_lanes, int *num_checked_lanes, + int *closest_segment_index, GridMapEntity entity, float agent_x, float agent_y, + float max_distance_threshold, int current_lane_index, float *signed_dist_out, + float *lane_heading_out) { + if (entity.entity_idx == -1 || entity.entity_type != ENTITY_TYPE_ROAD_ELEMENT) + return -1; - Entity *entity = &env->entities[agent_idx]; + int entity_idx = entity.entity_idx; + RoadMapElement *element = &env->road_elements[entity_idx]; - if (env->control_mode == CONTROL_SDC_ONLY) { - return agent_idx == env->sdc_track_index; + if (!is_drivable_road_lane(element->type)) + return -1; + + int already_checked = 0; + for (int c = 0; c < *num_checked_lanes; c++) { + if (checked_lanes[c] == entity_idx) { + already_checked = 1; + break; + } + } + if (already_checked) + return -1; + if (*num_checked_lanes < MAX_CHECKED_LANES) { + checked_lanes[*num_checked_lanes] = entity_idx; + (*num_checked_lanes)++; } - bool is_vehicle = (entity->type == VEHICLE); - bool is_ped_or_bike = (entity->type == PEDESTRIAN || entity->type == CYCLIST); - bool type_is_valid = false; + // Find closest segment on this lane + float signed_dist = find_closest_segment_on_lane(element, agent_x, agent_y, closest_segment_index); + float abs_dist = fabsf(signed_dist); - switch (env->control_mode) { - case CONTROL_WOSAC: - // Valid types only, ignore expert flag and goal distance - return (is_vehicle || is_ped_or_bike); + if (abs_dist > max_distance_threshold) + return -1; - case CONTROL_VEHICLES: - type_is_valid = is_vehicle; - break; + // Compute lane heading using multi-segment alignment + float avg_lane_heading = compute_multi_segment_alignment(element, *closest_segment_index); - default: - type_is_valid = (is_vehicle || is_ped_or_bike); - break; - } + // Compute heading alignment penalty (0.0 = perfect, 1.0 = opposite) + float heading_diff = compute_heading_diff(agent_heading, avg_lane_heading); + float heading_penalty = fabsf(heading_diff) / M_PI; // Normalize to [0, 1] - // Filter invalid types or experts - if (!type_is_valid || entity->mark_as_expert) { - return false; + // Normalize distance for scoring + float distance_penalty = abs_dist / LANE_DISTANCE_NORMALIZATION; + + // Combined score using defined weights + float score = LANE_SELECTION_DISTANCE_WEIGHT * distance_penalty + LANE_SELECTION_HEADING_WEIGHT * heading_penalty; + + // Hysteresis: penalize switching away from current lane + if (current_lane_index != entity_idx && current_lane_index != -1) { + score += LANE_SWITCH_THRESHOLD; } - // Check distance to goal in agent's local frame - float cos_heading = cosf(entity->traj_heading[0]); - float sin_heading = sinf(entity->traj_heading[0]); - float goal_dx = entity->goal_position_x - entity->traj_x[0]; - float goal_dy = entity->goal_position_y - entity->traj_y[0]; + *signed_dist_out = signed_dist; + *lane_heading_out = avg_lane_heading; + return score; +} - // Transform to agent's local frame - float local_goal_x = goal_dx * cos_heading + goal_dy * sin_heading; - float local_goal_y = -goal_dx * sin_heading + goal_dy * cos_heading; - float distance_to_goal = relative_distance_2d(0, 0, local_goal_x, local_goal_y); +static DepthPoint compute_z_distance_to_road_segment(const Agent *agent, const RoadMapElement *lane, int geometry_idx) { + float dx = agent->sim_x - lane->x[geometry_idx]; + float dy = agent->sim_y - lane->y[geometry_idx]; + float dz = agent->sim_z - lane->z[geometry_idx]; - return distance_to_goal >= MIN_DISTANCE_TO_GOAL; + DepthPoint point; + point.z_dis = fabsf(dz); + point.euclidean_dis = sqrtf(dx * dx + dy * dy + dz * dz); + point.z = lane->z[geometry_idx]; + return point; } -void set_active_agents(Drive *env) { +// ======================================== +// Route/Path/Goal Functions +// ======================================== - // Initialize - env->active_agent_count = 0; // Policy-controlled agents - env->static_agent_count = 0; // Non-moving background agents - env->expert_static_agent_count = 0; // Expert replay agents (non-controlled) - env->num_actors = 0; // Total agents created (there is always the SDC) +static int get_closest_waypoint_index_on_path(Drive *env, int agent_idx) { + const float MAX_DIST_SQ = 10000.0f; + const float MAX_ANGLE_DIFF = M_PI_2; // 90 degrees + const int WINDOW_FORWARD = 20; // 40m ahead (20 * 2m waypoint spacing) + const int WINDOW_BACKWARD = 10; // 20m behind - int active_agent_indices[MAX_AGENTS]; - int static_agent_indices[MAX_AGENTS]; - int expert_static_agent_indices[MAX_AGENTS]; + Agent *agent = &env->agents[agent_idx]; + if (agent->path == NULL || agent->path->num_waypoints == 0) + return 0; - if (env->num_agents == 0) { - env->num_agents = MAX_AGENTS; - } + int num_wp = agent->path->num_waypoints; + int prev_idx = agent->closest_path_idx_wp; + if (prev_idx >= num_wp) + prev_idx = 0; - int control_limit; - if (env->control_mode == CONTROL_MIXED_PLAY) { - control_limit = (env->max_controlled_agents < env->num_agents) ? env->max_controlled_agents : env->num_agents; - } else { - control_limit = env->num_agents; + float heading_x = cosf(agent->sim_heading); + float heading_y = sinf(agent->sim_heading); + + int best_idx = 0; + float min_dist_sq = MAX_DIST_SQ; + + // Try windowed search first, fallback to full search if no candidate found + int start_idx = fmaxf(0, prev_idx - WINDOW_BACKWARD); + int end_idx = fminf(num_wp, prev_idx + WINDOW_FORWARD); + + for (int pass = 0; pass < 2; pass++) { + for (int i = start_idx; i < end_idx; i++) { + float dx = agent->path->waypoints[i].x - agent->sim_x; + float dy = agent->path->waypoints[i].y - agent->sim_y; + + // Skip waypoints behind agent + if (dx * heading_x + dy * heading_y <= 0) + continue; + + // Skip waypoints with heading too different from agent + float angle_diff = agent->sim_heading - agent->path->waypoints[i].heading; + angle_diff = atan2f(sinf(angle_diff), cosf(angle_diff)); + + if (fabsf(angle_diff) > MAX_ANGLE_DIFF) + continue; + + float dist_sq = dx * dx + dy * dy; + if (dist_sq < min_dist_sq) { + min_dist_sq = dist_sq; + best_idx = i; + } + } + + // If found in windowed search, done + if (min_dist_sq < MAX_DIST_SQ) + break; + + // Fallback: full search + start_idx = 0; + end_idx = num_wp; } - // If we have a SDC index (WOMD), initialize it first: - int sdc_index = env->sdc_track_index; + return best_idx; +} - if (sdc_index >= 0) { - active_agent_indices[0] = sdc_index; - env->num_actors++; - env->active_agent_count++; - env->entities[sdc_index].active_agent = 1; +static inline void initialize_agent_progression(Drive *env, int agent_idx) { + Agent *agent = &env->agents[agent_idx]; + if (agent->path == NULL || agent->path->num_waypoints == 0) { + agent->closest_path_idx_wp = 0; + agent->path_progression = 0.0f; + agent->distance_since_spawn = 0.0f; + return; } - // Iterate through entities to find agents to create and/or control - for (int i = 0; i < env->num_objects && env->num_actors < MAX_AGENTS; i++) { + agent->closest_path_idx_wp = get_closest_waypoint_index_on_path(env, agent_idx); + float baseline_progression = compute_progression(agent); + agent->path_progression = baseline_progression; + agent->distance_since_spawn = 0.0f; +} - // Skip if its the SDC - if (i == sdc_index) { - continue; - } +static inline void reset_agent_path_progression(Drive *env, int agent_idx) { + Agent *agent = &env->agents[agent_idx]; - Entity *entity = &env->entities[i]; + if (agent->path == NULL || agent->path->num_waypoints == 0) { + agent->closest_path_idx_wp = 0; + agent->path_progression = 0.0f; + return; + } - // Skip if not valid at initialization - if (entity->traj_valid[env->init_steps] != 1) { - continue; - } + agent->closest_path_idx_wp = get_closest_waypoint_index_on_path(env, agent_idx); + agent->path_progression = compute_progression(agent); +} - // Determine if entity should be created - bool should_create = false; - if (env->init_mode == INIT_ALL_VALID) { - should_create = true; // All valid entities - } else if (env->control_mode == CONTROL_VEHICLES) { - should_create = (entity->type == VEHICLE); - } else { // Control all agents - should_create = (entity->type == VEHICLE || entity->type == PEDESTRIAN || entity->type == CYCLIST); - } +static void build_path(Drive *env, int agent_idx) { + // NOTE: This function assumes the agent's route is already set. + // It interpolates waypoints along the route lanes at fixed spacing. + // It is a mid-level representation between route and low-level goals waypoints. - if (!should_create) - continue; + float waypoints_spacing = env->min_waypoint_spacing; - env->num_actors++; + Agent *agent = &env->agents[agent_idx]; - // Determine if this agent should be policy-controlled - bool is_controlled = false; + if (agent->path != NULL) + free(agent->path); + agent->path = (struct Path *)malloc(sizeof(struct Path)); - is_controlled = should_control_agent(env, i, control_limit); + int wp_count = 0; + float prev_x, prev_y, prev_z, prev_s; - if (is_controlled) { - active_agent_indices[env->active_agent_count] = i; - env->active_agent_count++; - env->entities[i].active_agent = 1; - } else if (env->init_mode != INIT_ONLY_CONTROLLABLE_AGENTS) { - static_agent_indices[env->static_agent_count] = i; - env->static_agent_count++; // Includes expert replay and static agents - env->entities[i].active_agent = 0; - if (env->entities[i].mark_as_expert == 1 || env->active_agent_count == control_limit) { - expert_static_agent_indices[env->expert_static_agent_count] = i; - env->expert_static_agent_count++; - env->entities[i].mark_as_expert = 1; + // Interpolate waypoints along route lanes + for (int route_idx = 0; route_idx < agent->route_length && wp_count < MAX_NUM_WP_PATH; route_idx++) { + int lane_idx = find_lane_index_by_id(env, agent->route[route_idx]); + if (lane_idx == -1) + continue; + RoadMapElement *lane = &env->road_elements[lane_idx]; + + for (int i = 0; i < lane->segment_length && wp_count < MAX_NUM_WP_PATH; i++) { + float curr_x = lane->x[i]; + float curr_y = lane->y[i]; + float curr_z = lane->z[i]; + + // First point: add directly + if (wp_count == 0) { + agent->path->waypoints[0] = + (struct Waypoint){.x = curr_x, .y = curr_y, .z = curr_z, .s = 0.0f, .lane_id = lane->id}; + prev_x = curr_x; + prev_y = curr_y; + prev_z = curr_z; + prev_s = 0.0f; + wp_count++; + continue; + } + + float dx = curr_x - prev_x; + float dy = curr_y - prev_y; + float dz = curr_z - prev_z; + float seg_len = sqrtf(dx * dx + dy * dy + dz * dz); + if (seg_len < 1e-6f) { + prev_x = curr_x; + prev_y = curr_y; + prev_z = curr_z; + continue; + } + + float curr_s = prev_s + seg_len; + + // Interpolate waypoints within this segment + float target_s = (float)wp_count * waypoints_spacing; + while (target_s < curr_s && wp_count < MAX_NUM_WP_PATH) { + float t = (target_s - prev_s) / seg_len; + agent->path->waypoints[wp_count] = (struct Waypoint){ + .x = prev_x + t * dx, + .y = prev_y + t * dy, + .z = prev_z + t * dz, + .s = target_s, + .lane_id = lane->id, + }; + wp_count++; + target_s = (float)wp_count * waypoints_spacing; } + + prev_x = curr_x; + prev_y = curr_y; + prev_z = curr_z; + prev_s = curr_s; } } - // Set up initial active agents - env->active_agent_indices = (int *)malloc(env->active_agent_count * sizeof(int)); - env->static_agent_indices = (int *)malloc(env->static_agent_count * sizeof(int)); - env->expert_static_agent_indices = (int *)malloc(env->expert_static_agent_count * sizeof(int)); - for (int i = 0; i < env->active_agent_count; i++) { - env->active_agent_indices[i] = active_agent_indices[i]; - }; - for (int i = 0; i < env->static_agent_count; i++) { - env->static_agent_indices[i] = static_agent_indices[i]; + agent->path->num_waypoints = wp_count; + if (wp_count < 2) + return; + + // Compute heading (tangent angle) and cache trig values + for (int i = 0; i < wp_count - 1; i++) { + float dx = agent->path->waypoints[i + 1].x - agent->path->waypoints[i].x; + float dy = agent->path->waypoints[i + 1].y - agent->path->waypoints[i].y; + float heading = atan2f(dy, dx); + agent->path->waypoints[i].heading = heading; + agent->path->waypoints[i].cos_heading = cosf(heading); + agent->path->waypoints[i].sin_heading = sinf(heading); } - for (int i = 0; i < env->expert_static_agent_count; i++) { - env->expert_static_agent_indices[i] = expert_static_agent_indices[i]; + // Last waypoint copies from second-to-last + agent->path->waypoints[wp_count - 1].heading = agent->path->waypoints[wp_count - 2].heading; + agent->path->waypoints[wp_count - 1].cos_heading = agent->path->waypoints[wp_count - 2].cos_heading; + agent->path->waypoints[wp_count - 1].sin_heading = agent->path->waypoints[wp_count - 2].sin_heading; + + // Compute kappa (curvature = d_heading / ds) + for (int i = 1; i < wp_count - 1; i++) { + float d_heading = + compute_heading_diff(agent->path->waypoints[i + 1].heading, agent->path->waypoints[i].heading); + agent->path->waypoints[i].kappa = d_heading / waypoints_spacing; } - // printf("Total actors: %d, Active agents: %d, Static agents: %d, Expert static agents: %d\n", env->num_actors, - // env->active_agent_count, env->static_agent_count, env->expert_static_agent_count); - // printf("Control mode: %d, max controlled agents: %d\n", env->control_mode, env->max_controlled_agents); - - return; + agent->path->waypoints[0].kappa = agent->path->waypoints[1].kappa; + agent->path->waypoints[wp_count - 1].kappa = agent->path->waypoints[wp_count - 2].kappa; } -void remove_bad_trajectories(Drive *env) { - - if (env->control_mode != CONTROL_WOSAC) { - return; // Leave all trajectories in WOSAC control mode +// Generate a route by random walk through lane graph until target distance is reached +static int generate_random_route(Drive *env, int start_lane_id, float target_distance, int *route, int max_route_length, + float agent_x, float agent_y) { + // NOTE: This function performs a random walk through the lane connectivity graph,starting from start_lane_id, + // until the accumulated distance exceeds target_distance or max_route_length is reached. + // Cycles are avoided by tracking visited lanes and trying to increase distance. + + int route_length = 0; + float accumulated_distance = 0.0f; + int current_lane_id = start_lane_id; + float start_x = agent_x; + float start_y = agent_y; + + // Track visited lanes to avoid loops + int visited_ids[MAX_ROUTE_LENGTH]; + int visited_count = 0; + + // Add start lane to route + route[route_length++] = current_lane_id; + visited_ids[visited_count++] = current_lane_id; + + // Get start lane and add its length + int current_lane_idx = find_lane_index_by_id(env, current_lane_id); + if (current_lane_idx == -1) { + return 0; // Invalid start lane } - set_start_position(env); + RoadMapElement *current_lane = &env->road_elements[current_lane_idx]; + // Use remaining distance from agent position instead of full lane length + accumulated_distance += compute_remaining_lane_distance(current_lane, agent_x, agent_y); + float max_end_distance_sq = compute_lane_end_distance_sq(current_lane, start_x, start_y); + + // Random walk through lane graph + while (accumulated_distance < target_distance && route_length < max_route_length) { + current_lane_idx = find_lane_index_by_id(env, current_lane_id); + if (current_lane_idx == -1) + break; + + current_lane = &env->road_elements[current_lane_idx]; + + // Collect valid (unvisited, drivable) exit lanes + int valid_exits[8]; + float valid_exit_dist_sq[8]; + int num_valid_exits = 0; + + int progressing_exits[8]; + float progressing_dist_sq[8]; + int num_progressing_exits = 0; + + for (int e = 0; e < current_lane->num_exits && num_valid_exits < 8; e++) { + int exit_id = current_lane->exit_lanes[e]; + + // Check if already visited + int already_visited = 0; + for (int v = 0; v < visited_count; v++) { + if (visited_ids[v] == exit_id) { + already_visited = 1; + break; + } + } + if (already_visited) + continue; + + // Check if exit lane is drivable + int exit_lane_idx = find_lane_index_by_id(env, exit_id); + if (exit_lane_idx == -1) + continue; + + // NOTE: Dummy logic to prevent cycle, can be improved with better graph traversal + float exit_end_distance_sq = + compute_lane_end_distance_sq(&env->road_elements[exit_lane_idx], start_x, start_y); + valid_exits[num_valid_exits] = exit_id; + valid_exit_dist_sq[num_valid_exits] = exit_end_distance_sq; + num_valid_exits++; + + if (exit_end_distance_sq > max_end_distance_sq) { + progressing_exits[num_progressing_exits] = exit_id; + progressing_dist_sq[num_progressing_exits] = exit_end_distance_sq; + num_progressing_exits++; + } + } + + // If no valid exits, we've reached a dead end + if (num_valid_exits == 0) + break; + + // Pick a progressing exit lane if possible, otherwise pick the farthest available + int chosen_exit_id; + float chosen_exit_dist_sq; + if (num_progressing_exits > 0) { + int chosen_idx = rand() % num_progressing_exits; + chosen_exit_id = progressing_exits[chosen_idx]; + chosen_exit_dist_sq = progressing_dist_sq[chosen_idx]; + } else { + int best_idx = 0; + float best_dist_sq = valid_exit_dist_sq[0]; + for (int i = 1; i < num_valid_exits; i++) { + if (valid_exit_dist_sq[i] > best_dist_sq) { + best_dist_sq = valid_exit_dist_sq[i]; + best_idx = i; + } + } + chosen_exit_id = valid_exits[best_idx]; + chosen_exit_dist_sq = valid_exit_dist_sq[best_idx]; + } + int chosen_exit_idx = find_lane_index_by_id(env, chosen_exit_id); + + if (chosen_exit_idx == -1) + break; + + // Add to route + route[route_length++] = chosen_exit_id; + visited_ids[visited_count++] = chosen_exit_id; + + // Accumulate distance + RoadMapElement *exit_lane = &env->road_elements[chosen_exit_idx]; + accumulated_distance += compute_lane_length(exit_lane); + if (chosen_exit_dist_sq > max_end_distance_sq) { + max_end_distance_sq = chosen_exit_dist_sq; + } + + // Move to next lane + current_lane_id = chosen_exit_id; + } + + return route_length; +} + +// NOTE: Only works for closed maps with infinite looping routes +static int compute_new_route(Drive *env, int agent_idx, int current_lane_id) { + Agent *agent = &env->agents[agent_idx]; + + // Generate route by random walk through lane graph + // Use agent's current position to compute remaining distance on start lane + int num_target_waypoints = env->num_target_waypoints; + if (num_target_waypoints > MAX_TARGET_WAYPOINTS) { + num_target_waypoints = MAX_TARGET_WAYPOINTS; + } + + float min_route_distance; + // NOTE: make both multipliers config values and tune from a metric (route regenerations per 1k env steps). + if (env->target_type == TARGET_STATIC) { + min_route_distance = env->max_waypoint_spacing * num_target_waypoints * 2.0f; + } else { + min_route_distance = env->min_waypoint_spacing * num_target_waypoints * 20.0f; + } + + int temp_route[MAX_ROUTE_LENGTH]; + int route_length = generate_random_route(env, current_lane_id, min_route_distance, temp_route, MAX_ROUTE_LENGTH, + agent->sim_x, agent->sim_y); + + if (route_length == 0) { + printf("[GIGAFLOW WARNING] -> Failed to generate route for agent %d\n", agent->id); + agent->removed = 1; + return 0; + } + + // Free old route and allocate new one + if (agent->route != NULL) + free(agent->route); + + agent->route_length = route_length; + agent->route = (int *)malloc(route_length * sizeof(int)); + + for (int i = 0; i < route_length; i++) { + agent->route[i] = temp_route[i]; + } + + agent->current_route_index = 0; + + // Update path + build_path(env, agent_idx); + agent->closest_path_idx_wp = 0; // Reset before search (old index invalid on new path) + reset_agent_path_progression(env, agent_idx); + + return 1; // Success +} + +static void compute_goals(Drive *env, int agent_idx) { + Agent *agent = &env->agents[agent_idx]; + struct Path *path = agent->path; + + // Validate path exists + if (path == NULL || path->num_waypoints == 0) { + printf("[GIGAFLOW WARNING] -> Agent %d has no valid path\n", agent->id); + agent->removed = 1; + return; + } + + int num_target_waypoints = env->num_target_waypoints; + // Validate waypoint count in bounds + if (num_target_waypoints <= 0 || num_target_waypoints > MAX_TARGET_WAYPOINTS) { + num_target_waypoints = MAX_TARGET_WAYPOINTS; + } + + float goal_spacings[MAX_TARGET_WAYPOINTS]; + + // Iterative replacement for former recursion (bounded at 4 retries) + for (int iter = 0; iter <= 4; iter++) { + float total_spacing = 0.0f; + for (int i = 0; i < num_target_waypoints; i++) { + goal_spacings[i] = random_uniform(env->min_waypoint_spacing, env->max_waypoint_spacing); + total_spacing += goal_spacings[i]; + } + + // On iter 3, reset to path start to escape a short-path cycle + int base_idx = (iter == 3) ? 0 : get_closest_waypoint_index_on_path(env, agent_idx); + float base_s = path->waypoints[base_idx].s; + float needed_s = base_s + total_spacing; + float path_end_s = path->waypoints[path->num_waypoints - 1].s; + + // If we reached the end of the current path, compute a new route and retry. + // Bounded by iter <= 4 to prevent infinite loops on degenerate maps. + if (needed_s >= path_end_s) { + if (iter > 3) { + printf("[GIGAFLOW WARNING] -> Max iterations in compute_goals for agent %d\n", agent->id); + agent->removed = 1; + return; + } + if (env->simulation_mode == SIMULATION_GIGAFLOW) { + int route_ok = compute_new_route(env, agent_idx, path->waypoints[base_idx].lane_id); + if (route_ok == 0) { + agent->removed = 1; + return; + } + path = agent->path; + continue; + } + } + + // Place N goals along the path at random spacing intervals from current position + float cumulative_spacing = 0.0f; + for (int i = 0; i < num_target_waypoints; i++) { + cumulative_spacing += goal_spacings[i]; + float target_s = base_s + cumulative_spacing; + // Find waypoint at or past target_s + int wp_idx = path->num_waypoints - 1; + for (int j = base_idx + 1; j < path->num_waypoints; j++) { + if (path->waypoints[j].s >= target_s) { + wp_idx = j; + break; + } + } + agent->goal_positions_x[i] = path->waypoints[wp_idx].x; + agent->goal_positions_y[i] = path->waypoints[wp_idx].y; + agent->goal_positions_z[i] = path->waypoints[wp_idx].z; + } + + // Reset goal index and update alias + agent->current_goal_idx = 0; + agent->goal_position_x = agent->goal_positions_x[0]; + agent->goal_position_y = agent->goal_positions_y[0]; + agent->goal_position_z = agent->goal_positions_z[0]; + return; + } + + printf("[GIGAFLOW ERROR] -> Failed to compute goals for agent %d after multiple attempts\n", agent->id); + agent->removed = 1; +} + +// ======================================== +// Metrics/Collision Functions +// ======================================== + +static float compute_displacement_error(Agent *agent, int timestep) { + // Check if timestep is within valid range + if (timestep < 0 || timestep >= agent->trajectory_length) { + return 0.0f; + } + + // Check if reference trajectory is valid at this timestep + if (!agent->log_valid[timestep]) { + return 0.0f; + } + + // Get reference position from logged trajectory at current timestep + float ref_x = agent->log_trajectory_x[timestep]; + float ref_y = agent->log_trajectory_y[timestep]; + + if (ref_x == INVALID_POSITION || ref_y == INVALID_POSITION) { + return 0.0f; + } + + // Compute deltas: Euclidean distance between simulated and reference position + float dx = agent->sim_x - ref_x; + float dy = agent->sim_y - ref_y; + float displacement = sqrtf(dx * dx + dy * dy); + + return displacement; +} + +static bool check_line_intersection(float p1[2], float p2[2], float q1[2], float q2[2]) { + if (fmaxf(p1[0], p2[0]) < fminf(q1[0], q2[0]) || fminf(p1[0], p2[0]) > fmaxf(q1[0], q2[0]) || + fmaxf(p1[1], p2[1]) < fminf(q1[1], q2[1]) || fminf(p1[1], p2[1]) > fmaxf(q1[1], q2[1])) + return false; + + // Calculate vectors + float dx1 = p2[0] - p1[0]; + float dy1 = p2[1] - p1[1]; + float dx2 = q2[0] - q1[0]; + float dy2 = q2[1] - q1[1]; + + // Calculate cross products + float cross = dx1 * dy2 - dy1 * dx2; + + // If lines are parallel + if (cross == 0) + return false; + + // Calculate relative vectors between start points + float dx3 = p1[0] - q1[0]; + float dy3 = p1[1] - q1[1]; + + // Calculate parameters for intersection point + float s = (dx1 * dy3 - dy1 * dx3) / cross; + float t = (dx2 * dy3 - dy2 * dx3) / cross; + + // Check if intersection point lies within both line segments + return (s >= 0 && s <= 1 && t >= 0 && t <= 1); +} + +static bool check_stop_line_crossing(Drive *env, Agent *agent, int current_lane_id, float corners[4][2]) { + float agent_x = agent->sim_x; + float agent_y = agent->sim_y; + + for (int i = 0; i < env->num_traffic_elements; i++) { + TrafficControlElement *traffic = &env->traffic_elements[i]; + + if (traffic->type != TRAFFIC_LIGHT) + continue; + if (traffic->num_controlled_lanes == 0) + continue; + + int controls_lane = 0; + for (int j = 0; j < traffic->num_controlled_lanes; j++) { + if (traffic->controlled_lanes[j] == current_lane_id) { + controls_lane = 1; + break; + } + } + if (!controls_lane) + continue; + if (env->timestep >= traffic->state_length) + continue; + if (!is_traffic_light_red(traffic->states[env->timestep])) + continue; + + // Pre-filter: distance to stop line midpoint + float mid_x = (traffic->stop_line[0] + traffic->stop_line[3]) * 0.5f; + float mid_y = (traffic->stop_line[1] + traffic->stop_line[4]) * 0.5f; + float dx = agent_x - mid_x; + float dy = agent_y - mid_y; + if (dx * dx + dy * dy > TRAFFIC_LIGHT_DISTANCE_THRESHOLD * TRAFFIC_LIGHT_DISTANCE_THRESHOLD) + continue; + + // Heading check: agent must be heading towards the stop line + float heading_diff = compute_heading_diff(agent->sim_heading, traffic->heading); + if (fabsf(heading_diff) > RED_LIGHT_HEADING_THRESHOLD) + continue; + + // Extend stop line endpoints by STOP_LINE_EXTENSION_FACTOR + float sl_dx = traffic->stop_line[3] - traffic->stop_line[0]; + float sl_dy = traffic->stop_line[4] - traffic->stop_line[1]; + float ext = (STOP_LINE_EXTENSION_FACTOR - 1.0f) * 0.5f; + float ext_p1[2] = {traffic->stop_line[0] - ext * sl_dx, traffic->stop_line[1] - ext * sl_dy}; + float ext_p2[2] = {traffic->stop_line[3] + ext * sl_dx, traffic->stop_line[4] + ext * sl_dy}; + + // Check front + side edges vs extended stop line (skip back edge k=2) + for (int k = 0; k < 4; k++) { + if (k == 2) + continue; + int next = (k + 1) % 4; + if (check_line_intersection(corners[k], corners[next], ext_p1, ext_p2)) + return true; + } + } + return false; +} + +static bool check_lane_change_red_light(Drive *env, Agent *agent) { + if (agent->previous_lane_index == agent->current_lane_index) + return false; + if (agent->previous_lane_index == -1 || agent->current_lane_index == -1) + return false; + + int new_lane_id = env->road_elements[agent->current_lane_index].id; + float agent_x = agent->sim_x; + float agent_y = agent->sim_y; + + for (int i = 0; i < env->num_traffic_elements; i++) { + TrafficControlElement *traffic = &env->traffic_elements[i]; + + if (traffic->type != TRAFFIC_LIGHT) + continue; + if (traffic->num_controlled_lanes == 0) + continue; + if (env->timestep >= traffic->state_length) + continue; + if (!is_traffic_light_red(traffic->states[env->timestep])) + continue; + + for (int j = 0; j < traffic->num_controlled_lanes; j++) { + if (traffic->controlled_lanes[j] != new_lane_id) + continue; + + float mid_x = (traffic->stop_line[0] + traffic->stop_line[3]) * 0.5f; + float mid_y = (traffic->stop_line[1] + traffic->stop_line[4]) * 0.5f; + float dx = agent_x - mid_x; + float dy = agent_y - mid_y; + if (dx * dx + dy * dy > TRAFFIC_LIGHT_DISTANCE_THRESHOLD * TRAFFIC_LIGHT_DISTANCE_THRESHOLD) + continue; + + return true; + } + } + return false; +} + +static bool check_red_light_violation(Drive *env, int agent_idx) { + Agent *agent = &env->agents[agent_idx]; + int current_lane_index = agent->current_lane_index; + + if (current_lane_index == -1) + return false; + + // Compute bounding box corners: front-right(0), front-left(1), back-left(2), back-right(3) + static const float offsets[4][2] = {{1, 1}, {1, -1}, {-1, -1}, {-1, 1}}; + float half_length = agent->sim_length / 2.0f; + float half_width = agent->sim_width / 2.0f; + float corners[4][2]; + for (int i = 0; i < 4; i++) { + corners[i][0] = agent->sim_x + (offsets[i][0] * half_length * agent->cos_heading - + offsets[i][1] * half_width * agent->sin_heading); + corners[i][1] = agent->sim_y + (offsets[i][0] * half_length * agent->sin_heading + + offsets[i][1] * half_width * agent->cos_heading); + } + + int current_lane_id = env->road_elements[current_lane_index].id; + + if (check_stop_line_crossing(env, agent, current_lane_id, corners)) + return true; + + if (check_lane_change_red_light(env, agent)) + return true; + + return false; +} + +// OBB collision via SAT (Separating Axis Theorem). +// Projects both boxes onto 4 axes (2 per car) and checks for overlap on all axes. +// No epsilon tolerance: exact boundary contact may flicker across steps. +static bool check_obb_collision(Agent *car1, Agent *car2) { + // Get car corners in world space + float cos1 = car1->cos_heading; + float sin1 = car1->sin_heading; + + float cos2 = car2->cos_heading; + float sin2 = car2->sin_heading; + + // Calculate half dimensions + float half_len1 = car1->sim_length * 0.5f; + float half_width1 = car1->sim_width * 0.5f; + float half_len2 = car2->sim_length * 0.5f; + float half_width2 = car2->sim_width * 0.5f; + + // Calculate car1's corners in world space + float car1_corners[4][2] = { + {car1->sim_x + (half_len1 * cos1 - half_width1 * sin1), car1->sim_y + (half_len1 * sin1 + half_width1 * cos1)}, + {car1->sim_x + (half_len1 * cos1 + half_width1 * sin1), car1->sim_y + (half_len1 * sin1 - half_width1 * cos1)}, + {car1->sim_x + (-half_len1 * cos1 - half_width1 * sin1), + car1->sim_y + (-half_len1 * sin1 + half_width1 * cos1)}, + {car1->sim_x + (-half_len1 * cos1 + half_width1 * sin1), + car1->sim_y + (-half_len1 * sin1 - half_width1 * cos1)}}; + + // Calculate car2's corners in world space + float car2_corners[4][2] = { + {car2->sim_x + (half_len2 * cos2 - half_width2 * sin2), car2->sim_y + (half_len2 * sin2 + half_width2 * cos2)}, + {car2->sim_x + (half_len2 * cos2 + half_width2 * sin2), car2->sim_y + (half_len2 * sin2 - half_width2 * cos2)}, + {car2->sim_x + (-half_len2 * cos2 - half_width2 * sin2), + car2->sim_y + (-half_len2 * sin2 + half_width2 * cos2)}, + {car2->sim_x + (-half_len2 * cos2 + half_width2 * sin2), + car2->sim_y + (-half_len2 * sin2 - half_width2 * cos2)}}; + + // Get the axes to check (normalized vectors perpendicular to each edge) + float axes[4][2] = { + {cos1, sin1}, // Car1's length axis + {-sin1, cos1}, // Car1's width axis + {cos2, sin2}, // Car2's length axis + {-sin2, cos2} // Car2's width axis + }; + + // Check each axis + for (int i = 0; i < 4; i++) { + float min1 = INFINITY, max1 = -INFINITY; + float min2 = INFINITY, max2 = -INFINITY; + + // Project car1's corners onto the axis + for (int j = 0; j < 4; j++) { + float proj = car1_corners[j][0] * axes[i][0] + car1_corners[j][1] * axes[i][1]; + min1 = fminf(min1, proj); + max1 = fmaxf(max1, proj); + } + + // Project car2's corners onto the axis + for (int j = 0; j < 4; j++) { + float proj = car2_corners[j][0] * axes[i][0] + car2_corners[j][1] * axes[i][1]; + min2 = fminf(min2, proj); + max2 = fmaxf(max2, proj); + } + + // If there's a gap on this axis, the boxes don't intersect + if (max1 < min2 || min1 > max2) { + return false; // No collision + } + } + + // If we get here, there's no separating axis, so the boxes intersect + return true; +} + +static bool check_z_collision_possibility(const Agent *car1, const Agent *car2) { + float car1_bottom = car1->sim_z; + float car1_top = car1->sim_z + car1->sim_height; + float car2_bottom = car2->sim_z; + float car2_top = car2->sim_z + car2->sim_height; + + return !(car1_top < car2_bottom || car2_top < car1_bottom); +} + +static int collision_check(Drive *env, int agent_idx) { + Agent *agent = &env->agents[agent_idx]; + + if (agent->sim_x == INVALID_POSITION || agent->removed) + return -1; + + int car_collided_with_index = -1; + + // O(N) linear scan over all agents (active + static); no spatial grid used here. + // COLLISION_QUICK_CHECK_DIST (15m) is the real bottleneck — the 5m-cell grid + // neighborhood covers ~50m but this quick check prunes at 15m. + for (int i = 0; i < env->num_agents; i++) { + int index = -1; + if (i < env->active_agent_count) { + index = env->active_agent_indices[i]; + } else { + index = env->static_agent_indices[i - env->active_agent_count]; + } + if (index == -1) + continue; + if (index == agent_idx) + continue; + + Agent *other_agent = &env->agents[index]; + + float dist_sq = ((other_agent->sim_x - agent->sim_x) * (other_agent->sim_x - agent->sim_x) + + (other_agent->sim_y - agent->sim_y) * (other_agent->sim_y - agent->sim_y)); + if (dist_sq > COLLISION_QUICK_CHECK_DIST * COLLISION_QUICK_CHECK_DIST) + continue; + if (!check_z_collision_possibility(agent, other_agent)) + continue; + if (check_obb_collision(agent, other_agent)) { + car_collided_with_index = index; + break; + } + } + + return car_collided_with_index; +} + +// Classify whether a collision is at-fault for the ego agent. +static bool is_at_fault_collision(Drive *env, int agent_idx, int other_idx) { + Agent *agent = &env->agents[agent_idx]; + Agent *other = &env->agents[other_idx]; + + // Rule 1: Collision with stopped vehicle = always at-fault. + if (other->sim_speed < AGENT_STOPPED_SPEED_THRESHOLD) + return true; + + // Rule 2: If ego is stopped = never at-fault. + if (agent->sim_speed < AGENT_STOPPED_SPEED_THRESHOLD) + return false; + + // Rule 3: Rear-bumper collision = not at-fault. + // Check if the other car hit our rear using the heading-aligned relative position. + float dx = other->sim_x - agent->sim_x; + float dy = other->sim_y - agent->sim_y; + float dot = dx * agent->cos_heading + dy * agent->sin_heading; + if (dot < 0) + return false; + + return true; +} + +static inline struct ttc_result default_ttc_result(void) { + return (struct ttc_result){DEFAULT_TTC, -1, INFINITY, 0.0f}; +} + +static inline void ttc_update_min_result(Agent *ego, int other_idx, float distance_to_collision, float closing_speed, + float ttc) { + if (ttc < ego->cached_ttc.min_ttc) { + ego->cached_ttc.min_ttc = ttc; + ego->cached_ttc.other_idx = other_idx; + ego->cached_ttc.distance_to_collision = distance_to_collision; + ego->cached_ttc.closing_speed = closing_speed; + } +} + +// Compute TTC using ego front and other rear points with ahead and lateral corridor filters. +static inline void compute_pairwise_ttc(Agent *ego, int ego_idx, Agent *other, int other_idx) { + if (other_idx == ego_idx) + return; + if (other->sim_x == INVALID_POSITION) + return; + + float ego_x = ego->sim_x; + float ego_y = ego->sim_y; + float other_x = other->sim_x; + float other_y = other->sim_y; + + float ego_heading_x = ego->cos_heading; + float ego_heading_y = ego->sin_heading; + float other_heading_x = other->cos_heading; + float other_heading_y = other->sin_heading; + + float ego_front_x = ego_x + 0.5f * ego->sim_length * ego_heading_x; + float ego_front_y = ego_y + 0.5f * ego->sim_length * ego_heading_y; + float other_rear_x = other_x - 0.5f * other->sim_length * other_heading_x; + float other_rear_y = other_y - 0.5f * other->sim_length * other_heading_y; + + float rel_x = other_rear_x - ego_front_x; + float rel_y = other_rear_y - ego_front_y; + float ahead = rel_x * ego_heading_x + rel_y * ego_heading_y; + if (ahead <= 0.0f) + return; + + float lateral = fabsf(rel_x * ego_heading_y - rel_y * ego_heading_x); + float allowed = 0.5f * (ego->sim_width + other->sim_width); + if (lateral > allowed) + return; + + float distance_to_collision = sqrtf(rel_x * rel_x + rel_y * rel_y); + float ego_radius = 0.5f * ego->sim_width; + float other_radius = 0.5f * other->sim_width; + float combined_radius = ego_radius + other_radius; + + float rel_vx = other->sim_vx - ego->sim_vx; + float rel_vy = other->sim_vy - ego->sim_vy; + float a = rel_vx * rel_vx + rel_vy * rel_vy; + float c = rel_x * rel_x + rel_y * rel_y - combined_radius * combined_radius; + if (c <= 0.0f) { + ttc_update_min_result(ego, other_idx, distance_to_collision, INFINITY, 0.0f); + return; + } + if (a < 1e-6f) + return; + + float b = 2.0f * (rel_x * rel_vx + rel_y * rel_vy); + float disc = b * b - 4.0f * a * c; + if (disc < 0.0f) + return; + + float sqrt_disc = sqrtf(disc); + float inv_two_a = 0.5f / a; + float t1 = (-b - sqrt_disc) * inv_two_a; + float t2 = (-b + sqrt_disc) * inv_two_a; + float ttc = INFINITY; + if (t1 > 0.0f) { + ttc = t1; + } else if (t2 > 0.0f) { + ttc = t2; + } + if (!isfinite(ttc)) + return; + + float closing_speed = sqrtf(a); + ttc_update_min_result(ego, other_idx, distance_to_collision, closing_speed, ttc); +} + +// Compute TTC for a single ego agent against all other agents using ahead and lateral filters. +static void compute_agent_ttc(Drive *env, int ego_idx) { + Agent *ego = &env->agents[ego_idx]; + ego->cached_ttc = default_ttc_result(); + + if (ego->sim_x == INVALID_POSITION) + return; + + for (int j = 0; j < env->num_agents; j++) { + int other_idx; + if (j < env->active_agent_count) { + other_idx = env->active_agent_indices[j]; + } else { + other_idx = env->static_agent_indices[j - env->active_agent_count]; + } + if (other_idx == -1) + continue; + compute_pairwise_ttc(ego, ego_idx, &env->agents[other_idx], other_idx); + } +} + +// Puffer score computation +// Uses hybrid weighted average: multiplier weights (binary gates) + average weights (continuous) +static float calculate_puffer_score(Log *log_agent, int scenario_length, float dt) { + if (!log_agent) { + return 0.0f; + } + + float T = scenario_length * dt; + if (T <= 0.0f) + T = 1.0f; // Avoid division by zero + + float no_at_fault = (log_agent->at_fault_collision_rate > 0) ? 0.0f : 1.0f; + float no_offroad = (log_agent->offroad_rate > 0) ? 0.0f : 1.0f; + float no_red_light = (log_agent->red_light_violation_rate > 0) ? 0.0f : 1.0f; + float making_progress = (log_agent->progress_ratio > 0.2f) ? 1.0f : 0.0f; + + // Driving direction: 1.0 if <=2m, 0.5 if 2-6m, 0 if >6m wrong-way distance + float wrong_dist = log_agent->wrong_way_distance; + float direction_compliance = (wrong_dist <= 2.0f) ? 1.0f : (wrong_dist <= 6.0f) ? 0.5f : 0.0f; + + float multiplier = no_at_fault * no_offroad * no_red_light * making_progress * direction_compliance; + + if (multiplier == 0.0f) + return 0.0f; + + // TTC within bound (>0.95s): weight 5 + float ttc_score = log_agent->ttc_within_bound_rate; // Already 0-1 + + // Progress ratio (capped at 1): weight 5 + float progress_score = fminf(log_agent->progress_ratio, 1.0f); + + // Speed compliance (nuPlan formula): max(0, 1 - sum(violation * dt) / T): weight 4 + float speed_threshold = fmaxf(T, 1e-3f); + float speed_score = fmaxf(0.0f, 1.0f - log_agent->speed_violation_sum / speed_threshold); + + // Comfort (binary per episode): weight 2 + float comfort_score = log_agent->comfort_score; // 0 or 1 + + // Multi-lane (weight 3): tiered score based on accumulated time + float multi_lane_score = log_agent->multi_lane_score; + + // Weighted average + float weighted_sum = + 5 * ttc_score + 5 * progress_score + 4 * speed_score + 3 * multi_lane_score + 2 * comfort_score; + float total_weight = 5 + 5 + 4 + 3 + 2; // = 19 + + return multiplier * (weighted_sum / total_weight); +} + +static void add_log(Drive *env) { + int safe_timestep = (env->timestep > 0) ? env->timestep : 1; + const float progress_ref_speed = 10.0f; + for (int i = 0; i < env->active_agent_count; i++) { + Agent *agent = &env->agents[env->active_agent_indices[i]]; + float episode_duration_s = env->logs[i].episode_length * env->dt; + float reference_progress_distance = progress_ref_speed * episode_duration_s; + reference_progress_distance = fmaxf(reference_progress_distance, 1.0f); + env->logs[i].progress_ratio = agent->distance_since_spawn / reference_progress_distance; + + int offroad = env->logs[i].offroad_rate; + env->log.offroad_rate += offroad; + int collided = env->logs[i].collision_rate; + env->log.collision_rate += collided; + int red_light_violations = env->logs[i].red_light_violation_rate; + env->log.red_light_violation_rate += red_light_violations; + int total_infractions = (offroad || collided || red_light_violations) ? 1 : 0; + float avg_speed_per_agent = env->logs[i].avg_speed_per_agent; + env->log.avg_speed_per_agent += avg_speed_per_agent / safe_timestep; + int num_waypoints_reached = env->logs[i].num_waypoints_reached; + env->log.num_waypoints_reached += num_waypoints_reached; + int num_goals_reached = env->logs[i].num_goals_reached; + env->log.num_goals_reached += num_goals_reached; + // TODO: define better scoring criteria ? + if (num_goals_reached >= 1 && !agent->removed && !agent->stopped) { + env->log.score += 1.0f; + } + if (!offroad && !collided && !red_light_violations && num_waypoints_reached < 1) { + env->log.dnf_rate += 1.0f; + } + env->log.total_distance_travelled += agent->distance_since_spawn; + if (total_infractions > 0) { + env->log.total_infractions += 1.0f; + } + float displacement_error = env->logs[i].avg_displacement_error; + env->log.avg_displacement_error += displacement_error; + env->log.episode_length += env->logs[i].episode_length; + env->log.episode_return += env->logs[i].episode_return; + // Comfort and velocity metrics (normalized per timestep) + env->log.comfort_violation_count += env->logs[i].comfort_violation_count / safe_timestep; + env->log.velocity_progress_sum += env->logs[i].velocity_progress_sum / safe_timestep; + // Lane metrics (normalized per timestep for average per episode) + env->log.lane_center_rate += env->logs[i].lane_center_rate / safe_timestep; + env->log.lane_heading_aligned_rate += env->logs[i].lane_heading_aligned_rate / safe_timestep; + if (env->compute_eval_metrics) { + env->logs[i].progress_ratio = agent->distance_since_spawn / reference_progress_distance; + env->log.at_fault_collision_rate += env->logs[i].at_fault_collision_rate; + env->log.ttc_within_bound_rate += env->logs[i].ttc_within_bound_rate; + env->log.wrong_way_distance += env->logs[i].wrong_way_distance; + env->log.speed_violation_sum += env->logs[i].speed_violation_sum; + env->log.progress_ratio += env->logs[i].progress_ratio; + env->log.comfort_score += env->logs[i].comfort_score; + env->log.ttc_violations += env->logs[i].ttc_violations; + env->log.ttc_samples += env->logs[i].ttc_samples; + env->log.multi_lane_time += env->logs[i].multi_lane_time; + env->log.multi_lane_score += env->logs[i].multi_lane_score; + + float wrong_dist = env->logs[i].wrong_way_distance; + float direction_score = (wrong_dist <= 2.0f) ? 1.0f : (wrong_dist <= 6.0f) ? 0.5f : 0.0f; + env->log.driving_direction_score += direction_score; + + float T = safe_timestep * env->dt; + float speed_compliance = fmaxf(0.0f, 1.0f - env->logs[i].speed_violation_sum / fmaxf(T, 1e-3f)); + env->log.speed_limit_compliance += speed_compliance; + + float making_progress = (env->logs[i].progress_ratio > 0.2f) ? 1.0f : 0.0f; + env->log.making_progress_rate += making_progress; + env->log.puffer_score += calculate_puffer_score(&env->logs[i], safe_timestep, env->dt); + } + + env->log.n += 1; + } + // Log composition counts per agent so vec_log averaging recovers the per-env value + env->log.expert_static_car_count += env->expert_static_agent_count; + env->log.static_car_count += env->static_agent_count; +} + +// ======================================== +// Initialization Functions +// ======================================== + +static void reset_agent_metrics(Drive *env, int agent_idx) { + Agent *agent = &env->agents[agent_idx]; + for (int i = 0; i < NUM_METRICS; i++) { + agent->metrics_array[i] = 0.0f; + } +} + +static void reset_agent_state(Agent *agent) { + agent->cumulative_displacement = 0.0f; + agent->displacement_sample_count = 0; + agent->stopped = 0; + agent->removed = 0; + agent->current_lane_index = -1; + agent->previous_lane_index = -1; + agent->current_route_index = 0; + agent->a_long = 0.0f; + agent->a_lat = 0.0f; + agent->jerk_long = 0.0f; + agent->jerk_lat = 0.0f; + agent->steering_angle = 0.0f; + agent->path_progression = 0.0f; + agent->distance_since_spawn = 0.0f; + agent->closest_path_idx_wp = 0; + // Puffer score tracking reset + agent->wrong_way_distance = 0.0f; + agent->speed_violation_sum = 0.0f; + agent->cached_ttc = default_ttc_result(); + agent->ttc_violations = 0; + agent->ttc_samples = 0; + agent->at_fault_collision = 0; + agent->multi_lane_time = 0.0f; +} + +// Check if a spawn position collides with any existing agent +static bool check_spawn_collision(Drive *env, int num_existing_agents, float spawn_x, float spawn_y, float spawn_z, + float spawn_heading, float spawn_length, float spawn_width, float spawn_height) { + // Create a temporary agent structure for collision checking + Agent temp_agent; + temp_agent.sim_x = spawn_x; + temp_agent.sim_y = spawn_y; + temp_agent.sim_z = spawn_z; + temp_agent.sim_heading = spawn_heading; + temp_agent.cos_heading = cosf(spawn_heading); + temp_agent.sin_heading = sinf(spawn_heading); + temp_agent.yaw_rate = 0.0f; + temp_agent.sim_length = spawn_length; + temp_agent.sim_width = spawn_width; + temp_agent.sim_height = spawn_height; + + // Minimum safe distance + float min_safe_dist_sq = (spawn_length + 5.0f) * (spawn_length + 5.0f); + + for (int i = 0; i < num_existing_agents; i++) { + Agent *other = &env->agents[i]; + + // Skip invalid agents + if (other->sim_x == INVALID_POSITION || other->sim_valid != 1) + continue; + + // Quick distance check first + float dx = other->sim_x - spawn_x; + float dy = other->sim_y - spawn_y; + float dist_sq = dx * dx + dy * dy; + + if (dist_sq > min_safe_dist_sq) + continue; + if (!check_z_collision_possibility(&temp_agent, other)) + continue; + if (check_obb_collision(&temp_agent, other)) + return true; // Collision detected + } + + return false; // No collision +} + +static bool check_spawn_offroad(Drive *env, float spawn_x, float spawn_y, float spawn_heading, float spawn_length, + float spawn_width, float spawn_z) { + // Compute bounding box corners (same as offroad detection in step) + static const float offsets[4][2] = {{1, 1}, {1, -1}, {-1, -1}, {-1, 1}}; + // Increase length and width slightly for spawn offroad check + float half_length = (spawn_length * 1.1f) / 2.0f; + float half_width = (spawn_width * 1.1f) / 2.0f; + float cos_heading = cosf(spawn_heading); + float sin_heading = sinf(spawn_heading); + + float corners[4][2]; + for (int i = 0; i < 4; i++) { + corners[i][0] = + spawn_x + (offsets[i][0] * half_length * cos_heading - offsets[i][1] * half_width * sin_heading); + corners[i][1] = + spawn_y + (offsets[i][0] * half_length * sin_heading + offsets[i][1] * half_width * cos_heading); + } + + // Get neighboring road elements + GridMapEntity entity_list[MAX_ENTITIES_PER_CELL * 25]; + int list_size = + get_neighbors_entities(env, spawn_x, spawn_y, entity_list, MAX_ENTITIES_PER_CELL * 25, collision_offsets, 25); + + // Check intersection with road edges + for (int i = 0; i < list_size; i++) { + if (entity_list[i].entity_type != ENTITY_TYPE_ROAD_ELEMENT) + continue; + + int entity_idx = entity_list[i].entity_idx; + int geometry_idx = entity_list[i].geometry_idx; + RoadMapElement *element = &env->road_elements[entity_idx]; + + if (is_road_edge(element->type)) { + float abs_dz = fabsf(element->z[geometry_idx] - spawn_z); + if (abs_dz > Z_BUFFER) + continue; + float start[2] = {element->x[geometry_idx], element->y[geometry_idx]}; + float end[2] = {element->x[geometry_idx + 1], element->y[geometry_idx + 1]}; + for (int k = 0; k < 4; k++) { + int next = (k + 1) % 4; + if (check_line_intersection(corners[k], corners[next], start, end)) + return true; // Offroad detected + } + } + } + return false; +} + +static bool check_spawn_red_light_violation(Drive *env, float spawn_x, float spawn_y, float spawn_z, + float spawn_heading, float spawn_length, float spawn_width, + float spawn_height, int lane_id) { + Agent temp_agent; + temp_agent.sim_x = spawn_x; + temp_agent.sim_y = spawn_y; + temp_agent.sim_z = spawn_z; + temp_agent.sim_heading = spawn_heading; + temp_agent.cos_heading = cosf(spawn_heading); + temp_agent.sin_heading = sinf(spawn_heading); + temp_agent.sim_length = spawn_length; + temp_agent.sim_width = spawn_width; + temp_agent.sim_height = spawn_height; + + static const float offsets[4][2] = {{1, 1}, {1, -1}, {-1, -1}, {-1, 1}}; + float half_length = spawn_length / 2.0f; + float half_width = spawn_width / 2.0f; + float corners[4][2]; + for (int i = 0; i < 4; i++) { + corners[i][0] = spawn_x + (offsets[i][0] * half_length * temp_agent.cos_heading - + offsets[i][1] * half_width * temp_agent.sin_heading); + corners[i][1] = spawn_y + (offsets[i][0] * half_length * temp_agent.sin_heading + + offsets[i][1] * half_width * temp_agent.cos_heading); + } + + return check_stop_line_crossing(env, &temp_agent, lane_id, corners); +} + +// NOTE: type of function -> void, int, bool ? +static int spawn_agent(Drive *env, int agent_idx, int num_agents) { + Agent *agent = &env->agents[agent_idx]; + + // Free existing route on reset + if (agent->route != NULL) { + free(agent->route); + agent->route = NULL; + } + + // Initialize identity fields + agent->id = agent_idx; + agent->type = VEHICLE; + agent->active_agent = 1; + agent->mark_as_expert = 0; + + // Default vehicle dimensions + // length: [0.8, 7.0] m + // width: [0.8, 3.0] m + // width = min(width, length) + float spawn_length, spawn_width; + if (env->eval_mode) { + // Fixed size for eval mode + spawn_length = random_uniform(2.0f, 5.5f); + spawn_width = random_uniform(1.5f, 2.5f); + } else { + // Random size for training mode + spawn_length = random_uniform(0.8f, 7.0f); + spawn_width = random_uniform(0.8f, 3.0f); + } + if (spawn_width > spawn_length) + spawn_width = spawn_length; + float spawn_height = 1.5f; // Fixed height + + // Set spawn position on start lane + float spawn_x, spawn_y, spawn_z, spawn_heading; + RoadMapElement *start_lane; + int start_lane_idx; + int success = 0; + + // Sampling rejection loop + // TARGET: Only one attempt should be sufficient in most cases + const int MAX_SPAWN_ATTEMPTS = 30; + for (int attempt = 0; attempt < MAX_SPAWN_ATTEMPTS; attempt++) { + int chosen_lane_idx = -1; + + int list_idx = rand() % env->grid_map->num_drivable_grid_cell; + int grid_idx = env->grid_map->grid_index_drivable[list_idx]; + + GridMapEntity cell_candidates[MAX_ENTITIES_PER_CELL]; + int candidate_count = 0; + + for (int i = 0; i < env->grid_map->cell_entities_count[grid_idx]; i++) { + GridMapEntity entity = env->grid_map->cells[grid_idx][i]; + + if (entity.entity_type == ENTITY_TYPE_ROAD_ELEMENT) { + if (is_drivable_road_lane(env->road_elements[entity.entity_idx].type)) { + cell_candidates[candidate_count++] = entity; + } + } + } + + if (candidate_count == 0) + continue; + + GridMapEntity chosen_entity = cell_candidates[rand() % candidate_count]; + chosen_lane_idx = chosen_entity.entity_idx; + + start_lane_idx = chosen_lane_idx; + start_lane = &env->road_elements[start_lane_idx]; + + spawn_x = start_lane->x[chosen_entity.geometry_idx]; + spawn_y = start_lane->y[chosen_entity.geometry_idx]; + spawn_z = start_lane->z[chosen_entity.geometry_idx]; + spawn_heading = start_lane->headings[chosen_entity.geometry_idx]; + + // Check for collision with existing/already-reset agents + if (check_spawn_collision(env, num_agents, spawn_x, spawn_y, spawn_z, spawn_heading, spawn_length, spawn_width, + spawn_height)) + continue; + + // Check for offroad (vehicle corners intersecting road edges) + if (check_spawn_offroad(env, spawn_x, spawn_y, spawn_heading, spawn_length, spawn_width, spawn_z)) + continue; + + // Check for red light violation at spawn (vehicle corners intersecting stop lines) + if (check_spawn_red_light_violation(env, spawn_x, spawn_y, spawn_z, spawn_heading, spawn_length, spawn_width, + spawn_height, start_lane->id)) + continue; + + success = 1; + break; + } + + if (!success) { + printf("[GIGAFLOW WARNING] -> Failed to find a collision-free spawn position for agent %d\n", agent->id); + return 0; // Failed to find collision-free spawn + } + + // Update simulation state + agent->sim_x = spawn_x; + agent->sim_y = spawn_y; + agent->sim_z = spawn_z; + agent->sim_heading = spawn_heading; + agent->cos_heading = cosf(spawn_heading); + agent->sin_heading = sinf(spawn_heading); + agent->sim_length = spawn_length; + agent->sim_width = spawn_width; + agent->sim_height = spawn_height; + agent->sim_valid = 1; + agent->wheelbase = 0.6f * spawn_length; + + float spawn_speed = clip(env->spawn_initial_speed, 0.0f, MAX_SPEED); + agent->sim_vx = spawn_speed * agent->cos_heading; + agent->sim_vy = spawn_speed * agent->sin_heading; + agent->yaw_rate = 0.0f; + update_agent_speed(agent); + + // Compute initial route + if (!compute_new_route(env, agent_idx, start_lane->id)) { + printf("[GIGAFLOW WARNING] -> Failed to compute a new route for agent %d\n", agent->id); + return 0; // Failed to compute new goal + } + + // Compute initial goal + compute_goals(env, agent_idx); + + return 1; // Success +} + +static void set_start_position(Drive *env) { + bool is_log_replay = (env->control_mode == CONTROL_SDC_ONLY); + + for (int i = 0; i < env->num_total_agents; i++) { + int is_active = 0; + for (int j = 0; j < env->active_agent_count; j++) { + if (env->active_agent_indices[j] == i) { + is_active = 1; + break; + } + } + Agent *agent = &env->agents[i]; + + // Initialize simulation trajectory from logged trajectory at init_steps + if (env->simulation_mode == SIMULATION_REPLAY) { + // Clamp init_steps to ensure we don't go out of bounds + int step = env->init_steps; + if (step >= agent->trajectory_length) + step = agent->trajectory_length - 1; + if (step < 0) + step = 0; + + // For agents invalid at init_steps, set INVALID_POSITION + // move_expert will update them when they become valid + if (agent->log_valid[step] != 1) { + invalidate_agent(agent); + agent->sim_length = agent->log_length[step]; + agent->sim_width = agent->log_width[step]; + agent->sim_height = agent->log_height[step]; + continue; + } + + agent->sim_x = agent->log_trajectory_x[step]; + agent->sim_y = agent->log_trajectory_y[step]; + agent->sim_z = agent->log_trajectory_z[step]; + agent->sim_heading = agent->log_heading[step]; + agent->cos_heading = cosf(agent->sim_heading); + agent->sin_heading = sinf(agent->sim_heading); + agent->sim_valid = agent->log_valid[step]; + agent->sim_length = agent->log_length[step]; + agent->sim_width = agent->log_width[step]; + agent->sim_height = agent->log_height[step]; + // Estimate wheelbase as 60% of length + agent->wheelbase = 0.6f * agent->sim_length; + + if (agent->type == UNKNOWN) + continue; + + if (is_active == 0) { + agent->sim_vx = 0.0f; + agent->sim_vy = 0.0f; + agent->yaw_rate = 0.0f; + agent->sim_speed = 0.0f; + agent->sim_speed_signed = 0.0f; + } else { + agent->yaw_rate = compute_log_yaw_rate(agent, step, env->dt); + agent->sim_vx = agent->log_velocity_x[step]; + agent->sim_vy = agent->log_velocity_y[step]; + update_agent_speed(agent); + } + + // Shrink width and length slightly to avoid initial collisions (not in log-replay) + if (!is_log_replay) { + agent->sim_length *= INIT_COLLISION_SHRINK_FACTOR; + agent->sim_width *= INIT_COLLISION_SHRINK_FACTOR; + } + } + + // Reset agent metrics and state + reset_agent_metrics(env, i); + reset_agent_state(agent); + generate_reward_coefs(env, agent); + } +} + +static bool should_control_agent(Drive *env, int agent_idx) { + // Check if we have room for more agents or are already at capacity + if (env->num_controllable_agents != 0 && env->active_agent_count >= env->num_controllable_agents) { + return false; + } + + Agent *agent = &env->agents[agent_idx]; + + if (env->control_mode == CONTROL_SDC_ONLY) { + return agent_idx == 0 && agent->route_length != 0; + } + + if (env->control_mode == CONTROL_WOSAC) { + for (int j = 0; j < env->num_tracks_to_predict; j++) { + if (env->tracks_to_predict[j] == agent_idx) { + return true; + } + } + return false; + } + + // Standard mode: check type, distance to goal, and expert status + bool type_is_controllable = false; + if (env->control_mode == CONTROL_VEHICLES) { + type_is_controllable = (agent->type == VEHICLE); + } else { // CONTROL_AGENTS mode + type_is_controllable = is_controllable_agent(agent->type); + } + + if (!type_is_controllable || agent->mark_as_expert) + return false; + + // Control if the agent has a route to follow + return agent->route_length != 0; +} + +void set_active_agents(Drive *env) { + // Initialize + env->active_agent_count = 0; // Policy-controlled agents + env->static_agent_count = 0; // Non-moving background agents + env->expert_static_agent_count = 0; // Expert replay agents (non-controlled) + env->num_agents = 0; // Total agents created + + // In GIGAFLOW mode, spawn agents dynamically on the map + if (env->simulation_mode == SIMULATION_GIGAFLOW) { + int num_agents_to_create = env->num_controllable_agents; + + // Initialize agents for GIGAFLOW mode + env->agents = (Agent *)calloc(num_agents_to_create, sizeof(Agent)); + + int successfully_created = 0; + for (int i = 0; i < num_agents_to_create; i++) { + // Pass the number of already successfully created agents for collision checking + if (spawn_agent(env, i, successfully_created)) { + successfully_created++; + } else { + // Failed spawn: ensure agent is properly invalidated + invalidate_agent(&env->agents[i]); + env->agents[i].removed = 1; + } + } + + env->num_total_agents = successfully_created; + + // Set up active agent indices + env->active_agent_indices = (int *)malloc(env->num_total_agents * sizeof(int)); + env->static_agent_indices = NULL; + env->expert_static_agent_indices = NULL; + + for (int i = 0; i < env->num_total_agents; i++) + env->active_agent_indices[i] = i; + + env->active_agent_count = env->num_total_agents; + env->num_agents = env->num_total_agents; + env->static_agent_count = 0; + env->expert_static_agent_count = 0; + + return; + } + + // In REPLAY mode, determine which agents to control + bool is_log_replay = (env->control_mode == CONTROL_SDC_ONLY); + // In log-replay mode, no cap on actors + int max_agents = is_log_replay ? env->num_total_agents : env->num_max_agents; + + int *active_agent_indices = (int *)malloc(max_agents * sizeof(int)); + int *static_agent_indices = (int *)malloc(max_agents * sizeof(int)); + int *expert_static_agent_indices = (int *)malloc(max_agents * sizeof(int)); + + // Iterate through entities to find agents to create and/or control + for (int i = 0; i < env->num_total_agents && env->num_agents < max_agents; i++) { + + Agent *agent = &env->agents[i]; + + // Skip if not valid at initialization + if (agent->log_valid[env->init_steps] != 1 && !is_log_replay) { + continue; + } + + // Determine if entity should be created + bool should_create = false; + if (is_log_replay) { + should_create = true; // Log-replay: all valid agents + } else if (env->init_mode == INIT_ALL_VALID) { + should_create = true; // All valid entities + } else if (env->control_mode == CONTROL_VEHICLES) { + should_create = (agent->type == VEHICLE); + } else { // Control all agents + should_create = (is_controllable_agent(agent->type)); + } + + if (!should_create) + continue; + + env->num_agents++; + + // Determine if this agent should be policy-controlled + bool is_controlled = should_control_agent(env, i); + + if (is_controlled) { + active_agent_indices[env->active_agent_count] = i; + env->active_agent_count++; + env->agents[i].active_agent = 1; + } else if (is_log_replay || env->init_mode != INIT_ONLY_CONTROLLABLE_AGENTS) { + // In log-replay mode, all non-controlled agents become expert_static + static_agent_indices[env->static_agent_count] = i; + env->static_agent_count++; + env->agents[i].active_agent = 0; + if (is_log_replay || env->agents[i].mark_as_expert == 1 || env->active_agent_count == env->num_max_agents) { + expert_static_agent_indices[env->expert_static_agent_count] = i; + env->expert_static_agent_count++; + env->agents[i].mark_as_expert = 1; + } + } + } + + // Set up initial active agents + env->active_agent_indices = (int *)malloc(env->active_agent_count * sizeof(int)); + env->static_agent_indices = (int *)malloc(env->static_agent_count * sizeof(int)); + env->expert_static_agent_indices = (int *)malloc(env->expert_static_agent_count * sizeof(int)); + for (int i = 0; i < env->active_agent_count; i++) { + env->active_agent_indices[i] = active_agent_indices[i]; + }; + for (int i = 0; i < env->static_agent_count; i++) { + env->static_agent_indices[i] = static_agent_indices[i]; + } + for (int i = 0; i < env->expert_static_agent_count; i++) { + env->expert_static_agent_indices[i] = expert_static_agent_indices[i]; + } + + // Free temporary buffers + free(active_agent_indices); + free(static_agent_indices); + free(expert_static_agent_indices); + + if (env->num_controllable_agents > 0 && env->active_agent_count != env->num_controllable_agents) { + printf("ERROR Between my_shared and init : Mismatch in active agent count: %d vs %d\n", env->active_agent_count, + env->num_controllable_agents); + } + + return; +} + +void move_expert(Drive *env, float *actions, int agent_idx) { + if (env->simulation_mode == SIMULATION_GIGAFLOW) { + printf("[GIGAFLOW ERROR] -> move_expert() called in GIGAFLOW mode\n"); + return; + } + + bool is_log_replay = (env->control_mode == CONTROL_SDC_ONLY); + + Agent *agent = &env->agents[agent_idx]; + int t = env->timestep; + + // If agent is invalid at this timestep, set simulated state to invalid + if (t < 0 || t >= agent->trajectory_length || agent->log_valid[t] == 0) { + invalidate_agent(agent); + return; + } + + // Copy from logged trajectory to simulated state + agent->sim_x = agent->log_trajectory_x[t]; + agent->sim_y = agent->log_trajectory_y[t]; + agent->sim_z = agent->log_trajectory_z[t]; + agent->sim_heading = agent->log_heading[t]; + agent->cos_heading = cosf(agent->sim_heading); + agent->sin_heading = sinf(agent->sim_heading); + agent->sim_valid = agent->log_valid[t]; + + if (is_log_replay) { + agent->sim_length = agent->log_length[t]; + agent->sim_width = agent->log_width[t]; + agent->sim_height = agent->log_height[t]; + agent->wheelbase = 0.6f * agent->sim_length; + } + + agent->yaw_rate = compute_log_yaw_rate(agent, t, env->dt); + agent->sim_vx = agent->log_velocity_x[t]; + agent->sim_vy = agent->log_velocity_y[t]; + + update_agent_speed(agent); + agent->sim_valid = agent->log_valid[t]; +} + +void remove_bad_trajectories(Drive *env) { + + if (env->control_mode == CONTROL_WOSAC) { + return; // Leave all trajectories in WOSAC control mode + } + + set_start_position(env); int collided_agents[env->active_agent_count]; int collided_with_indices[env->active_agent_count]; memset(collided_agents, 0, env->active_agent_count * sizeof(int)); @@ -1407,21 +3187,20 @@ void remove_bad_trajectories(Drive *env) { collided_with_indices[i] = -1; } // move experts through trajectories to check for collisions and remove as illegal agents - for (int t = 0; t < env->episode_length; t++) { + for (int t = 0; t < env->scenario_length; t++) { for (int i = 0; i < env->active_agent_count; i++) { int agent_idx = env->active_agent_indices[i]; move_expert(env, env->actions, agent_idx); } for (int i = 0; i < env->expert_static_agent_count; i++) { int expert_idx = env->expert_static_agent_indices[i]; - if (env->entities[expert_idx].x == INVALID_POSITION) + if (env->agents[expert_idx].sim_x == INVALID_POSITION) continue; move_expert(env, env->actions, expert_idx); } // check collisions for (int i = 0; i < env->active_agent_count; i++) { int agent_idx = env->active_agent_indices[i]; - env->entities[agent_idx].collision_state = 0; int collided_with_index = collision_check(env, agent_idx); if ((collided_with_index >= 0) && collided_agents[i] == 0) { collided_agents[i] = 1; @@ -1438,50 +3217,73 @@ void remove_bad_trajectories(Drive *env) { int static_agent_idx = env->static_agent_indices[j]; if (static_agent_idx != collided_with_indices[i]) continue; - env->entities[static_agent_idx].traj_x[0] = INVALID_POSITION; - env->entities[static_agent_idx].traj_y[0] = INVALID_POSITION; + env->agents[static_agent_idx].log_trajectory_x[0] = INVALID_POSITION; + env->agents[static_agent_idx].log_trajectory_y[0] = INVALID_POSITION; + env->agents[static_agent_idx].log_valid[0] = 0; } } env->timestep = 0; } -void init_goal_positions(Drive *env) { - for (int x = 0; x < env->active_agent_count; x++) { - int agent_idx = env->active_agent_indices[x]; - env->entities[agent_idx].init_goal_x = env->entities[agent_idx].goal_position_x; - env->entities[agent_idx].init_goal_y = env->entities[agent_idx].goal_position_y; - } -} - void init(Drive *env) { env->human_agent_idx = 0; env->timestep = 0; - env->entities = load_map_binary(env->map_name, env); - set_means(env); + load_map_binary(env->map_name, env); init_grid_map(env); - env->grid_map->vision_range = 21; // TODO: Why is this hardcoded? + env->grid_map->vision_range = 2 * VISION_HALF_RANGE + 1; init_neighbor_offsets(env); cache_neighbor_offsets(env); env->logs_capacity = 0; set_active_agents(env); env->logs_capacity = env->active_agent_count; - remove_bad_trajectories(env); + if (env->simulation_mode == SIMULATION_REPLAY) { + remove_bad_trajectories(env); + } set_start_position(env); - init_goal_positions(env); + if (env->simulation_mode == SIMULATION_GIGAFLOW) { + int steps = env->scenario_length; + if (steps > 0) { + for (int i = 0; i < env->num_traffic_elements; i++) { + TrafficControlElement *traffic = &env->traffic_elements[i]; + if (traffic->type != TRAFFIC_LIGHT) + continue; + if (traffic->states && traffic->state_length != steps) { + free(traffic->states); + traffic->states = NULL; + } + if (traffic->states == NULL) { + traffic->states = (int *)malloc(steps * sizeof(int)); + if (traffic->states == NULL) { + traffic->state_length = 0; + continue; + } + } + traffic->state_length = steps; + } + } + generate_traffic_light_states(env); + } env->logs = (Log *)calloc(env->active_agent_count, sizeof(Log)); -} -void close_client(Client *client); + if (env->simulation_mode == SIMULATION_REPLAY) { + for (int i = 0; i < env->active_agent_count; i++) { + int agent_idx = env->active_agent_indices[i]; + build_path(env, agent_idx); + compute_goals(env, agent_idx); + } + } +} void c_close(Drive *env) { - if (env->client != NULL) { - close_client(env->client); - env->client = NULL; - } - for (int i = 0; i < env->num_entities; i++) { - free_entity(&env->entities[i]); - } - free(env->entities); + for (int i = 0; i < env->num_total_agents; i++) + free_agent(&env->agents[i]); + for (int i = 0; i < env->num_road_elements; i++) + free_road_element(&env->road_elements[i]); + for (int i = 0; i < env->num_traffic_elements; i++) + free_traffic_element(&env->traffic_elements[i]); + free(env->agents); + free(env->road_elements); + free(env->traffic_elements); free(env->active_agent_indices); free(env->logs); // GridMap cleanup @@ -1491,6 +3293,7 @@ void c_close(Drive *env) { } free(env->grid_map->cells); free(env->grid_map->cell_entities_count); + free(env->grid_map->grid_index_drivable); free(env->neighbor_offsets); for (int i = 0; i < grid_cell_count; i++) { @@ -1501,20 +3304,50 @@ void c_close(Drive *env) { free(env->grid_map); free(env->static_agent_indices); free(env->expert_static_agent_indices); + free(env->objects_of_interest); + free(env->tracks_to_predict); + free_lane_graph(&env->lane_graph); + free(env->map_name); free(env->ini_file); - free(env->tracks_to_predict_indices); - env->tracks_to_predict_indices = NULL; +} + +static int compute_observation_size(Drive *env) { + int ego_dim = (env->dynamics_model == JERK) ? EGO_FEATURES_JERK : EGO_FEATURES_CLASSIC; + int num_target_waypoints = env->num_target_waypoints; + if (num_target_waypoints > MAX_TARGET_WAYPOINTS) { + num_target_waypoints = MAX_TARGET_WAYPOINTS; + } + + if (env->max_stop_sign_observations > 0) { + ego_dim += 1; + } + + int max_obs = ego_dim + PARTNER_FEATURES * env->max_partner_observations + + ROAD_FEATURES * (env->max_lane_segment_observations + env->max_boundary_segment_observations) + + TRAFFIC_LIGHT_FEATURES * env->max_traffic_light_observations + + STOP_SIGN_FEATURES * env->max_stop_sign_observations; + if (env->reward_conditioning) { + max_obs += NUM_REWARD_COEFS; + } + if (env->target_type == TARGET_STATIC) { + max_obs += num_target_waypoints * STATIC_TARGET_FEATURES; + } else if (env->target_type == TARGET_DYNAMIC) { + max_obs += num_target_waypoints * DYNAMIC_TARGET_FEATURES; + } + + return max_obs; } void allocate(Drive *env) { init(env); - int ego_dim = (env->dynamics_model == JERK) ? EGO_FEATURES_JERK : EGO_FEATURES_CLASSIC; - int max_obs = ego_dim + PARTNER_FEATURES * (MAX_AGENTS - 1) + ROAD_FEATURES * MAX_ROAD_SEGMENT_OBSERVATIONS; + int max_obs = compute_observation_size(env); + env->observations = (float *)calloc(env->active_agent_count * max_obs, sizeof(float)); env->actions = (float *)calloc(env->active_agent_count * 2, sizeof(float)); env->rewards = (float *)calloc(env->active_agent_count, sizeof(float)); env->terminals = (unsigned char *)calloc(env->active_agent_count, sizeof(unsigned char)); env->truncations = (unsigned char *)calloc(env->active_agent_count, sizeof(unsigned char)); + env->masks = (unsigned char *)calloc(env->active_agent_count, sizeof(unsigned char)); } void free_allocated(Drive *env) { @@ -1523,1666 +3356,1317 @@ void free_allocated(Drive *env) { free(env->rewards); free(env->terminals); free(env->truncations); + free(env->masks); c_close(env); } -float clipSpeed(float speed) { - const float maxSpeed = MAX_SPEED; - if (speed > maxSpeed) - return maxSpeed; - if (speed < -maxSpeed) - return -maxSpeed; - return speed; +// ======================================== +// Extra C API Functions +// ======================================== + +int get_track_id_or_placeholder(Drive *env, int agent_idx) { + if (env->tracks_to_predict == NULL || env->num_tracks_to_predict == 0) { + return -1; + } + for (int k = 0; k < env->num_tracks_to_predict; k++) { + if (env->tracks_to_predict[k] == agent_idx) { + return env->tracks_to_predict[k]; + } + } + return -1; +} + +void c_get_global_agent_state(Drive *env, float *x_out, float *y_out, float *z_out, float *heading_out, int *id_out, + float *length_out, float *width_out) { + for (int i = 0; i < env->active_agent_count; i++) { + int agent_idx = env->active_agent_indices[i]; + Agent *agent = &env->agents[agent_idx]; + + // For WOSAC, we need the original world coordinates, so we add the world means back + x_out[i] = agent->sim_x + env->world_mean_x; + y_out[i] = agent->sim_y + env->world_mean_y; + z_out[i] = agent->sim_z; + heading_out[i] = agent->sim_heading; + id_out[i] = get_track_id_or_placeholder(env, agent_idx); + length_out[i] = agent->sim_length; + width_out[i] = agent->sim_width; + } } -float normalize_heading(float heading) { - if (heading > M_PI) - heading -= 2 * M_PI; - if (heading < -M_PI) - heading += 2 * M_PI; - return heading; +void c_get_global_ground_truth_trajectories(Drive *env, float *x_out, float *y_out, float *z_out, float *heading_out, + int *valid_out, int *id_out, int *scenario_id_out) { + for (int i = 0; i < env->active_agent_count; i++) { + int agent_idx = env->active_agent_indices[i]; + Agent *agent = &env->agents[agent_idx]; + id_out[i] = get_track_id_or_placeholder(env, agent_idx); + scenario_id_out[i] = 0; // TODO: FIXME + + for (int t = env->init_steps; t < agent->trajectory_length; t++) { + int out_idx = i * (agent->trajectory_length - env->init_steps) + (t - env->init_steps); + // Add world means back to get original world coordinates + x_out[out_idx] = agent->log_trajectory_x[t] + env->world_mean_x; + y_out[out_idx] = agent->log_trajectory_y[t] + env->world_mean_y; + z_out[out_idx] = agent->log_trajectory_z[t]; + heading_out[out_idx] = agent->log_heading[t]; + valid_out[out_idx] = agent->log_valid[t]; + } + } +} + +void c_get_road_edge_counts(Drive *env, int *num_polylines_out, int *total_points_out) { + int count = 0, points = 0; + for (int i = 0; i < env->num_road_elements; i++) { + if (is_road_edge(env->road_elements[i].type)) { + count++; + points += env->road_elements[i].segment_length; + } + } + *num_polylines_out = count; + *total_points_out = points; +} + +void c_get_road_edge_polylines(Drive *env, float *x_out, float *y_out, int *lengths_out, int *scenario_ids_out) { + int poly_idx = 0, pt_idx = 0; + for (int i = 0; i < env->num_road_elements; i++) { + RoadMapElement *e = &env->road_elements[i]; + if (is_road_edge(e->type)) { + lengths_out[poly_idx] = e->segment_length; + scenario_ids_out[poly_idx] = 0; // TODO: FIXME + for (int j = 0; j < e->segment_length; j++) { + x_out[pt_idx] = e->x[j] + env->world_mean_x; + y_out[pt_idx] = e->y[j] + env->world_mean_y; + pt_idx++; + } + poly_idx++; + } + } } -float normalize_value(float value, float min, float max) { return (value - min) / (max - min); } +// ======================================== +// Core Simulation Functions +// ======================================== + +static void compute_metrics(Drive *env, int agent_idx) { + Agent *agent = &env->agents[agent_idx]; + + reset_agent_metrics(env, agent_idx); + + if (agent->sim_x == INVALID_POSITION) + return; // invalid agent position -void move_dynamics(Drive *env, int action_idx, int agent_idx) { - Entity *agent = &env->entities[agent_idx]; - if (agent->removed) + // Current agent is offgrid, treat as offroad + if (get_grid_index(env, agent->sim_x, agent->sim_y) == -1) { + agent->metrics_array[OFFROAD_IDX] = 1.0f; + if (env->offroad_behavior == STOP_AGENT && !agent->stopped) { + agent->stopped = 1; + } else if (env->offroad_behavior == REMOVE_AGENT && !agent->removed) { + agent->removed = 1; + } return; + } + + // Compute log-replay metrics + if (env->simulation_mode == SIMULATION_REPLAY) { + // Compute displacement error + float displacement_error = compute_displacement_error(agent, env->timestep); + if (displacement_error > 0.0f) { // Only count valid displacements + agent->cumulative_displacement += displacement_error; + agent->displacement_sample_count++; + + // Compute running average + agent->metrics_array[AVG_DISPLACEMENT_ERROR_IDX] = + agent->cumulative_displacement / agent->displacement_sample_count; + } + } + + bool is_offroad = false; + float half_length = agent->sim_length / 2.0f; + float half_width = agent->sim_width / 2.0f; + // Use cached trig values from move_dynamics + float cos_heading = agent->cos_heading; + float sin_heading = agent->sin_heading; + + // Track best candidate by combined distance/heading score + float best_score = 1e9f; + int best_candidate_entity_idx = -1; + int best_candidate_geometry_idx = -1; + float best_candidate_signed_lane_distance = 0.0f; + float best_candidate_lane_heading = 0.0f; + + float corners[4][2]; + for (int i = 0; i < 4; i++) { + corners[i][0] = + agent->sim_x + (offsets[i][0] * half_length * cos_heading - offsets[i][1] * half_width * sin_heading); + corners[i][1] = + agent->sim_y + (offsets[i][0] * half_length * sin_heading + offsets[i][1] * half_width * cos_heading); + } + + GridMapEntity entity_list[MAX_ENTITIES_PER_CELL * 25]; // Array big enough for all neighboring cells + int list_size = get_neighbors_entities(env, agent->sim_x, agent->sim_y, entity_list, MAX_ENTITIES_PER_CELL * 25, + collision_offsets, 25); + + // Vehicle-width based distance threshold (3x width) + float max_distance_threshold = 3.0f * agent->sim_width; + + // Track already-checked drivable lanes to avoid redundant processing + int checked_lanes[MAX_CHECKED_LANES]; + int num_checked_lanes = 0; + + // Loop through road entities and compute associated metrics (offroad, lane alignment) + for (int i = 0; i < list_size; i++) { + if (entity_list[i].entity_idx == -1) + continue; + + // Get the road element (only road elements are in grid) + if (entity_list[i].entity_type != ENTITY_TYPE_ROAD_ELEMENT) + continue; + + int entity_idx = entity_list[i].entity_idx; + int geometry_idx = entity_list[i].geometry_idx; + RoadMapElement *element = &env->road_elements[entity_idx]; + + // Check for offroad collision with road edges + if (is_road_edge(element->type)) { + float start[2] = {element->x[geometry_idx], element->y[geometry_idx]}; + float end[2] = {element->x[geometry_idx + 1], element->y[geometry_idx + 1]}; + float abs_dz = fabsf(element->z[geometry_idx] - agent->sim_z); + if (abs_dz > Z_BUFFER) + continue; + for (int k = 0; k < 4; k++) { // Check each edge of the bounding box + int next = (k + 1) % 4; + if (check_line_intersection(corners[k], corners[next], start, end)) { + is_offroad = true; + break; + } + } + } + + if (is_offroad) + break; + + int closest_seg_idx; + float signed_dist_out; + float lane_heading_out; + float score = score_lane_candidate(env, agent->sim_heading, checked_lanes, &num_checked_lanes, &closest_seg_idx, + entity_list[i], agent->sim_x, agent->sim_y, max_distance_threshold, + agent->current_lane_index, &signed_dist_out, &lane_heading_out); + + if (score >= 0.0f && score < best_score) { + best_score = score; + best_candidate_entity_idx = entity_idx; + best_candidate_geometry_idx = closest_seg_idx; + best_candidate_signed_lane_distance = signed_dist_out; + best_candidate_lane_heading = lane_heading_out; + } + } + + // Update lane alignment metric (running average) + if (best_candidate_entity_idx != -1) { + agent->previous_lane_index = agent->current_lane_index; + agent->current_lane_index = best_candidate_entity_idx; + agent->current_lane_geometry_idx = best_candidate_geometry_idx; + + // Lane distance and angle metrics (GIGAFLOW Frenet coordinates) + // x_f = lateral offset from lane center (left = negative, right = positive) + agent->metrics_array[LANE_DIST_IDX] = best_candidate_signed_lane_distance; + // Multi-lane detection: vehicle edge exceeds lane boundary + float edge_dist = fabsf(best_candidate_signed_lane_distance) + half_width; + if (env->compute_eval_metrics && edge_dist > MULTI_LANE_THRESHOLD && agent->sim_speed > 0.0f) { + agent->multi_lane_time += env->dt; + } + // theta_f = angle relative to lane heading + float theta_f = compute_heading_diff(agent->sim_heading, best_candidate_lane_heading); + agent->metrics_array[LANE_ANGLE_IDX] = cosf(theta_f); // Store cos(θ_f) + } else { + // Agent not on any lane - use "bad" values to indicate offroad state + agent->previous_lane_index = -1; + agent->current_lane_index = -1; + agent->current_lane_geometry_idx = -1; + agent->metrics_array[LANE_DIST_IDX] = LANE_DISTANCE_NORMALIZATION; // Max distance (far from lane) + agent->metrics_array[LANE_ANGLE_IDX] = 0.0f; // Perpendicular (no alignment) + } + + agent->closest_path_idx_wp = get_closest_waypoint_index_on_path(env, agent_idx); + + // Speed limit metric (CUSTOM) + float target_speed = 15.0f; // Default target speed + int current_lane_index = agent->current_lane_index; + if (current_lane_index != -1 && env->road_elements[current_lane_index].speed_limit > 0) { + target_speed = env->road_elements[current_lane_index].speed_limit; + } + // Binary overspeed metric, 1.0 if overspeeding by more than 2 m/s + agent->metrics_array[SPEED_LIMIT_IDX] = (agent->sim_speed > target_speed + 2.0f) ? 1.0f : 0.0f; + if (env->compute_eval_metrics) { + agent->speed_violation_sum += fmaxf(agent->sim_speed - target_speed, 0.0f) * env->dt; + } + + // Velocity metric (GIGAFLOW) - forward progress aligned with lane + const float VELOCITY_MIN_SPEED = 2.5f; // m/s + if (agent->sim_speed > VELOCITY_MIN_SPEED && best_candidate_entity_idx != -1) { + float cos_theta = agent->metrics_array[LANE_ANGLE_IDX]; + agent->metrics_array[VELOCITY_PROGRESS_IDX] = fmaxf(cos_theta, 0.0f); + if (env->compute_eval_metrics && cos_theta < 0.0f) { + agent->wrong_way_distance += agent->sim_speed * env->dt; + } + } else { + agent->metrics_array[VELOCITY_PROGRESS_IDX] = 0.0f; + } + + // Comfort metric (GIGAFLOW) + const float COMFORT_ACCEL_THRESHOLD = 3.0f; // m/s² + const float COMFORT_JERK_THRESHOLD = 5.0f; // m/s³ + int accel_violation = + (fabsf(agent->a_long) > COMFORT_ACCEL_THRESHOLD) + (fabsf(agent->a_lat) > COMFORT_ACCEL_THRESHOLD); + int jerk_violation = + (fabsf(agent->jerk_long) > COMFORT_JERK_THRESHOLD || fabsf(agent->jerk_lat) > COMFORT_JERK_THRESHOLD) ? 1 : 0; + agent->metrics_array[COMFORT_VIOLATION_IDX] = (float)(accel_violation + jerk_violation); + + // Handle terminal events - NOTE: move it elsewhere? + // IMPORTANT: early returns after offroad and collision enforce mutual exclusivity of + // terminal flags. Red_light and stop_sign have NO early return and can coexist. + // Order matters: offroad > collision > red_light/stop_sign. + + // Priority 1: Handle offroad + if (is_offroad) { + agent->metrics_array[OFFROAD_IDX] = 1.0f; + if (env->offroad_behavior == STOP_AGENT && !agent->stopped) { // Stop + agent->stopped = 1; + } else if (env->offroad_behavior == REMOVE_AGENT && !agent->removed) { + agent->removed = 1; + } + return; // early return: no other terminal flags set when offroad + } + + // Priority 2: Handle vehicle collision + int car_collided_with_index = collision_check(env, agent_idx); + + if (car_collided_with_index != -1) { + agent->metrics_array[COLLISION_IDX] = 1.0f; + // Track at-fault collisions for evaluation metrics. + if (env->compute_eval_metrics && is_at_fault_collision(env, agent_idx, car_collided_with_index)) { + agent->at_fault_collision = 1; + agent->metrics_array[AT_FAULT_COLLISION_IDX] = 1.0f; + } + if (env->collision_behavior == STOP_AGENT && !agent->stopped) { // Stop + agent->stopped = 1; + } else if (env->collision_behavior == REMOVE_AGENT && !agent->removed) { + agent->removed = 1; + } + + return; // early return: red_light/stop_sign not checked after collision + } - if (agent->stopped) { - agent->vx = 0.0f; - agent->vy = 0.0f; - return; + // Priority 3: Handle red light violation + // Guard: violations are invisible to rewards when max_traffic_light_observations == 0 + if (env->max_traffic_light_observations && check_red_light_violation(env, agent_idx)) { + agent->metrics_array[RED_LIGHT_IDX] = 1.0f; + if (env->traffic_light_behavior == STOP_AGENT && !agent->stopped) { + agent->stopped = 1; + } else if (env->traffic_light_behavior == REMOVE_AGENT && !agent->removed) { + agent->removed = 1; + } } - if (env->dynamics_model == CLASSIC) { - // Classic dynamics model - float acceleration = 0.0f; - float steering = 0.0f; + float distance_to_goal = + compute_euclidean_distance(agent->sim_x, agent->sim_y, agent->goal_position_x, agent->goal_position_y); + float goal_z_dist = fabsf(agent->sim_z - agent->goal_position_z); - if (env->action_type == 1) { // continuous - float (*action_array_f)[2] = (float (*)[2])env->actions; - acceleration = action_array_f[action_idx][0]; - steering = action_array_f[action_idx][1]; + // Goal reaching + if (distance_to_goal < agent->reward_coefs[REWARD_COEF_GOAL_RADIUS] && goal_z_dist < Z_BUFFER) { + agent->metrics_array[REACHED_GOAL_IDX] = 1.0f; + agent->current_goal_idx++; + } - acceleration *= ACCELERATION_VALUES[6]; - steering *= STEERING_VALUES[12]; - } else { // discrete - // Interpret action as a single integer: a = accel_idx * num_steer + steer_idx - int *action_array = (int *)env->actions; - int num_steer = sizeof(STEERING_VALUES) / sizeof(STEERING_VALUES[0]); - int action_val = action_array[action_idx]; - int acceleration_index = action_val / num_steer; - int steering_index = action_val % num_steer; - acceleration = ACCELERATION_VALUES[acceleration_index]; - steering = STEERING_VALUES[steering_index]; - } + return; +} - // Current state - float x = agent->x; - float y = agent->y; - float heading = agent->heading; - float vx = agent->vx; - float vy = agent->vy; +static void compute_rewards(Drive *env, int i) { + int agent_idx = env->active_agent_indices[i]; + Agent *agent = &env->agents[agent_idx]; - // Calculate current speed (signed based on direction relative to heading) - float speed_magnitude = sqrtf(vx * vx + vy * vy); - float v_dot_heading = vx * agent->heading_x + vy * agent->heading_y; - float signed_speed = copysignf(speed_magnitude, v_dot_heading); + // NOTE: compute_metrics enforces offroad > collision priority via early returns. + // Red_light and stop_sign can coexist with each other (both flags may be set). + // All penalty terms below are applied independently (no early returns here). - // Update speed with acceleration - signed_speed = signed_speed + acceleration * env->dt; - signed_speed = clipSpeed(signed_speed); - // Compute yaw rate - float beta = tanh(.5 * tanf(steering)); + // Collision reward (GIGAFLOW) + if (agent->metrics_array[COLLISION_IDX] > 0.0f) { + // Velocity-dependent penalty: incentivizes braking before unavoidable collision. + // At max speed (~20 m/s): extra -2.0 on top of base coefficient. + float reward_collision = -(agent->reward_coefs[REWARD_COEF_COLLISION] + 0.1f * agent->sim_speed); - // New heading - float yaw_rate = (signed_speed * cosf(beta) * tanf(steering)) / agent->length; + env->rewards[i] += reward_collision; + env->logs[i].episode_return += reward_collision; + env->logs[i].collision_rate = 1.0f; + } - // New velocity - float new_vx = signed_speed * cosf(heading + beta); - float new_vy = signed_speed * sinf(heading + beta); + // Offroad reward (GIGAFLOW) + if (agent->metrics_array[OFFROAD_IDX] > 0.0f) { + float reward_offroad = -agent->reward_coefs[REWARD_COEF_OFFROAD]; - // Update position - x = x + (new_vx * env->dt); - y = y + (new_vy * env->dt); - heading = heading + yaw_rate * env->dt; + env->rewards[i] += reward_offroad; + env->logs[i].offroad_rate = 1.0f; + env->logs[i].episode_return += reward_offroad; + } - // Apply updates to the agent's state - agent->x = x; - agent->y = y; - agent->heading = heading; - agent->heading_x = cosf(heading); - agent->heading_y = sinf(heading); - agent->vx = new_vx; - agent->vy = new_vy; - } else { - // JERK dynamics model - // Extract action components - float a_long, a_lat; - if (env->action_type == 1) { // continuous - float (*action_array_f)[2] = (float (*)[2])env->actions; + // Red light violation reward (GIGAFLOW) + if (agent->metrics_array[RED_LIGHT_IDX] > 0.0f) { + float reward_red_light = -agent->reward_coefs[REWARD_COEF_TRAFFIC_LIGHT]; - // Asymmetric scaling for longitudinal jerk to match discrete action space - // Discrete: JERK_LONG = [-15, -4, 0, 4] (more braking than acceleration) - float a_long_action = action_array_f[action_idx][0]; // [-1, 1] - if (a_long_action < 0) { - a_long = a_long_action * (-JERK_LONG[0]); // Negative: [-1, 0] → [-15, 0] (braking) - } else { - a_long = a_long_action * JERK_LONG[3]; // Positive: [0, 1] → [0, 4] (acceleration) - } + env->rewards[i] += reward_red_light; + env->logs[i].red_light_violation_rate = 1.0f; + env->logs[i].episode_return += reward_red_light; + } - // Symmetric scaling for lateral jerk - a_lat = action_array_f[action_idx][1] * JERK_LAT[2]; - } else { // discrete - // Interpret action as a single integer: a = long_idx * num_lat + lat_idx - int *action_array = (int *)env->actions; - int num_lat = sizeof(JERK_LAT) / sizeof(JERK_LAT[0]); - int action_val = action_array[action_idx]; - int a_long_idx = action_val / num_lat; - int a_lat_idx = action_val % num_lat; - a_long = JERK_LONG[a_long_idx]; - a_lat = JERK_LAT[a_lat_idx]; + // Goal reward + if (agent->metrics_array[REACHED_GOAL_IDX] > 0.0f) { + float weight = 1.0f; + if (env->simulation_mode == SIMULATION_GIGAFLOW) { + // current_goal_idx is already incremented in compute_metrics when goal is reached. + // == num_target_waypoints means the last waypoint was just reached. + // weight=0 suppresses the final goal reward when agent is already moving (> 3 m/s). + // This prevents rewarding a drive-through of the last waypoint. + if (agent->current_goal_idx == env->num_target_waypoints && agent->sim_speed > env->goal_speed_threshold) + weight = 0.0f; } - // Calculate new acceleration - float a_long_new = agent->a_long + a_long * env->dt; - float a_lat_new = agent->a_lat + a_lat * env->dt; + env->rewards[i] += env->reward_goal * weight; + env->logs[i].episode_return += env->reward_goal * weight; + env->logs[i].num_waypoints_reached += 1; + } - // Make it easy to stop with 0 accel - if (agent->a_long * a_long_new < 0) { - a_long_new = 0.0f; - } else { - a_long_new = clip(a_long_new, -5.0f, 2.5f); - } + // Get lane angle metric: cos(θ_f) where θ_f = heading diff from lane + float cos_theta = agent->metrics_array[LANE_ANGLE_IDX]; + float theta_f = acosf(fminf(fmaxf(cos_theta, -1.0f), 1.0f)); // Get |θ_f| from cos + env->logs[i].lane_heading_aligned_rate += (cos_theta >= LANE_ALIGN_COS_THRESHOLD) ? 1.0f : 0.0f; - if (agent->a_lat * a_lat_new < 0) { - a_lat_new = 0.0f; - } else { - a_lat_new = clip(a_lat_new, -4.0f, 4.0f); - } + // Rl-align (GIGAFLOW): min(cos,0) + vel_align*min(cos*v,0) + 0.0025*(1-|θ|/(π/2)) + float against_lane_penalty = fminf(cos_theta, 0.0f); // negative when >90 degrees off + float vel_aligned_penalty = agent->reward_coefs[REWARD_COEF_VEL_ALIGN] * fminf(cos_theta * agent->sim_speed, 0.0f); + float alignment_bonus = 0.0025f * (1.0f - theta_f / (M_PI / 2.0f)); - // Calculate new velocity - float v_dot_heading = agent->vx * agent->heading_x + agent->vy * agent->heading_y; - float signed_v = copysignf(sqrtf(agent->vx * agent->vx + agent->vy * agent->vy), v_dot_heading); - float v_new = signed_v + 0.5f * (a_long_new + agent->a_long) * env->dt; + float lane_align_reward = agent->reward_coefs[REWARD_COEF_LANE_ALIGN] * env->dt * + (against_lane_penalty + vel_aligned_penalty + alignment_bonus); - // Make it easy to stop with 0 vel - if (signed_v * v_new < 0) { - v_new = 0.0f; - } else { - v_new = clip(v_new, -2.0f, 20.0f); - } + env->rewards[i] += lane_align_reward; + env->logs[i].episode_return += lane_align_reward; - // Calculate new steering angle - float signed_curvature = a_lat_new / fmaxf(v_new * v_new, 1e-5f); - signed_curvature = copysignf(fmaxf(fabsf(signed_curvature), 1e-5f), signed_curvature); - float steering_angle = atanf(signed_curvature * agent->wheelbase); - float delta_steer = clip(steering_angle - agent->steering_angle, -0.6f * env->dt, 0.6f * env->dt); - float new_steering_angle = clip(agent->steering_angle + delta_steer, -0.55f, 0.55f); + // Rl-center (GIGAFLOW): -α * dt * (|x_f - bias| - 0.05 / exp(|x_f - bias| - 0.5)) + float lane_center_distance = agent->metrics_array[LANE_DIST_IDX]; + float adjusted_dist = fabsf(lane_center_distance - agent->reward_coefs[REWARD_COEF_CENTER_BIAS]); + float exp_decay = 0.05f / expf(adjusted_dist - 0.5f); - // Update curvature and accel to account for limited steering - signed_curvature = tanf(new_steering_angle) / agent->wheelbase; - a_lat_new = v_new * v_new * signed_curvature; + float lane_center_reward = + -agent->reward_coefs[REWARD_COEF_LANE_CENTER] * env->dt * ((cos_theta > 0.5f) * adjusted_dist - exp_decay); - // Calculate resulting movement using bicycle dynamics - float d = 0.5f * (v_new + signed_v) * env->dt; - float theta = d * signed_curvature; - float dx_local, dy_local; + env->rewards[i] += lane_center_reward; + env->logs[i].lane_center_rate += fabsf(lane_center_distance) < 0.5f ? 1.0f : 0.0f; + env->logs[i].episode_return += lane_center_reward; - if (fabsf(signed_curvature) < 1e-5f || fabsf(theta) < 1e-5f) { - dx_local = d; - dy_local = 0.0f; - } else { - dx_local = sinf(theta) / signed_curvature; - dy_local = (1.0f - cosf(theta)) / signed_curvature; - } + // Comfort reward (GIGAFLOW) + float comfort_violations = agent->metrics_array[COMFORT_VIOLATION_IDX]; + float comfort_penalty = -agent->reward_coefs[REWARD_COEF_COMFORT] * comfort_violations; - float dx = dx_local * agent->heading_x - dy_local * agent->heading_y; - float dy = dx_local * agent->heading_y + dy_local * agent->heading_x; + env->rewards[i] += comfort_penalty; + env->logs[i].comfort_violation_count += comfort_violations; + env->logs[i].episode_return += comfort_penalty; - // Update everything - agent->x += dx; - agent->y += dy; - agent->jerk_long = (a_long_new - agent->a_long) / env->dt; - agent->jerk_lat = (a_lat_new - agent->a_lat) / env->dt; - agent->a_long = a_long_new; - agent->a_lat = a_lat_new; - agent->heading = normalize_heading(agent->heading + theta); - agent->heading_x = cosf(agent->heading); - agent->heading_y = sinf(agent->heading); - agent->vx = v_new * agent->heading_x; - agent->vy = v_new * agent->heading_y; - agent->steering_angle = new_steering_angle; - } + // Velocity reward (GIGAFLOW) + float velocity_progress = agent->metrics_array[VELOCITY_PROGRESS_IDX]; + float velocity_reward = agent->reward_coefs[REWARD_COEF_VELOCITY] * env->dt * velocity_progress; - return; -} + env->rewards[i] += velocity_reward; + env->logs[i].episode_return += velocity_reward; + env->logs[i].velocity_progress_sum += velocity_progress; -static inline int is_in_track_to_predicts(Drive *env, int agent_idx) { - if (env->tracks_to_predict_indices == NULL || env->num_tracks_to_predict == 0) { - return 0; - } - for (int k = 0; k < env->num_tracks_to_predict; k++) { - if (env->tracks_to_predict_indices[k] == agent_idx) { - return 1; - } + // Timestep reward (GIGAFLOW) + float accel = sqrtf(agent->a_long * agent->a_long + agent->a_lat * agent->a_lat); + // Only penalize when moving (v > 0) or accelerating (a > 0) + if (agent->sim_speed > 0.01f || accel > 0.01f) { + float timestep_penalty = -agent->reward_coefs[REWARD_COEF_TIMESTEP] * env->dt; + + env->rewards[i] += timestep_penalty; + env->logs[i].episode_return += timestep_penalty; } - return 0; -} -void c_get_global_agent_state(Drive *env, float *x_out, float *y_out, float *z_out, float *heading_out, int *id_out, - float *length_out, float *width_out) { - for (int i = 0; i < env->active_agent_count; i++) { - int agent_idx = env->active_agent_indices[i]; - Entity *agent = &env->entities[agent_idx]; + // Reverse reward (GIGAFLOW) + if (agent->sim_speed_signed < -0.01f) { + float reverse_penalty = -agent->reward_coefs[REWARD_COEF_REVERSE] * env->dt; - // For WOSAC, we need the original world coordinates, so we add the world means back - x_out[i] = agent->x + env->world_mean_x; - y_out[i] = agent->y + env->world_mean_y; - z_out[i] = agent->z; - heading_out[i] = agent->heading; - id_out[i] = agent->id; - length_out[i] = agent->length; - width_out[i] = agent->width; + env->rewards[i] += reverse_penalty; + env->logs[i].episode_return += reverse_penalty; } -} -void c_get_global_ground_truth_trajectories(Drive *env, float *x_out, float *y_out, float *z_out, float *heading_out, - int *valid_out, int *id_out, bool *is_vehicle_out, - bool *is_track_to_predict_out, char *scenario_id_out) { - for (int i = 0; i < env->active_agent_count; i++) { - int agent_idx = env->active_agent_indices[i]; - Entity *agent = &env->entities[agent_idx]; - id_out[i] = agent->id; - is_vehicle_out[i] = agent->type == VEHICLE; - is_track_to_predict_out[i] = is_in_track_to_predicts(env, agent_idx); + // Over speed reward (GIGAFLOW++) + float speed_reward = -agent->reward_coefs[REWARD_COEF_OVERSPEED] * agent->metrics_array[SPEED_LIMIT_IDX]; - // The scenario_id is an array of 16 char - memcpy(scenario_id_out + (i * 16), env->scenario_id, 16); + env->rewards[i] += speed_reward; + env->logs[i].avg_speed_per_agent += agent->sim_speed; + env->logs[i].episode_return += speed_reward; - for (int t = env->init_steps; t < agent->array_size; t++) { - int out_idx = i * (agent->array_size - env->init_steps) + (t - env->init_steps); - // Add world means back to get original world coordinates - x_out[out_idx] = agent->traj_x[t] + env->world_mean_x; - y_out[out_idx] = agent->traj_y[t] + env->world_mean_y; - z_out[out_idx] = agent->traj_z[t]; - heading_out[out_idx] = agent->traj_heading[t]; - valid_out[out_idx] = agent->traj_valid[t]; - } - } -} + // ADE reward (CUSTOM) + float current_ade = agent->metrics_array[AVG_DISPLACEMENT_ERROR_IDX]; + if (current_ade > 0.0f && env->reward_ade != 0.0f) { + float ade_reward = env->reward_ade * current_ade; -void c_get_road_edge_counts(Drive *env, int *num_polylines_out, int *total_points_out) { - int count = 0, points = 0; - for (int i = env->num_objects; i < env->num_entities; i++) { - if (env->entities[i].type == ROAD_EDGE) { - count++; - points += env->entities[i].array_size; - } + env->rewards[i] += ade_reward; + env->logs[i].episode_return += ade_reward; } - *num_polylines_out = count; - *total_points_out = points; -} + env->logs[i].avg_displacement_error = current_ade; -void c_get_road_edge_polylines(Drive *env, float *x_out, float *y_out, int *lengths_out, char *scenario_ids_out) { - int poly_idx = 0, pt_idx = 0; - for (int i = env->num_objects; i < env->num_entities; i++) { - Entity *e = &env->entities[i]; - if (e->type == ROAD_EDGE) { - lengths_out[poly_idx] = e->array_size; + if (env->compute_eval_metrics) { + if (agent->at_fault_collision > 0) { + env->logs[i].at_fault_collision_rate = 1.0f; + } - char *scenario_id_ptr = scenario_ids_out + poly_idx * 16; - memcpy(scenario_id_ptr, env->scenario_id, 16); + env->logs[i].wrong_way_distance = agent->wrong_way_distance; + env->logs[i].speed_violation_sum = agent->speed_violation_sum; + env->logs[i].multi_lane_time = agent->multi_lane_time; + float ml_time = env->logs[i].multi_lane_time; + float ml_score = (ml_time <= MULTI_LANE_FULL_SCORE_TIME) ? 1.0f + : (ml_time <= MULTI_LANE_HALF_SCORE_TIME) ? 0.5f + : 0.0f; + env->logs[i].multi_lane_score = ml_score; + agent->metrics_array[MULTI_LANE_TIME_IDX] = ml_time; + agent->metrics_array[MULTI_LANE_SCORE_IDX] = ml_score; + + compute_agent_ttc(env, agent_idx); + if (agent->metrics_array[COLLISION_IDX] > 0.0f) { + agent->cached_ttc.min_ttc = 0.0f; + agent->cached_ttc.other_idx = -1; + agent->cached_ttc.distance_to_collision = 0.0f; + agent->cached_ttc.closing_speed = INFINITY; + } + struct ttc_result ttc_agents = agent->cached_ttc; + float min_vehicle_ttc = ttc_agents.min_ttc; + agent->metrics_array[TTC_IDX] = min_vehicle_ttc; + agent->metrics_array[TTC_TFL_IDX] = DEFAULT_TTC; + agent->ttc_samples++; + if (min_vehicle_ttc < TTC_VIOLATION_THRESHOLD) { + agent->ttc_violations++; + } - for (int j = 0; j < e->array_size; j++) { - x_out[pt_idx] = e->traj_x[j] + env->world_mean_x; - y_out[pt_idx] = e->traj_y[j] + env->world_mean_y; - pt_idx++; - } - poly_idx++; + env->logs[i].ttc_violations = (float)agent->ttc_violations; + env->logs[i].ttc_samples = (float)agent->ttc_samples; + if (agent->ttc_samples > 0) { + env->logs[i].ttc_within_bound_rate = 1.0f - ((float)agent->ttc_violations / (float)agent->ttc_samples); + } else { + env->logs[i].ttc_within_bound_rate = 1.0f; } + + env->logs[i].comfort_score = (env->logs[i].comfort_violation_count > 0) ? 0.0f : 1.0f; + } else { + struct ttc_result default_ttc = default_ttc_result(); + agent->metrics_array[TTC_IDX] = default_ttc.min_ttc; + agent->metrics_array[TTC_TFL_IDX] = default_ttc.min_ttc; + agent->metrics_array[MULTI_LANE_TIME_IDX] = 0.0f; + agent->metrics_array[MULTI_LANE_SCORE_IDX] = 0.0f; + agent->metrics_array[AT_FAULT_COLLISION_IDX] = 0.0f; } } -void compute_observations(Drive *env) { - int ego_dim = (env->dynamics_model == JERK) ? EGO_FEATURES_JERK : EGO_FEATURES_CLASSIC; - int max_obs = ego_dim + PARTNER_FEATURES * (MAX_AGENTS - 1) + ROAD_FEATURES * MAX_ROAD_SEGMENT_OBSERVATIONS; +static void compute_observations(Drive *env) { + int max_obs = compute_observation_size(env); + memset(env->observations, 0, max_obs * env->active_agent_count * sizeof(float)); float (*observations)[max_obs] = (float (*)[max_obs])env->observations; for (int i = 0; i < env->active_agent_count; i++) { float *obs = &observations[i][0]; - Entity *ego_entity = &env->entities[env->active_agent_indices[i]]; - if (ego_entity->type > 3) - break; - - float cos_heading = ego_entity->heading_x; - float sin_heading = ego_entity->heading_y; - float speed_magnitude = sqrtf(ego_entity->vx * ego_entity->vx + ego_entity->vy * ego_entity->vy); - float v_dot_heading = ego_entity->vx * ego_entity->heading_x + ego_entity->vy * ego_entity->heading_y; - float signed_speed = copysignf(speed_magnitude, v_dot_heading); + int obs_idx = 0; + Agent *ego_entity = &env->agents[env->active_agent_indices[i]]; - // Set goal distances - float goal_x = ego_entity->goal_position_x - ego_entity->x; - float goal_y = ego_entity->goal_position_y - ego_entity->y; + // Use cached trig values (updated in move_dynamics/spawn) + float cos_heading = ego_entity->cos_heading; + float sin_heading = ego_entity->sin_heading; - // Rotate to ego vehicle's frame - float rel_goal_x = goal_x * cos_heading + goal_y * sin_heading; - float rel_goal_y = -goal_x * sin_heading + goal_y * cos_heading; - - obs[0] = rel_goal_x * 0.005f; - obs[1] = rel_goal_y * 0.005f; - obs[2] = signed_speed / MAX_SPEED; - obs[3] = ego_entity->width / MAX_VEH_WIDTH; - obs[4] = ego_entity->length / MAX_VEH_LEN; - obs[5] = (ego_entity->collision_state > 0) ? 1.0f : 0.0f; + obs[obs_idx++] = ego_entity->sim_speed_signed / MAX_SPEED; + obs[obs_idx++] = ego_entity->sim_width / MAX_VEH_WIDTH; + obs[obs_idx++] = ego_entity->sim_length / MAX_VEH_LEN; + obs[obs_idx++] = ego_entity->steering_angle / STEERING_VALUES[8]; if (env->dynamics_model == JERK) { - obs[6] = ego_entity->steering_angle / M_PI; - // Asymmetric normalization for a_long to match action space - obs[7] = + obs[obs_idx++] = (ego_entity->a_long < 0) ? ego_entity->a_long / (-JERK_LONG[0]) : ego_entity->a_long / JERK_LONG[3]; - obs[8] = ego_entity->a_lat / JERK_LAT[2]; - obs[9] = (ego_entity->respawn_timestep != -1) ? 1 : 0; - // Add normalized entity type (VEHICLE=1, PEDESTRIAN=2, CYCLIST=3) - obs[10] = ego_entity->type / 3.0f; - } else { - obs[6] = (ego_entity->respawn_timestep != -1) ? 1 : 0; - obs[7] = ego_entity->type / 3.0f; + obs[obs_idx++] = ego_entity->a_lat / JERK_LAT[2]; } - // Relative Pos of other cars - int obs_idx = ego_dim; - int cars_seen = 0; - for (int j = 0; j < MAX_AGENTS; j++) { + float lane_center_dist = ego_entity->metrics_array[LANE_DIST_IDX] / LANE_DISTANCE_NORMALIZATION; + lane_center_dist = fmaxf(-1.0f, fminf(1.0f, lane_center_dist)); + obs[obs_idx++] = lane_center_dist; + obs[obs_idx++] = ego_entity->metrics_array[LANE_ANGLE_IDX]; + + float speed_limit = 20.0f; + int current_lane_index = ego_entity->current_lane_index; + if (current_lane_index != -1 && env->road_elements[current_lane_index].speed_limit > 0) { + speed_limit = env->road_elements[current_lane_index].speed_limit; + } + obs[obs_idx++] = fminf(speed_limit / MAX_SPEED, 1.0f); + + // Encoder -> Conditioning and goal waypoints + if (env->reward_conditioning) { + for (int c = 0; c < NUM_REWARD_COEFS; c++) { + obs[obs_idx++] = normalize_reward_coef(ego_entity->reward_coefs[c], c); + } + } + + // Target observations (static or dynamic) + if (env->target_type == TARGET_STATIC) { + for (int wp = 0; wp < env->num_target_waypoints; wp++) { + if (wp < ego_entity->current_goal_idx) { + // Already reached - zeroed + obs[obs_idx++] = 0.0f; + obs[obs_idx++] = 0.0f; + obs[obs_idx++] = 0.0f; + } else { + float gx = ego_entity->goal_positions_x[wp] - ego_entity->sim_x; + float gy = ego_entity->goal_positions_y[wp] - ego_entity->sim_y; + obs[obs_idx++] = (gx * cos_heading + gy * sin_heading) / MAX_GOAL_POSITION; + obs[obs_idx++] = (-gx * sin_heading + gy * cos_heading) / MAX_GOAL_POSITION; + obs[obs_idx++] = (ego_entity->goal_positions_z[wp] - ego_entity->sim_z) / MAX_POSITION; + } + } + } else if (env->target_type == TARGET_DYNAMIC) { + if (ego_entity->path != NULL && ego_entity->path->num_waypoints > 0) { + for (int wp = 0; wp < env->num_target_waypoints; wp++) { + int wp_index = fmin(ego_entity->closest_path_idx_wp + wp, ego_entity->path->num_waypoints - 1); + if (wp_index < 0) + wp_index = 0; + struct Waypoint *wp = &ego_entity->path->waypoints[wp_index]; + float wp_x = wp->x - ego_entity->sim_x; + float wp_y = wp->y - ego_entity->sim_y; + float wp_z = wp->z - ego_entity->sim_z; + // Use pre-computed trig values from build_path + float wp_cos_h = wp->cos_heading; + float wp_sin_h = wp->sin_heading; + float rel_wp_x = wp_x * cos_heading + wp_y * sin_heading; + float rel_wp_y = -wp_x * sin_heading + wp_y * cos_heading; + float rel_heading_x = wp_cos_h * cos_heading + wp_sin_h * sin_heading; + float rel_heading_y = wp_sin_h * cos_heading - wp_cos_h * sin_heading; + obs[obs_idx++] = rel_wp_x / MAX_POSITION; + obs[obs_idx++] = rel_wp_y / MAX_POSITION; + obs[obs_idx++] = rel_heading_x; + obs[obs_idx++] = rel_heading_y; + obs[obs_idx++] = wp_z / MAX_POSITION; + } + } else { + // No valid path - zero out + obs_idx += DYNAMIC_TARGET_FEATURES * env->num_target_waypoints; + } + } + + // Encode -> Partner vehicle + AgentDistance candidates[env->num_agents]; + int candidate_count = 0; + for (int j = 0; j < env->num_agents; j++) { int index = -1; if (j < env->active_agent_count) { index = env->active_agent_indices[j]; - } else if (j < env->num_actors && env->static_agent_count > 0) { + } else if (j < env->num_agents) { index = env->static_agent_indices[j - env->active_agent_count]; } if (index == -1) continue; - if (env->entities[index].type > 3) - break; + if (env->agents[index].type >= OTHER) + continue; if (index == env->active_agent_indices[i]) continue; // Skip self, but don't increment obs_idx - Entity *other_entity = &env->entities[index]; - if (ego_entity->respawn_timestep != -1) - continue; - if (other_entity->respawn_timestep != -1) - continue; + Agent *other_entity = &env->agents[index]; // Store original relative positions - float dx = other_entity->x - ego_entity->x; - float dy = other_entity->y - ego_entity->y; - float dist = (dx * dx + dy * dy); - if (dist > 2500.0f) + float dx = other_entity->sim_x - ego_entity->sim_x; + float dy = other_entity->sim_y - ego_entity->sim_y; + float dz = other_entity->sim_z - ego_entity->sim_z; + float abs_dz = fabsf(dz); + float dist_sq = dx * dx + dy * dy + dz * dz; + if (dist_sq > AGENT_OBS_MAX_DIST * AGENT_OBS_MAX_DIST || abs_dz > Z_BUFFER) continue; - // Rotate to ego vehicle's frame - float rel_x = dx * cos_heading + dy * sin_heading; - float rel_y = -dx * sin_heading + dy * cos_heading; - // Store observations with correct indexing - obs[obs_idx] = rel_x * 0.02f; - obs[obs_idx + 1] = rel_y * 0.02f; - obs[obs_idx + 2] = other_entity->width / MAX_VEH_WIDTH; - obs[obs_idx + 3] = other_entity->length / MAX_VEH_LEN; - // relative heading - float rel_heading_x = - other_entity->heading_x * ego_entity->heading_x + - other_entity->heading_y * ego_entity->heading_y; // cos(a-b) = cos(a)cos(b) + sin(a)sin(b) - float rel_heading_y = - other_entity->heading_y * ego_entity->heading_x - - other_entity->heading_x * ego_entity->heading_y; // sin(a-b) = sin(a)cos(b) - cos(a)sin(b) - - obs[obs_idx + 4] = rel_heading_x; - obs[obs_idx + 5] = rel_heading_y; - - // relative speed - float other_speed_magnitude = - sqrtf(other_entity->vx * other_entity->vx + other_entity->vy * other_entity->vy); - float other_v_dot_heading = - other_entity->vx * other_entity->heading_x + other_entity->vy * other_entity->heading_y; - float other_signed_speed = copysignf(other_speed_magnitude, other_v_dot_heading); - obs[obs_idx + 6] = other_signed_speed / MAX_SPEED; - cars_seen++; - obs_idx += 7; // Move to next observation slot - } - int remaining_partner_obs = (MAX_AGENTS - 1 - cars_seen) * 7; + // Add to candidate list + candidates[candidate_count].index = index; + candidates[candidate_count].dist_sq = dist_sq; + candidates[candidate_count].dx = dx; + candidates[candidate_count].dy = dy; + candidates[candidate_count].dz = dz; + candidate_count++; + } + int cars_seen = 0; + // Find K closest candidates using partial selection (O(N*K) instead of O(N log N) qsort) + if (candidate_count > 0) { + int num_agents_to_observe = + (candidate_count < env->max_partner_observations) ? candidate_count : env->max_partner_observations; + + // Partial selection sort: find the k-th smallest for each k + for (int k = 0; k < num_agents_to_observe; k++) { + // Find minimum in remaining elements [k, candidate_count) + int min_idx = k; + for (int j = k + 1; j < candidate_count; j++) { + if (candidates[j].dist_sq < candidates[min_idx].dist_sq) { + min_idx = j; + } + } + // Swap to position k + if (min_idx != k) { + AgentDistance tmp = candidates[k]; + candidates[k] = candidates[min_idx]; + candidates[min_idx] = tmp; + } + } + + for (int k = 0; k < num_agents_to_observe; k++) { + // Get the data for the k-th closest agent + int index = candidates[k].index; + float dx = candidates[k].dx; + float dy = candidates[k].dy; + float dz = candidates[k].dz; + Agent *other_entity = &env->agents[index]; + // Rotate to ego vehicle's frame + float rel_x = dx * cos_heading + dy * sin_heading; + float rel_y = -dx * sin_heading + dy * cos_heading; + // Store observations with correct indexing + obs[obs_idx++] = rel_x / MAX_POSITION; + obs[obs_idx++] = rel_y / MAX_POSITION; + obs[obs_idx++] = other_entity->sim_width / MAX_VEH_WIDTH; + obs[obs_idx++] = other_entity->sim_length / MAX_VEH_LEN; + // relative heading + // Use cached trig values from other agent + float other_cos = other_entity->cos_heading; + float other_sin = other_entity->sin_heading; + float rel_heading_x = + other_cos * cos_heading + other_sin * sin_heading; // cos(a-b) = cos(a)cos(b) + sin(a)sin(b) + float rel_heading_y = + other_sin * cos_heading - other_cos * sin_heading; // sin(a-b) = sin(a)cos(b) - cos(a)sin(b) + + obs[obs_idx++] = rel_heading_x; + obs[obs_idx++] = rel_heading_y; + obs[obs_idx++] = other_entity->sim_speed / MAX_SPEED; + obs[obs_idx++] = dz / MAX_POSITION; + cars_seen++; + } + } + int remaining_partner_obs = (env->max_partner_observations - cars_seen) * PARTNER_FEATURES; memset(&obs[obs_idx], 0, remaining_partner_obs * sizeof(float)); obs_idx += remaining_partner_obs; - // map observations - GridMapEntity entity_list[MAX_ENTITIES_PER_CELL * 25]; - int grid_idx = getGridIndex(env, ego_entity->x, ego_entity->y); - int list_size = get_neighbor_cache_entities(env, grid_idx, entity_list, MAX_ROAD_SEGMENT_OBSERVATIONS); + // Encode -> Road segments (lanes and boundaries) + int grid_idx = get_grid_index(env, ego_entity->sim_x, ego_entity->sim_y); + int list_size; + const GridMapEntity *entity_list = get_neighbor_cache_entities(env, grid_idx, &list_size); + + int lane_added = 0; + int boundary_added = 0; + int lane_obs_idx = obs_idx; + int boundary_obs_idx = lane_obs_idx + env->max_lane_segment_observations * ROAD_FEATURES; for (int k = 0; k < list_size; k++) { + if (lane_added >= env->max_lane_segment_observations && + boundary_added >= env->max_boundary_segment_observations) { + break; + } + int entity_type = entity_list[k].entity_type; int entity_idx = entity_list[k].entity_idx; int geometry_idx = entity_list[k].geometry_idx; + // Only process road elements in observations + if (entity_type != ENTITY_TYPE_ROAD_ELEMENT) + continue; + // Validate entity_idx before accessing - if (entity_idx < 0 || entity_idx >= env->num_entities) { - printf("ERROR: Invalid entity_idx %d (max: %d)\n", entity_idx, env->num_entities - 1); + if (entity_idx < 0 || entity_idx >= env->num_road_elements) { + printf("ERROR: Invalid road element idx %d (max: %d)\n", entity_idx, env->num_road_elements - 1); continue; } - Entity *entity = &env->entities[entity_idx]; + RoadMapElement *element = &env->road_elements[entity_idx]; + int is_lane = is_road_lane(element->type); + int is_edge = is_road_edge(element->type); + if (!is_lane && !is_edge) { + continue; + } // Validate geometry_idx before accessing - if (geometry_idx < 0 || geometry_idx >= entity->array_size) { - printf("ERROR: Invalid geometry_idx %d for entity %d (max: %d)\n", geometry_idx, entity_idx, - entity->array_size - 1); + if (geometry_idx < 0 || geometry_idx >= element->segment_length - 1) { + printf("ERROR: Invalid geometry_idx %d for road element %d (max: %d)\n", geometry_idx, entity_idx, + element->segment_length - 2); continue; } - float start_x = entity->traj_x[geometry_idx]; - float start_y = entity->traj_y[geometry_idx]; - float end_x = entity->traj_x[geometry_idx + 1]; - float end_y = entity->traj_y[geometry_idx + 1]; + float start_x = element->x[geometry_idx]; + float start_y = element->y[geometry_idx]; + float start_z = element->z[geometry_idx]; + float end_x = element->x[geometry_idx + 1]; + float end_y = element->y[geometry_idx + 1]; + float end_z = element->z[geometry_idx + 1]; float mid_x = (start_x + end_x) / 2.0f; float mid_y = (start_y + end_y) / 2.0f; - float rel_x = mid_x - ego_entity->x; - float rel_y = mid_y - ego_entity->y; + float mid_z = (start_z + end_z) / 2.0f; + float rel_x = mid_x - ego_entity->sim_x; + float rel_y = mid_y - ego_entity->sim_y; + float rel_z = mid_z - ego_entity->sim_z; float x_obs = rel_x * cos_heading + rel_y * sin_heading; float y_obs = -rel_x * sin_heading + rel_y * cos_heading; - float length = relative_distance_2d(mid_x, mid_y, end_x, end_y); - float width = 0.1; - // Calculate angle from ego to midpoint (vector from ego to midpoint) - float dx = end_x - mid_x; - float dy = end_y - mid_y; - float dx_norm = dx; - float dy_norm = dy; - float hypot = sqrtf(dx * dx + dy * dy); - if (hypot > 0) { - dx_norm /= hypot; - dy_norm /= hypot; - } - // Compute sin and cos of relative angle directly without atan2f - float cos_angle = dx_norm * cos_heading + dy_norm * sin_heading; - float sin_angle = -dx_norm * sin_heading + dy_norm * cos_heading; - obs[obs_idx] = x_obs * 0.02f; - obs[obs_idx + 1] = y_obs * 0.02f; - obs[obs_idx + 2] = length / MAX_ROAD_SEGMENT_LENGTH; - obs[obs_idx + 3] = width / MAX_ROAD_SCALE; - obs[obs_idx + 4] = cos_angle; - obs[obs_idx + 5] = sin_angle; - obs[obs_idx + 6] = entity->type - 4.0f; - obs_idx += 7; - } - int remaining_obs = (MAX_ROAD_SEGMENT_OBSERVATIONS - list_size) * 7; - // Set the entire block to 0 at once - memset(&obs[obs_idx], 0, remaining_obs * sizeof(float)); - } -} - -void sample_new_goal(Drive *env, int agent_idx) { - // Samples a new goal position based on the existing road lane points - Entity *agent = &env->entities[agent_idx]; - float best_x = agent->x; - float best_y = agent->y; - float best_distance_error = 1e30f; - - // Sample points from all road lanes - for (int i = env->num_objects; i < env->num_entities; i++) { - if (env->entities[i].type != ROAD_LANE) - continue; - Entity *lane = &env->entities[i]; - - // Check every point in the lane - for (int j = 0; j < lane->array_size; j++) { - float point_x = lane->traj_x[j]; - float point_y = lane->traj_y[j]; - - // Calculate vector from agent to point - float to_point_x = point_x - agent->x; - float to_point_y = point_y - agent->y; - - // Check if point is ahead of agent - float dot = to_point_x * agent->heading_x + to_point_y * agent->heading_y; - if (dot <= 0.0f) + // Filter by asymmetric vision rectangle + if (x_obs < -ROAD_OBS_BEHIND_DIST || x_obs > ROAD_OBS_FRONT_DIST) + continue; + if (fabsf(y_obs) > ROAD_OBS_SIDE_DIST) + continue; + if (fabsf(rel_z) > Z_BUFFER) continue; - // Calculate distance to point - float distance = sqrtf(to_point_x * to_point_x + to_point_y * to_point_y); - - // Find point closest to target distance - float distance_error = fabsf(distance - env->goal_target_distance); - if (distance_error < best_distance_error) { - best_distance_error = distance_error; - best_x = point_x; - best_y = point_y; - } - } - } - - // If no valid goal found, use another agent's initial goal - if (best_distance_error >= 1e30f && env->active_agent_count > 1) { - int other_idx = env->active_agent_indices[(agent_idx + 1) % env->active_agent_count]; - best_x = env->entities[other_idx].init_goal_x; - best_y = env->entities[other_idx].init_goal_y; - } - - agent->goal_position_x = best_x; - agent->goal_position_y = best_y; - agent->goals_sampled_this_episode += 1; -} - -void c_reset(Drive *env) { - env->timestep = env->init_steps; - set_start_position(env); - for (int x = 0; x < env->active_agent_count; x++) { - env->logs[x] = (Log){0}; - int agent_idx = env->active_agent_indices[x]; - env->entities[agent_idx].respawn_timestep = -1; - env->entities[agent_idx].respawn_count = 0; - env->entities[agent_idx].collided_before_goal = 0; - env->entities[agent_idx].goals_reached_this_episode = 0.0f; - // Initialize to 1 because there is one goal in the data file - env->entities[agent_idx].goals_sampled_this_episode = 1.0f; - env->entities[agent_idx].current_goal_reached = 0; - env->entities[agent_idx].metrics_array[COLLISION_IDX] = 0.0f; - env->entities[agent_idx].metrics_array[OFFROAD_IDX] = 0.0f; - env->entities[agent_idx].metrics_array[REACHED_GOAL_IDX] = 0.0f; - env->entities[agent_idx].metrics_array[LANE_ALIGNED_IDX] = 0.0f; - env->entities[agent_idx].stopped = 0; - env->entities[agent_idx].removed = 0; - - if (env->goal_behavior == GOAL_GENERATE_NEW) { - env->entities[agent_idx].goal_position_x = env->entities[agent_idx].init_goal_x; - env->entities[agent_idx].goal_position_y = env->entities[agent_idx].init_goal_y; - } - - compute_agent_metrics(env, agent_idx); - } - compute_observations(env); -} - -void respawn_agent(Drive *env, int agent_idx) { - env->entities[agent_idx].x = env->entities[agent_idx].traj_x[0]; - env->entities[agent_idx].y = env->entities[agent_idx].traj_y[0]; - env->entities[agent_idx].heading = env->entities[agent_idx].traj_heading[0]; - env->entities[agent_idx].heading_x = cosf(env->entities[agent_idx].heading); - env->entities[agent_idx].heading_y = sinf(env->entities[agent_idx].heading); - env->entities[agent_idx].vx = env->entities[agent_idx].traj_vx[0]; - env->entities[agent_idx].vy = env->entities[agent_idx].traj_vy[0]; - env->entities[agent_idx].metrics_array[COLLISION_IDX] = 0.0f; - env->entities[agent_idx].metrics_array[OFFROAD_IDX] = 0.0f; - env->entities[agent_idx].metrics_array[REACHED_GOAL_IDX] = 0.0f; - env->entities[agent_idx].metrics_array[LANE_ALIGNED_IDX] = 0.0f; - - env->entities[agent_idx].respawn_timestep = env->timestep; - env->entities[agent_idx].collided_before_goal = 0; - env->entities[agent_idx].stopped = 0; - env->entities[agent_idx].removed = 0; - env->entities[agent_idx].a_long = 0.0f; - env->entities[agent_idx].a_lat = 0.0f; - env->entities[agent_idx].jerk_long = 0.0f; - env->entities[agent_idx].jerk_lat = 0.0f; - env->entities[agent_idx].steering_angle = 0.0f; -} - -void c_step(Drive *env) { - memset(env->rewards, 0, env->active_agent_count * sizeof(float)); - memset(env->terminals, 0, env->active_agent_count * sizeof(unsigned char)); - memset(env->truncations, 0, env->active_agent_count * sizeof(unsigned char)); - env->timestep++; - - // Move static experts - for (int i = 0; i < env->expert_static_agent_count; i++) { - int expert_idx = env->expert_static_agent_indices[i]; - if (env->entities[expert_idx].x == INVALID_POSITION) - continue; - move_expert(env, env->actions, expert_idx); - } - // Process actions for all active agents - for (int i = 0; i < env->active_agent_count; i++) { - env->logs[i].score = 0.0f; - env->logs[i].episode_length += 1; - int agent_idx = env->active_agent_indices[i]; - env->entities[agent_idx].collision_state = 0; - float prev_vx = env->entities[agent_idx].vx; - float prev_vy = env->entities[agent_idx].vy; - - move_dynamics(env, i, agent_idx); - - // Tiny jerk penalty for smoothness - if (env->dynamics_model == CLASSIC) { - float delta_vx = env->entities[agent_idx].vx - prev_vx; - float delta_vy = env->entities[agent_idx].vy - prev_vy; - float jerk_penalty = -0.0002f * sqrtf(delta_vx * delta_vx + delta_vy * delta_vy) / env->dt; - env->rewards[i] += jerk_penalty; - env->logs[i].episode_return += jerk_penalty; - } - } - - // Compute rewards - for (int i = 0; i < env->active_agent_count; i++) { - int agent_idx = env->active_agent_indices[i]; - env->entities[agent_idx].collision_state = 0; - - compute_agent_metrics(env, agent_idx); - int collision_state = env->entities[agent_idx].collision_state; - - if (collision_state > 0) { - if (collision_state == VEHICLE_COLLISION) { - env->rewards[i] += env->reward_vehicle_collision; - env->logs[i].episode_return += env->reward_vehicle_collision; - env->logs[i].collision_rate = 1.0f; - env->logs[i].collisions_per_agent += 1.0f; - } else if (collision_state == OFFROAD) { - env->rewards[i] += env->reward_offroad_collision; - env->logs[i].episode_return += env->reward_offroad_collision; - env->logs[i].offroad_rate = 1.0f; - env->logs[i].offroad_per_agent += 1.0f; - } - - env->entities[agent_idx].collided_before_goal = 1; - } - - float distance_to_goal = - relative_distance_2d(env->entities[agent_idx].x, env->entities[agent_idx].y, - env->entities[agent_idx].goal_position_x, env->entities[agent_idx].goal_position_y); - - float current_speed = sqrtf(env->entities[agent_idx].vx * env->entities[agent_idx].vx + - env->entities[agent_idx].vy * env->entities[agent_idx].vy); - - // Reward agent if it is within X meters of goal and speed is below threshold - bool within_distance = distance_to_goal < env->goal_radius; - bool within_speed = current_speed <= env->goal_speed; - - if (within_distance && within_speed && !env->entities[agent_idx].current_goal_reached) { - if (env->goal_behavior == GOAL_RESPAWN && env->entities[agent_idx].respawn_timestep != -1) { - env->rewards[i] += env->reward_goal_post_respawn; - env->logs[i].episode_return += env->reward_goal_post_respawn; - env->entities[agent_idx].current_goal_reached = 1; - } else if (env->goal_behavior == GOAL_GENERATE_NEW && (!env->entities[agent_idx].current_goal_reached)) { - env->rewards[i] += env->reward_goal; - env->logs[i].episode_return += env->reward_goal; - sample_new_goal(env, agent_idx); - env->entities[agent_idx].current_goal_reached = 0; - env->entities[agent_idx].goals_reached_this_episode += 1.0f; - } else { // Zero out the velocity so that the agent stops at the goal - env->rewards[i] = env->reward_goal; - env->logs[i].episode_return = env->reward_goal; - env->entities[agent_idx].stopped = 1; - env->entities[agent_idx].vx = env->entities[agent_idx].vy = 0.0f; - env->entities[agent_idx].goals_reached_this_episode += 1.0f; - } - env->entities[agent_idx].metrics_array[REACHED_GOAL_IDX] = 1.0f; - env->logs[i].speed_at_goal = current_speed; - } - - int lane_aligned = env->entities[agent_idx].metrics_array[LANE_ALIGNED_IDX]; - env->logs[i].lane_alignment_rate = lane_aligned; - } - - if (env->goal_behavior == GOAL_RESPAWN) { - for (int i = 0; i < env->active_agent_count; i++) { - int agent_idx = env->active_agent_indices[i]; - int reached_goal = env->entities[agent_idx].metrics_array[REACHED_GOAL_IDX]; - if (reached_goal) { - env->terminals[i] = 1; - respawn_agent(env, agent_idx); - env->entities[agent_idx].respawn_count++; - } - } - } else if (env->goal_behavior == GOAL_STOP) { - for (int i = 0; i < env->active_agent_count; i++) { - int agent_idx = env->active_agent_indices[i]; - int reached_goal = env->entities[agent_idx].metrics_array[REACHED_GOAL_IDX]; - if (reached_goal) { - env->entities[agent_idx].stopped = 1; - env->entities[agent_idx].vx = env->entities[agent_idx].vy = 0.0f; - } - } - } - - // Episode boundary after this step: treat time-limit and early-termination as truncation. - // `timestep` is incremented at step start, so truncate when `(timestep + 1) >= episode_length`. - int originals_remaining = 0; - for (int i = 0; i < env->active_agent_count; i++) { - int agent_idx = env->active_agent_indices[i]; - if (env->entities[agent_idx].respawn_count == 0) { - originals_remaining = 1; - break; - } - } - int reached_time_limit = (env->timestep + 1) >= env->episode_length; - int reached_early_termination = (!originals_remaining && env->termination_mode == 1); - if (reached_time_limit || reached_early_termination) { - for (int i = 0; i < env->active_agent_count; i++) { - env->truncations[i] = 1; - } - add_log(env); - c_reset(env); - return; - } - - compute_observations(env); -} + // Compute segment direction and length (single sqrt instead of two) + float dx = end_x - mid_x; + float dy = end_y - mid_y; + float length = sqrtf(dx * dx + dy * dy); + float dx_norm = (length > 0) ? dx / length : dx; + float dy_norm = (length > 0) ? dy / length : dy; + float cos_angle = dx_norm * cos_heading + dy_norm * sin_heading; + float sin_angle = -dx_norm * sin_heading + dy_norm * cos_heading; -typedef struct Client Client; + int write_idx = 0; + if (is_lane) { + if (lane_added >= env->max_lane_segment_observations) { + continue; + } + write_idx = lane_obs_idx + lane_added * ROAD_FEATURES; + lane_added++; + } else { + if (boundary_added >= env->max_boundary_segment_observations) { + continue; + } + write_idx = boundary_obs_idx + boundary_added * ROAD_FEATURES; + boundary_added++; + } -struct Client { - float width; - float height; - Texture2D puffers; - Vector3 camera_target; - float camera_zoom; - Camera3D camera; - Model cars[6]; - Model cyclist; - Model pedestrian; - ModelAnimation *cycle_anim; - int car_assignments[MAX_AGENTS]; - Vector3 default_camera_position; - Vector3 default_camera_target; - int recorder_pipefd[2]; - pid_t recorder_pid; - pid_t xvfb_pid; - int xvfb_display_num; -}; + obs[write_idx] = x_obs / MAX_POSITION; + obs[write_idx + 1] = y_obs / MAX_POSITION; + obs[write_idx + 2] = length / MAX_ROAD_SEGMENT_LENGTH; + obs[write_idx + 3] = 0.1f / MAX_ROAD_SCALE; + obs[write_idx + 4] = cos_angle; + obs[write_idx + 5] = sin_angle; + obs[write_idx + 6] = rel_z / MAX_POSITION; + } + int remaining_lane_obs = (env->max_lane_segment_observations - lane_added) * ROAD_FEATURES; + memset(&obs[lane_obs_idx + lane_added * ROAD_FEATURES], 0, remaining_lane_obs * sizeof(float)); + int remaining_boundary_obs = (env->max_boundary_segment_observations - boundary_added) * ROAD_FEATURES; + memset(&obs[boundary_obs_idx + boundary_added * ROAD_FEATURES], 0, remaining_boundary_obs * sizeof(float)); + obs_idx = boundary_obs_idx + env->max_boundary_segment_observations * ROAD_FEATURES; + + // Traffic light observations + typedef struct { + int idx; + float dist_sq; + } TrafficLightDist; + TrafficLightDist traffic_lights[env->num_traffic_elements > 0 ? env->num_traffic_elements : 1]; + int num_visible_lights = 0; + + // Collect traffic lights within range + for (int j = 0; j < env->num_traffic_elements; j++) { + TrafficControlElement *traffic = &env->traffic_elements[j]; + if (traffic->type != TRAFFIC_LIGHT) + continue; // Only traffic lights + + float mid_x = (traffic->stop_line[0] + traffic->stop_line[3]) * 0.5f; + float mid_y = (traffic->stop_line[1] + traffic->stop_line[4]) * 0.5f; + float mid_z = (traffic->stop_line[2] + traffic->stop_line[5]) * 0.5f; + float dx = mid_x - ego_entity->sim_x; + float dy = mid_y - ego_entity->sim_y; + float dz = mid_z - ego_entity->sim_z; + float abs_dz = fabsf(dz); + float dist_sq = dx * dx + dy * dy + dz * dz; + + if (dist_sq > MAX_TRAFFIC_LIGHT_DISTANCE * MAX_TRAFFIC_LIGHT_DISTANCE || abs_dz > Z_BUFFER) + continue; -Client *make_client(Drive *env) { - - Client *client = (Client *)calloc(1, sizeof(Client)); - - if (env->render_mode == RENDER_HEADLESS && getenv("DISPLAY") == NULL) { - - // Kill any existing Xvfb first - system("pkill -9 Xvfb"); - usleep(200000); - unlink("/tmp/.X99-lock"); - unlink("/tmp/.X11-unix/X99"); - - // Hardcode to single display because we only run this in one process at once - client->xvfb_display_num = 99; - - // Clean up stale lock if process is dead - FILE *f = fopen("/tmp/.X99-lock", "r"); - if (f) { - pid_t pid = -1; - fscanf(f, "%d", &pid); - fclose(f); - if (pid > 0 && kill(pid, 0) != 0) - unlink("/tmp/.X99-lock"); - } - - client->xvfb_pid = fork(); - if (client->xvfb_pid == 0) { - close(STDOUT_FILENO); - close(STDERR_FILENO); - execlp("Xvfb", "Xvfb", ":99", "-screen", "0", "1280x720x24", "+extension", "GLX", "-ac", "-noreset", NULL); - _exit(1); - } - - setenv("DISPLAY", ":99", 1); - // Xvfb starts asynchronously after fork(), so we poll until it creates its - // lock file (max 2s) then wait an extra 200ms for GLX to finish initializing. - // Without this, raylib's InitWindow() would try to connect before Xvfb is ready. - for (int i = 0; i < 20 && access("/tmp/.X99-lock", F_OK) != 0; i++) - usleep(100000); - usleep(200000); - } - - if (env->render_mode == RENDER_WINDOW) { - client->width = 1280; - client->height = 704; - SetConfigFlags(FLAG_MSAA_4X_HINT); - SetTargetFPS(30); - - // Set up camera for interactive window - Vector3 target_pos = {0, 0, 1}; // Y is up, Z is depth + traffic_lights[num_visible_lights].idx = j; + traffic_lights[num_visible_lights].dist_sq = dist_sq; + num_visible_lights++; + } - client->default_camera_position = (Vector3){ - 0, // Same X as target - 120.0f, // 20 units above target - 175.0f // 20 units behind target - }; - client->default_camera_target = target_pos; - client->camera.position = client->default_camera_position; - client->camera.target = client->default_camera_target; - client->camera.up = (Vector3){0.0f, -1.0f, 0.0f}; // Y is up - client->camera.fovy = 45.0f; - client->camera.projection = CAMERA_PERSPECTIVE; + // Partial selection sort: find K closest (O(N*K) instead of O(N²) bubble sort) + int num_lights_to_observe = (num_visible_lights < env->max_traffic_light_observations) + ? num_visible_lights + : env->max_traffic_light_observations; + for (int k = 0; k < num_lights_to_observe; k++) { + int min_idx = k; + for (int j = k + 1; j < num_visible_lights; j++) { + if (traffic_lights[j].dist_sq < traffic_lights[min_idx].dist_sq) { + min_idx = j; + } + } + if (min_idx != k) { + TrafficLightDist temp = traffic_lights[k]; + traffic_lights[k] = traffic_lights[min_idx]; + traffic_lights[min_idx] = temp; + } + } - } else { // Headless rendering - SetConfigFlags(FLAG_WINDOW_HIDDEN); - SetTargetFPS(6000); + // Add observations for closest traffic lights + int lights_added = 0; + for (int j = 0; j < num_lights_to_observe && lights_added < env->max_traffic_light_observations; j++) { + TrafficControlElement *traffic = &env->traffic_elements[traffic_lights[j].idx]; + + // Stop line endpoint 1 + float dx1 = traffic->stop_line[0] - ego_entity->sim_x; + float dy1 = traffic->stop_line[1] - ego_entity->sim_y; + float rel_x1 = dx1 * cos_heading + dy1 * sin_heading; + float rel_y1 = -dx1 * sin_heading + dy1 * cos_heading; + + // Stop line endpoint 2 + float dx2 = traffic->stop_line[3] - ego_entity->sim_x; + float dy2 = traffic->stop_line[4] - ego_entity->sim_y; + float rel_x2 = dx2 * cos_heading + dy2 * sin_heading; + float rel_y2 = -dx2 * sin_heading + dy2 * cos_heading; + float rel_z = ((traffic->stop_line[2] + traffic->stop_line[5]) * 0.5f - ego_entity->sim_z) / MAX_POSITION; + + // Get current state with bounds check + int t = env->timestep; + int state = TL_STATE_DISABLED; + if (t >= 0 && t < traffic->state_length) { + state = traffic->states[t]; + } - float map_width = env->grid_map->bottom_right_x - env->grid_map->top_left_x; - float map_height = env->grid_map->top_left_y - env->grid_map->bottom_right_y; - float scale = 6.0f; // Controls the resolution of the output video - int img_width = (int)roundf(map_width * scale / 2.0f) * 2; - int img_height = (int)roundf(map_height * scale / 2.0f) * 2; + obs[obs_idx++] = rel_x1 / MAX_POSITION; + obs[obs_idx++] = rel_y1 / MAX_POSITION; + obs[obs_idx++] = rel_x2 / MAX_POSITION; + obs[obs_idx++] = rel_y2 / MAX_POSITION; + obs[obs_idx++] = rel_z; + obs[obs_idx++] = normalize_traffic_light_state(state); + lights_added++; + } - client->width = img_width; - client->height = img_height; + // Zero out remaining traffic light slots + int remaining_traffic_obs = (env->max_traffic_light_observations - lights_added) * TRAFFIC_LIGHT_FEATURES; + memset(&obs[obs_idx], 0, remaining_traffic_obs * sizeof(float)); } +} - SetTraceLogLevel(LOG_WARNING); // Only show warnings and errors - InitWindow(client->width, client->height, "PufferDrive"); +static void move_dynamics(Drive *env, int action_idx, int agent_idx) { + Agent *agent = &env->agents[agent_idx]; - // Load assets - client->cars[0] = LoadModel("resources/drive/RedCar.glb"); - client->cars[1] = LoadModel("resources/drive/WhiteCar.glb"); - client->cars[2] = LoadModel("resources/drive/BlueCar.glb"); - client->cars[3] = LoadModel("resources/drive/YellowCar.glb"); - client->cars[4] = LoadModel("resources/drive/GreenCar.glb"); - client->cars[5] = LoadModel("resources/drive/GreyCar.glb"); - client->cyclist = LoadModel("resources/drive/cyclist.glb"); - client->pedestrian = LoadModel("resources/drive/pedestrian.glb"); - int animCountCyc = 0; - client->cycle_anim = LoadModelAnimations("resources/drive/cyclist.glb", &animCountCyc); - for (int i = 0; i < MAX_AGENTS; i++) { - client->car_assignments[i] = (rand() % 4) + 1; + // If agent is removed, set position to invalid and return + if (agent->removed) { + invalidate_agent(agent); + return; } - // Set up ffmpeg process for recording - if (env->render_mode == RENDER_HEADLESS) { - if (pipe(client->recorder_pipefd) == -1) { - fprintf(stderr, "Failed to create pipe\n"); - free(client); - return NULL; - } - - char size_str[64]; - snprintf(size_str, sizeof(size_str), "%dx%d", (int)client->width, (int)client->height); - - char filename[256]; - snprintf(filename, sizeof(filename), "%s.mp4", env->scenario_id); - - client->recorder_pid = fork(); - if (client->recorder_pid == -1) { - fprintf(stderr, "Failed to fork\n"); - free(client); - return NULL; - } - - if (client->recorder_pid == 0) { // Child process - close(client->recorder_pipefd[1]); - dup2(client->recorder_pipefd[0], STDIN_FILENO); - close(client->recorder_pipefd[0]); - for (int fd = 3; fd < 256; fd++) - close(fd); - execlp("ffmpeg", "ffmpeg", "-y", "-f", "rawvideo", "-pix_fmt", "rgba", "-s", size_str, "-r", "30", "-i", - "-", "-c:v", "libx264", "-pix_fmt", "yuv420p", "-preset", "ultrafast", "-crf", "23", "-loglevel", - "error", filename, NULL); - fprintf(stderr, "execlp ffmpeg failed\n"); - _exit(1); - } - close(client->recorder_pipefd[0]); - } - - return client; -} - -// Camera control functions -void handle_camera_controls(Client *client) { - static Vector2 prev_mouse_pos = {0}; - static bool is_dragging = false; - float camera_move_speed = 0.5f; - - // Handle mouse drag for camera movement - if (IsMouseButtonPressed(MOUSE_BUTTON_LEFT)) { - prev_mouse_pos = GetMousePosition(); - is_dragging = true; - } - - if (IsMouseButtonReleased(MOUSE_BUTTON_LEFT)) { - is_dragging = false; - } - - if (is_dragging) { - Vector2 current_mouse_pos = GetMousePosition(); - Vector2 delta = {(current_mouse_pos.x - prev_mouse_pos.x) * camera_move_speed, - -(current_mouse_pos.y - prev_mouse_pos.y) * camera_move_speed}; - - // Update camera position (only X and Y) - client->camera.position.x += delta.x; - client->camera.position.y += delta.y; - - // Update camera target (only X and Y) - client->camera.target.x += delta.x; - client->camera.target.y += delta.y; - - prev_mouse_pos = current_mouse_pos; - } + // If agent is stopped, zero out velocities and accelerations + if (agent->stopped) { + agent->sim_vx = 0.0f; + agent->sim_vy = 0.0f; + agent->yaw_rate = 0.0f; + agent->sim_speed = 0.0f; + agent->sim_speed_signed = 0.0f; + agent->a_long = 0.0f; + agent->a_lat = 0.0f; + agent->jerk_long = 0.0f; + agent->jerk_lat = 0.0f; + agent->steering_angle = 0.0f; + return; + } - // Handle mouse wheel for zoom - float wheel = GetMouseWheelMove(); - if (wheel != 0) { - float zoom_factor = 1.0f - (wheel * 0.1f); - // Calculate the current direction vector from target to position - Vector3 direction = {client->camera.position.x - client->camera.target.x, - client->camera.position.y - client->camera.target.y, - client->camera.position.z - client->camera.target.z}; + if (env->dynamics_model == CLASSIC) { + // Classic dynamics model + float acceleration = 0.0f; + float steering = 0.0f; - // Scale the direction vector by the zoom factor - direction.x *= zoom_factor; - direction.y *= zoom_factor; - direction.z *= zoom_factor; - - // Update the camera position based on the scaled direction - client->camera.position.x = client->camera.target.x + direction.x; - client->camera.position.y = client->camera.target.y + direction.y; - client->camera.position.z = client->camera.target.z + direction.z; - } -} + if (env->action_type == 0) { // discrete + // Interpret action as a single integer: a = accel_idx * num_steer + steer_idx + int *action_array = (int *)env->actions; + int num_steer = sizeof(STEERING_VALUES) / sizeof(STEERING_VALUES[0]); + int action_val = action_array[action_idx]; + int acceleration_index = action_val / num_steer; + int steering_index = action_val % num_steer; + acceleration = ACCELERATION_VALUES[acceleration_index]; + steering = STEERING_VALUES[steering_index]; + } else if (env->action_type == 1) { // continuous + float (*action_array_f)[2] = (float (*)[2])env->actions; + acceleration = action_array_f[action_idx][0]; + steering = action_array_f[action_idx][1]; -void draw_agent_obs(Drive *env, int agent_index, int mode, int obs_only, int lasers) { - // Diamond dimensions - float diamond_height = 3.0f; // Total height of diamond - float diamond_width = 1.5f; // Width of diamond - float diamond_z = 8.0f; // Base Z position + acceleration *= ACCELERATION_VALUES[6]; + steering *= STEERING_VALUES[8]; + } - // Define diamond points - Vector3 top_point = (Vector3){0.0f, 0.0f, diamond_z + diamond_height / 2}; // Top point - Vector3 bottom_point = (Vector3){0.0f, 0.0f, diamond_z - diamond_height / 2}; // Bottom point - Vector3 front_point = (Vector3){0.0f, diamond_width / 2, diamond_z}; // Front point - Vector3 back_point = (Vector3){0.0f, -diamond_width / 2, diamond_z}; // Back point - Vector3 left_point = (Vector3){-diamond_width / 2, 0.0f, diamond_z}; // Left point - Vector3 right_point = (Vector3){diamond_width / 2, 0.0f, diamond_z}; // Right point + // Limit the steering rate similar to the jerk model + float delta_steer = clip(steering - agent->steering_angle, -0.6f * env->dt, 0.6f * env->dt); + steering = clip(agent->steering_angle + delta_steer, -0.667f, 0.667f); + agent->steering_angle = steering; - // Draw the diamond faces - // Top pyramid - if (mode == 0) { - DrawTriangle3D(top_point, front_point, right_point, PUFF_CYAN); // Front-right face - DrawTriangle3D(top_point, right_point, back_point, PUFF_CYAN); // Back-right face - DrawTriangle3D(top_point, back_point, left_point, PUFF_CYAN); // Back-left face - DrawTriangle3D(top_point, left_point, front_point, PUFF_CYAN); // Front-left face + // Current state + float x = agent->sim_x; + float y = agent->sim_y; + float heading = agent->sim_heading; + float speed = agent->sim_speed_signed; - // Bottom pyramid - DrawTriangle3D(bottom_point, right_point, front_point, PUFF_CYAN); // Front-right face - DrawTriangle3D(bottom_point, back_point, right_point, PUFF_CYAN); // Back-right face - DrawTriangle3D(bottom_point, left_point, back_point, PUFF_CYAN); // Back-left face - DrawTriangle3D(bottom_point, front_point, left_point, PUFF_CYAN); // Front-left face - } - if (!IsKeyDown(KEY_LEFT_CONTROL) && obs_only == 0) { - return; - } + // Update speed with acceleration + speed += acceleration * env->dt; + speed = clip(speed, -MAX_SPEED, MAX_SPEED); + // Compute yaw rate + float beta = atanf(0.5f * tanf(steering)); + // New heading + float yaw_rate = (speed * cosf(beta) * tanf(steering)) / agent->wheelbase; - int ego_dim = (env->dynamics_model == JERK) ? EGO_FEATURES_JERK : EGO_FEATURES_CLASSIC; - int max_obs = ego_dim + PARTNER_FEATURES * (MAX_AGENTS - 1) + ROAD_FEATURES * MAX_ROAD_SEGMENT_OBSERVATIONS; - float (*observations)[max_obs] = (float (*)[max_obs])env->observations; - float *agent_obs = &observations[agent_index][0]; - // self - int active_idx = env->active_agent_indices[agent_index]; - float heading_self_x = env->entities[active_idx].heading_x; - float heading_self_y = env->entities[active_idx].heading_y; - float px = env->entities[active_idx].x; - float py = env->entities[active_idx].y; - // draw goal - float goal_x = agent_obs[0] * 200; - float goal_y = agent_obs[1] * 200; - - int agent_type = env->entities[active_idx].type; - Color goal_color = LIGHTBLUE; - if (agent_type == PEDESTRIAN) - goal_color = LIGHT_ORANGE; - else if (agent_type == CYCLIST) - goal_color = LIGHT_PURPLE; - - if (mode == 0) { // agent-relative coordinates - DrawSphere((Vector3){goal_x, goal_y, Z_AGENT_DETAILS}, 0.5f, goal_color); - DrawCircle3D((Vector3){goal_x, goal_y, Z_AGENT_DETAILS}, env->goal_radius, (Vector3){0, 0, 1}, 90.0f, - Fade(goal_color, 0.3f)); - } - - if (mode == 1) { // world coordinates - - float goal_x_world = px + (goal_x * heading_self_x - goal_y * heading_self_y); - float goal_y_world = py + (goal_x * heading_self_y + goal_y * heading_self_x); - DrawSphere((Vector3){goal_x_world, goal_y_world, Z_AGENT_DETAILS}, 0.5f, goal_color); - DrawCircle3D((Vector3){goal_x_world, goal_y_world, Z_AGENT_DETAILS}, env->goal_radius, (Vector3){0, 0, 1}, - 90.0f, Fade(goal_color, 0.3f)); - } - // First draw other agent observations - int obs_idx = ego_dim; // Start after ego obs - for (int j = 0; j < MAX_AGENTS - 1; j++) { - if (agent_obs[obs_idx] == 0 || agent_obs[obs_idx + 1] == 0) { - obs_idx += 7; // Move to next agent observation - continue; - } - // Draw position of other agents - float x = agent_obs[obs_idx] * 50; - float y = agent_obs[obs_idx + 1] * 50; - if (lasers && mode == 0) { - DrawLine3D((Vector3){0, 0, 0}, (Vector3){x, y, Z_AGENT_DETAILS}, ORANGE); - } - - float partner_x = px + (x * heading_self_x - y * heading_self_y); - float partner_y = py + (x * heading_self_y + y * heading_self_x); - if (lasers && mode == 1) { - DrawLine3D((Vector3){px, py, Z_AGENT_DETAILS}, (Vector3){partner_x, partner_y, Z_AGENT_DETAILS}, ORANGE); - } - - float half_width = 0.5 * agent_obs[obs_idx + 2] * MAX_VEH_WIDTH; - float half_len = 0.5 * agent_obs[obs_idx + 3] * MAX_VEH_LEN; - float theta_x = agent_obs[obs_idx + 4]; - float theta_y = agent_obs[obs_idx + 5]; - float partner_angle = atan2f(theta_y, theta_x); - float cos_heading = cosf(partner_angle); - float sin_heading = sinf(partner_angle); - Vector3 corners[4] = { - (Vector3){x + (half_len * cos_heading - half_width * sin_heading), - y + (half_len * sin_heading + half_width * cos_heading), Z_AGENT_DETAILS}, - (Vector3){x + (half_len * cos_heading + half_width * sin_heading), - y + (half_len * sin_heading - half_width * cos_heading), Z_AGENT_DETAILS}, - (Vector3){x + (-half_len * cos_heading + half_width * sin_heading), - y + (-half_len * sin_heading - half_width * cos_heading), Z_AGENT_DETAILS}, - (Vector3){x + (-half_len * cos_heading - half_width * sin_heading), - y + (-half_len * sin_heading + half_width * cos_heading), Z_AGENT_DETAILS}, - }; - - if (mode == 0) { - for (int j = 0; j < 4; j++) { - DrawLine3D(corners[j], corners[(j + 1) % 4], ORANGE); - } - } + // New velocity + float new_vx = speed * cosf(heading + beta); + float new_vy = speed * sinf(heading + beta); - if (mode == 1) { - Vector3 world_corners[4]; - for (int j = 0; j < 4; j++) { - float lx = corners[j].x; - float ly = corners[j].y; + // Update position + x = x + (new_vx * env->dt); + y = y + (new_vy * env->dt); + heading = heading + yaw_rate * env->dt; - world_corners[j].x = px + (lx * heading_self_x - ly * heading_self_y); - world_corners[j].y = py + (lx * heading_self_y + ly * heading_self_x); - world_corners[j].z = 1; - } - for (int j = 0; j < 4; j++) { - DrawLine3D(world_corners[j], world_corners[(j + 1) % 4], ORANGE); - } - } + // Apply updates to the agent's state + agent->sim_x = x; + agent->sim_y = y; + agent->sim_heading = normalize_heading(heading); + agent->cos_heading = cosf(agent->sim_heading); + agent->sin_heading = sinf(agent->sim_heading); + agent->sim_vx = new_vx; + agent->sim_vy = new_vy; + agent->yaw_rate = yaw_rate; + update_agent_speed(agent); + + // Compute acceleration and jerk from finite differences (for comfort metric) + float new_a_long = acceleration; // commanded longitudinal acceleration + float new_a_lat = speed * yaw_rate; // centripetal: v * omega + agent->jerk_long = (new_a_long - agent->a_long) / env->dt; + agent->jerk_lat = (new_a_lat - agent->a_lat) / env->dt; + agent->a_long = new_a_long; + agent->a_lat = new_a_lat; + } else { + // JERK dynamics model + // Extract jerk action components + float j_long, j_lat; + if (env->action_type == 1) { // continuous + float (*action_array_f)[2] = (float (*)[2])env->actions; - // draw an arrow above the car pointing in the direction that the partner is going - float arrow_length = 2.5f; - float arrow_x = x + arrow_length * cosf(partner_angle); - float arrow_y = y + arrow_length * sinf(partner_angle); - float arrow_x_world; - float arrow_y_world; - if (mode == 0) { - DrawLine3D((Vector3){x, y, Z_AGENT_DETAILS}, (Vector3){arrow_x, arrow_y, Z_AGENT_DETAILS}, PUFF_WHITE); - } - if (mode == 1) { - arrow_x_world = px + (arrow_x * heading_self_x - arrow_y * heading_self_y); - arrow_y_world = py + (arrow_x * heading_self_y + arrow_y * heading_self_x); - DrawLine3D((Vector3){partner_x, partner_y, Z_AGENT_DETAILS}, - (Vector3){arrow_x_world, arrow_y_world, Z_AGENT_DETAILS}, PUFF_WHITE); - } - // Calculate perpendicular offsets for arrow head - float arrow_size = 0.3f; // Size of the arrow head - float dx = arrow_x - x; - float dy = arrow_y - y; - float length = sqrtf(dx * dx + dy * dy); - if (length > 0) { - // Normalize direction vector - dx /= length; - dy /= length; - - // Calculate perpendicular vector - float perp_x = -dy * arrow_size; - float perp_y = dx * arrow_size; - - float arrow_x_end1 = arrow_x - dx * arrow_size + perp_x; - float arrow_y_end1 = arrow_y - dy * arrow_size + perp_y; - float arrow_x_end2 = arrow_x - dx * arrow_size - perp_x; - float arrow_y_end2 = arrow_y - dy * arrow_size - perp_y; - - // Draw the two lines forming the arrow head - if (mode == 0) { - DrawLine3D((Vector3){arrow_x, arrow_y, 0.0}, (Vector3){arrow_x_end1, arrow_y_end1, 0.0}, PUFF_WHITE); - DrawLine3D((Vector3){arrow_x, arrow_y, 0.0}, (Vector3){arrow_x_end2, arrow_y_end2, 0.0}, PUFF_WHITE); + // Asymmetric scaling for longitudinal jerk to match discrete action space + // Discrete: JERK_LONG = [-15, -4, 0, 4] (more braking than acceleration) + float j_long_action = action_array_f[action_idx][0]; // [-1, 1] + if (j_long_action < 0) { + j_long = j_long_action * (-JERK_LONG[0]); // Negative: [-1, 0] → [-15, 0] (braking) + } else { + j_long = j_long_action * JERK_LONG[3]; // Positive: [0, 1] → [0, 4] (acceleration) } - if (mode == 1) { - float arrow_x_end1_world = px + (arrow_x_end1 * heading_self_x - arrow_y_end1 * heading_self_y); - float arrow_y_end1_world = py + (arrow_x_end1 * heading_self_y + arrow_y_end1 * heading_self_x); - float arrow_x_end2_world = px + (arrow_x_end2 * heading_self_x - arrow_y_end2 * heading_self_y); - float arrow_y_end2_world = py + (arrow_x_end2 * heading_self_y + arrow_y_end2 * heading_self_x); - DrawLine3D((Vector3){arrow_x_world, arrow_y_world, 0.0}, - (Vector3){arrow_x_end1_world, arrow_y_end1_world, 0.0}, PUFF_WHITE); - DrawLine3D((Vector3){arrow_x_world, arrow_y_world, 0.0}, - (Vector3){arrow_x_end2_world, arrow_y_end2_world, 0.0}, PUFF_WHITE); - } + // Symmetric scaling for lateral jerk + j_lat = action_array_f[action_idx][1] * JERK_LAT[2]; + } else if (env->action_type == 0) { // discrete + // Interpret action as a single integer: a = long_idx * num_lat + lat_idx + int *action_array = (int *)env->actions; + int num_lat = sizeof(JERK_LAT) / sizeof(JERK_LAT[0]); + int action_val = action_array[action_idx]; + int j_long_idx = action_val / num_lat; + int j_lat_idx = action_val % num_lat; + j_long = JERK_LONG[j_long_idx]; + j_lat = JERK_LAT[j_lat_idx]; } - obs_idx += PARTNER_FEATURES; // Move to next agent observation (7 values per agent) - } - // Then draw map observations - int map_start_idx = ego_dim + PARTNER_FEATURES * (MAX_AGENTS - 1); // Start after agent observations - for (int k = 0; k < MAX_ROAD_SEGMENT_OBSERVATIONS; k++) { // Loop through potential map entities - int entity_idx = map_start_idx + k * 7; - if (agent_obs[entity_idx] == 0 && agent_obs[entity_idx + 1] == 0) { - continue; - } - Color lineColor = BLUE; // Default color - int entity_type = (int)agent_obs[entity_idx + 6]; - // Choose color based on entity type - if (entity_type + 4 != ROAD_EDGE) { - continue; - } - lineColor = PUFF_CYAN; - // For road segments, draw line between start and end points - float x_middle = agent_obs[entity_idx] * 50; - float y_middle = agent_obs[entity_idx + 1] * 50; - float rel_angle_x = (agent_obs[entity_idx + 4]); - float rel_angle_y = (agent_obs[entity_idx + 5]); - float rel_angle = atan2f(rel_angle_y, rel_angle_x); - float segment_length = agent_obs[entity_idx + 2] * MAX_ROAD_SEGMENT_LENGTH; - // Calculate endpoint using the relative angle directly - // Calculate endpoint directly - float x_start = x_middle - segment_length * cosf(rel_angle); - float y_start = y_middle - segment_length * sinf(rel_angle); - float x_end = x_middle + segment_length * cosf(rel_angle); - float y_end = y_middle + segment_length * sinf(rel_angle); - - if (lasers && mode == 0) { - DrawLine3D((Vector3){0, 0, 0}, (Vector3){x_middle, y_middle, 1}, lineColor); - } - - if (mode == 1) { - float x_middle_world = px + (x_middle * heading_self_x - y_middle * heading_self_y); - float y_middle_world = py + (x_middle * heading_self_y + y_middle * heading_self_x); - float x_start_world = px + (x_start * heading_self_x - y_start * heading_self_y); - float y_start_world = py + (x_start * heading_self_y + y_start * heading_self_x); - float x_end_world = px + (x_end * heading_self_x - y_end * heading_self_y); - float y_end_world = py + (x_end * heading_self_y + y_end * heading_self_x); - DrawCube((Vector3){x_middle_world, y_middle_world, 1}, 0.5f, 0.5f, 0.5f, lineColor); - DrawLine3D((Vector3){x_start_world, y_start_world, 1}, (Vector3){x_end_world, y_end_world, 1}, BLUE); - if (lasers) - DrawLine3D((Vector3){px, py, 1}, (Vector3){x_middle_world, y_middle_world, 1}, lineColor); - } - if (mode == 0) { - DrawCube((Vector3){x_middle, y_middle, 1}, 0.5f, 0.5f, 0.5f, lineColor); - DrawLine3D((Vector3){x_start, y_start, 1}, (Vector3){x_end, y_end, 1}, BLUE); - } - } -} - -void draw_road_edge(Drive *env, float start_x, float start_y, float end_x, float end_y) { - Color CURB_TOP = (Color){220, 220, 220, 255}; // Top surface - lightest - Color CURB_SIDE = (Color){180, 180, 180, 255}; // Side faces - medium - Color CURB_BOTTOM = (Color){160, 160, 160, 255}; - // Calculate curb dimensions - float curb_height = 0.5f; // Height of the curb - float curb_width = 0.3f; // Width/thickness of the curb - float road_z = 0.0f; // Ensure z-level for roads is below agents - - // Calculate direction vector between start and end - Vector3 direction = {end_x - start_x, end_y - start_y, 0.0f}; - - // Calculate length of the segment - float length = sqrtf(direction.x * direction.x + direction.y * direction.y); - - // Normalize direction vector - Vector3 normalized_dir = {direction.x / length, direction.y / length, 0.0f}; - - // Calculate perpendicular vector for width - Vector3 perpendicular = {-normalized_dir.y, normalized_dir.x, 0.0f}; - - // Calculate the four bottom corners of the curb - Vector3 b1 = {start_x - perpendicular.x * curb_width / 2, start_y - perpendicular.y * curb_width / 2, road_z}; - Vector3 b2 = {start_x + perpendicular.x * curb_width / 2, start_y + perpendicular.y * curb_width / 2, road_z}; - Vector3 b3 = {end_x + perpendicular.x * curb_width / 2, end_y + perpendicular.y * curb_width / 2, road_z}; - Vector3 b4 = {end_x - perpendicular.x * curb_width / 2, end_y - perpendicular.y * curb_width / 2, road_z}; - - // Draw the curb faces - // Bottom face - DrawTriangle3D(b1, b2, b3, CURB_BOTTOM); - DrawTriangle3D(b1, b3, b4, CURB_BOTTOM); - - // Top face (raised by curb_height) - Vector3 t1 = {b1.x, b1.y, b1.z + curb_height}; - Vector3 t2 = {b2.x, b2.y, b2.z + curb_height}; - Vector3 t3 = {b3.x, b3.y, b3.z + curb_height}; - Vector3 t4 = {b4.x, b4.y, b4.z + curb_height}; - DrawTriangle3D(t1, t3, t2, CURB_TOP); - DrawTriangle3D(t1, t4, t3, CURB_TOP); - - // Side faces - DrawTriangle3D(b1, t1, b2, CURB_SIDE); - DrawTriangle3D(t1, t2, b2, CURB_SIDE); - DrawTriangle3D(b2, t2, b3, CURB_SIDE); - DrawTriangle3D(t2, t3, b3, CURB_SIDE); - DrawTriangle3D(b3, t3, b4, CURB_SIDE); - DrawTriangle3D(t3, t4, b4, CURB_SIDE); - DrawTriangle3D(b4, t4, b1, CURB_SIDE); - DrawTriangle3D(t4, t1, b1, CURB_SIDE); -} - -void draw_scene(Drive *env, Client *client, int mode, int obs_only, int lasers, int show_grid) { - - if (show_grid) { - float grid_start_x = env->grid_map->top_left_x; - float grid_start_y = env->grid_map->bottom_right_y; - for (int i = 0; i < env->grid_map->grid_cols; i++) { - for (int j = 0; j < env->grid_map->grid_rows; j++) { - float x = grid_start_x + i * GRID_CELL_SIZE; - float y = grid_start_y + j * GRID_CELL_SIZE; - DrawCubeWires((Vector3){x + GRID_CELL_SIZE / 2, y + GRID_CELL_SIZE / 2, 0.0f}, GRID_CELL_SIZE, - GRID_CELL_SIZE, 0.1f, Fade(PUFF_BACKGROUND2, 0.3f)); - } - } - } + // Get dynamic conditioning coefficients + float c_throttle = agent->reward_coefs[REWARD_COEF_THROTTLE]; + float c_steer = agent->reward_coefs[REWARD_COEF_STEER]; + float c_acc = agent->reward_coefs[REWARD_COEF_ACC]; - // Draw a grid to help with orientation - for (int i = 0; i < env->num_entities; i++) { - // Draw objects - if (env->entities[i].type == VEHICLE || env->entities[i].type == PEDESTRIAN || - env->entities[i].type == CYCLIST) { - // Check if this vehicle is an active agent - bool is_active_agent = false; - bool is_static_agent = false; - int agent_index = -1; - for (int j = 0; j < env->active_agent_count; j++) { - if (env->active_agent_indices[j] == i) { - is_active_agent = true; - agent_index = j; - break; - } - } + // Calculate new longitudinal acceleration from jerk (Eq. 1 in paper) + float a_long_new = agent->a_long + c_throttle * j_long * env->dt; - for (int j = 0; j < env->expert_static_agent_count; j++) { - if (env->expert_static_agent_indices[j] == i) { - is_static_agent = true; - break; - } - } + // Zero-crossing: snap to 0 when crossing zero + if (agent->a_long * a_long_new < 0) { + a_long_new = 0.0f; + } else { + a_long_new = clip(a_long_new, -5.0f, 2.5f * c_acc); + } - for (int j = 0; j < env->static_agent_count; j++) { - if (env->static_agent_indices[j] == i) { - is_static_agent = true; - break; - } - } + // Calculate new lateral acceleration from jerk (Eq. 2 in paper) + float a_lat_new = agent->a_lat + c_steer * j_lat * env->dt; - if ((!is_active_agent && !is_static_agent) || env->entities[i].respawn_timestep != -1) { - continue; - } - Vector3 position; - float heading; - position = (Vector3){env->entities[i].x, env->entities[i].y, Z_AGENTS}; - heading = env->entities[i].heading; - - // Create size vector - Vector3 size = {env->entities[i].length, env->entities[i].width, env->entities[i].height}; - - bool is_expert = (!is_active_agent) && (env->entities[i].mark_as_expert == 1); - - // Save current transform - if (mode == 1) { - float cos_heading = env->entities[i].heading_x; - float sin_heading = env->entities[i].heading_y; - - // Calculate half dimensions - float half_len = env->entities[i].length * 0.5f; - float half_width = env->entities[i].width * 0.5f; - - // Calculate the four corners of the collision box - Vector3 corners[4] = { - (Vector3){position.x + (half_len * cos_heading - half_width * sin_heading), - position.y + (half_len * sin_heading + half_width * cos_heading), position.z}, - (Vector3){position.x + (half_len * cos_heading + half_width * sin_heading), - position.y + (half_len * sin_heading - half_width * cos_heading), position.z}, - (Vector3){position.x + (-half_len * cos_heading + half_width * sin_heading), - position.y + (-half_len * sin_heading - half_width * cos_heading), position.z}, - (Vector3){position.x + (-half_len * cos_heading - half_width * sin_heading), - position.y + (-half_len * sin_heading + half_width * cos_heading), position.z}, + // Zero-crossing: snap to 0 when crossing zero + if (agent->a_lat * a_lat_new < 0) { + a_lat_new = 0.0f; + } else { + a_lat_new = clip(a_lat_new, -4.0f, 4.0f); + } - }; + float heading_x = agent->cos_heading; + float heading_y = agent->sin_heading; - if (agent_index == env->human_agent_idx && - !env->entities[agent_index].metrics_array[REACHED_GOAL_IDX]) { - draw_agent_obs(env, agent_index, mode, obs_only, lasers); - } + // Calculate new velocity using trapezoidal integration + float v_dot_heading = agent->sim_vx * heading_x + agent->sim_vy * heading_y; + float signed_v = copysignf(sqrtf(agent->sim_vx * agent->sim_vx + agent->sim_vy * agent->sim_vy), v_dot_heading); + float v_new = signed_v + 0.5f * (a_long_new + agent->a_long) * env->dt; - if ((obs_only || IsKeyDown(KEY_LEFT_CONTROL)) && agent_index != env->human_agent_idx) { - continue; - } + // Zero-crossing: snap to 0 when crossing zero + if (signed_v * v_new < 0) { + v_new = 0.0f; + } else { + v_new = clip(v_new, -2.0f, 20.0f); + } - // Draw the agent bounding boxes - Color agent_color = GRAY; - if (is_expert) { - if (env->entities[i].type == PEDESTRIAN || env->entities[i].type == CYCLIST) - agent_color = EXPERT_REPLAY_SMALL; - else - agent_color = EXPERT_REPLAY; - } - if (is_active_agent) { - if (env->entities[i].type == PEDESTRIAN) - agent_color = LIGHT_ORANGE; - else if (env->entities[i].type == CYCLIST) - agent_color = LIGHT_PURPLE; - else - agent_color = BLUE; - } - if (is_active_agent && env->entities[i].collision_state > 0) - agent_color = RED; + // GIGAFLOW paper approach: a_lat → curvature → steering + // v_eff = max(|v|, 1.0) to avoid division issues at low speed + float v_eff = fmaxf(fabsf(v_new), 1.0f); + float signed_curvature = a_lat_new / (v_eff * v_eff); - rlPushMatrix(); - rlTranslatef(position.x, position.y, position.z); - rlRotatef(heading * RAD2DEG, 0.0f, 0.0f, 1.0f); - DrawCube((Vector3){0.0f, 0.0f, 0.0f}, size.x, size.y, 1.0f, Fade(agent_color, 0.5f)); - DrawCubeWires((Vector3){0.0f, 0.0f, 0.0f}, size.x, size.y, 1.0f, agent_color); - rlPopMatrix(); + // Convert curvature to steering angle + float steering_angle = atanf(signed_curvature * agent->wheelbase); - // Draw a heading arrow pointing forward - Vector3 arrowStart = position; - Vector3 arrowEnd = {position.x + cos_heading * half_len * 1.5f, // extend arrow beyond car - position.y + sin_heading * half_len * 1.5f, position.z}; + // Apply steering rate limit (±0.6 rad/s) + float delta_steer = clip(steering_angle - agent->steering_angle, -0.6f * env->dt, 0.6f * env->dt); - DrawLine3D(arrowStart, arrowEnd, agent_color); - DrawSphere(arrowEnd, 0.2f, agent_color); // arrow tip + // Apply steering position limit (±0.55 rad) + float new_steering_angle = clip(agent->steering_angle + delta_steer, -0.55f, 0.55f); - } else { // Agent view - rlPushMatrix(); - // Translate to position, rotate around Y axis, then draw - rlTranslatef(position.x, position.y, position.z); - rlRotatef(heading * RAD2DEG, 0.0f, 0.0f, 1.0f); // Convert radians to degrees + // Recalculate curvature from limited steering + signed_curvature = tanf(new_steering_angle) / agent->wheelbase; - // Select car model (skip index 0) - Model car_model = client->cars[(i % 5) + 1]; // Cycles through indices 1-5 + // Recalculate lateral acceleration from actual curvature + a_lat_new = v_new * v_new * signed_curvature; - if (agent_index == env->human_agent_idx) { - car_model = client->cars[0]; // Ego agent always uses red car - } else if (is_active_agent) { + // Calculate resulting movement using bicycle dynamics + float d = 0.5f * (v_new + signed_v) * env->dt; + float theta = d * signed_curvature; + float dx_local, dy_local; - car_model = client->cars[(i % 5) + 1]; + if (fabsf(signed_curvature) < 1e-5f || fabsf(theta) < 1e-5f) { + dx_local = d; + dy_local = 0.0f; + } else { + dx_local = sinf(theta) / signed_curvature; + dy_local = (1.0f - cosf(theta)) / signed_curvature; + } - if (env->entities[i].collision_state > 0) { - car_model = client->cars[0]; // Collided agents use red - } - } - // Draw obs for selected agent index - if (agent_index == env->human_agent_idx && - (!env->entities[agent_index].metrics_array[REACHED_GOAL_IDX] || - env->goal_behavior == GOAL_GENERATE_NEW || env->goal_behavior == GOAL_STOP)) { - draw_agent_obs(env, agent_index, mode, obs_only, lasers); - } + float dx = dx_local * heading_x - dy_local * heading_y; + float dy = dx_local * heading_y + dy_local * heading_x; + + // Update agent state + agent->sim_x += dx; + agent->sim_y += dy; + agent->sim_heading = normalize_heading(agent->sim_heading + theta); + agent->cos_heading = cosf(agent->sim_heading); + agent->sin_heading = sinf(agent->sim_heading); + agent->sim_vx = v_new * cosf(agent->sim_heading); + agent->sim_vy = v_new * sinf(agent->sim_heading); + const float yaw_rate = v_new * signed_curvature; + agent->yaw_rate = yaw_rate; + + update_agent_speed(agent); + // Update jerk and acceleration + agent->jerk_long = (a_long_new - agent->a_long) / env->dt; + agent->jerk_lat = (a_lat_new - agent->a_lat) / env->dt; + agent->a_long = a_long_new; + agent->a_lat = a_lat_new; + agent->steering_angle = new_steering_angle; + } - // Draw cube for cars static and active - // Calculate scale factors based on desired size and model dimensions - BoundingBox bounds = GetModelBoundingBox(car_model); - Vector3 model_size = {bounds.max.x - bounds.min.x, bounds.max.y - bounds.min.y, - bounds.max.z - bounds.min.z}; - Vector3 scale = {size.x / model_size.x, size.y / model_size.y, size.z / model_size.z}; + GridMapEntity entity_list[MAX_ENTITIES_PER_CELL * Z_COMPUTATION_OFFSET_COUNT]; + int list_size = get_neighbors_entities(env, agent->sim_x, agent->sim_y, entity_list, + MAX_ENTITIES_PER_CELL * Z_COMPUTATION_OFFSET_COUNT, z_computation_offsets, + Z_COMPUTATION_OFFSET_COUNT); + if (list_size > 0) { + DepthPoint road_neighbors[list_size]; + DepthPoint current_lane_neighbors[list_size]; + int valid_count = 0; + int current_lane_count = 0; + for (int i = 0; i < list_size; i++) { + if (entity_list[i].entity_idx == -1) + continue; + if (entity_list[i].entity_type != ENTITY_TYPE_ROAD_ELEMENT) + continue; - if (env->entities[i].type == CYCLIST) { - scale = (Vector3){0.01, 0.01, 0.01}; - car_model = client->cyclist; - } - if (env->entities[i].type == PEDESTRIAN) { - scale = (Vector3){2, 2, 2}; - car_model = client->pedestrian; - } - DrawModelEx(car_model, (Vector3){0, 0, 0}, (Vector3){1, 0, 0}, 90.0f, scale, WHITE); - { - float half_len = env->entities[i].length * 0.5f; - float half_width = env->entities[i].width * 0.5f; - Vector3 corners[4] = { - (Vector3){half_len, -half_width, 0}, // Front-left - (Vector3){half_len, half_width, 0}, // Front-right - (Vector3){-half_len, half_width, 0}, // Back-right - (Vector3){-half_len, -half_width, 0}, // Back-left - }; - Color wire_color = GRAY; - if (!is_active_agent && env->entities[i].mark_as_expert == 1) - wire_color = EXPERT_REPLAY; - if (is_active_agent) - wire_color = BLUE; // Policy-controlled - if (is_active_agent && env->entities[i].collision_state > 0) - wire_color = RED; - rlSetLineWidth(2.0f); - for (int j = 0; j < 4; j++) { - DrawLine3D(corners[j], corners[(j + 1) % 4], wire_color); - } + const RoadMapElement *entity = &env->road_elements[entity_list[i].entity_idx]; + DepthPoint point = compute_z_distance_to_road_segment(agent, entity, entity_list[i].geometry_idx); + if (point.z_dis < Z_BUFFER) { + road_neighbors[valid_count++] = point; + if (entity_list[i].entity_idx == agent->current_lane_index) { + current_lane_neighbors[current_lane_count++] = point; } - rlPopMatrix(); - } - - // FPV Camera Control - if (IsKeyDown(KEY_SPACE) && env->human_agent_idx == agent_index) { - Vector3 camera_position = (Vector3){position.x - (25.0f * cosf(heading)), - position.y - (25.0f * sinf(heading)), position.z + 15}; - - Vector3 camera_target = (Vector3){position.x + 40.0f * cosf(heading), - position.y + 40.0f * sinf(heading), position.z - 5.0f}; - client->camera.position = camera_position; - client->camera.target = camera_target; - client->camera.up = (Vector3){0, 0, 1}; - } - if (IsKeyReleased(KEY_SPACE)) { - client->camera.position = client->default_camera_position; - client->camera.target = client->default_camera_target; - client->camera.up = (Vector3){0, 0, 1}; - } - // Draw goal position for active agents - if (!is_active_agent || env->entities[i].valid == 0) { - continue; - } - if (!IsKeyDown(KEY_LEFT_CONTROL) && obs_only == 0) { - Color goal_color = DEEPBLUE; - if (env->entities[i].type == PEDESTRIAN) - goal_color = LIGHT_ORANGE; - else if (env->entities[i].type == CYCLIST) - goal_color = LIGHT_PURPLE; - - DrawSphere( - (Vector3){env->entities[i].goal_position_x, env->entities[i].goal_position_y, Z_AGENT_DETAILS}, - 0.5f, goal_color); - DrawCircle3D( - (Vector3){env->entities[i].goal_position_x, env->entities[i].goal_position_y, Z_AGENT_DETAILS}, - env->goal_radius, (Vector3){0, 0, Z_AGENT_DETAILS}, 90.0f, Fade(goal_color, 0.9f)); } } - // Draw road elements - if (env->entities[i].type <= 3 && env->entities[i].type >= 7) { - continue; - } - for (int j = 0; j < env->entities[i].array_size - 1; j++) { - Vector3 start = {env->entities[i].traj_x[j], env->entities[i].traj_y[j], Z_ROAD_MARKINGS}; - Vector3 end = {env->entities[i].traj_x[j + 1], env->entities[i].traj_y[j + 1], Z_ROAD_MARKINGS}; - Color lineColor = GRAY; - if (env->entities[i].type == ROAD_LANE) - lineColor = Fade(SOFT_YELLOW, 0.25f); - else if (env->entities[i].type == ROAD_LINE) - lineColor = WHITE; - else if (env->entities[i].type == ROAD_EDGE) - lineColor = Fade(WHITE, 0.7f); - else if (env->entities[i].type == DRIVEWAY) - lineColor = RED; - - if (!IsKeyDown(KEY_LEFT_CONTROL) && obs_only == 0) { - if (env->entities[i].type == ROAD_EDGE) { - draw_road_edge(env, start.x, start.y, end.x, end.y); - } else if (env->entities[i].type == ROAD_LANE || env->entities[i].type == ROAD_LINE) { - // Draw road lanes and lines as purple lines - rlSetLineWidth(2.0f); - DrawLine3D(start, end, lineColor); - } + + int neighbor_count = (current_lane_count > 0) ? current_lane_count : valid_count; + if (neighbor_count > 0) { + DepthPoint *neighbors = (current_lane_count > 0) ? current_lane_neighbors : road_neighbors; + qsort(neighbors, neighbor_count, sizeof(DepthPoint), compare_depthpoint); + int check_count = (neighbor_count < Z_NUM_PT_AVG) ? neighbor_count : Z_NUM_PT_AVG; + float sum_z = 0.0f; + for (int i = 0; i < check_count; i++) { + sum_z += neighbors[i].z; } + agent->sim_z = sum_z / check_count; } } - EndMode3D(); - - // Draw track indices for the tracks to predict - if (mode == 1 && env->control_mode == CONTROL_WOSAC) { - float map_height = env->grid_map->top_left_y - env->grid_map->bottom_right_y; - float pixels_per_world_unit = client->height / map_height; + return; +} - for (int i = 0; i < env->active_agent_count; i++) { - // Ignore respawned agents - if (env->entities[i].respawn_timestep != -1) { - continue; - } - int agent_idx = env->active_agent_indices[i]; - int womd_track_idx = env->tracks_to_predict_indices[i]; +void c_reset(Drive *env) { + if (env->timestep == 0) { + for (int x = 0; x < env->active_agent_count; x++) { + env->logs[x] = (Log){0}; + int agent_idx = env->active_agent_indices[x]; + initialize_agent_progression(env, agent_idx); + compute_metrics(env, agent_idx); + } + compute_observations(env); + return; + } - float raw_x = -env->entities[agent_idx].x * pixels_per_world_unit; - float raw_y = env->entities[agent_idx].y * pixels_per_world_unit; + env->timestep = env->init_steps; - int screen_x = (int)raw_x + client->width / 2 + 20; - int screen_y = (int)raw_y + client->height / 2 - 25; + if (env->simulation_mode == SIMULATION_GIGAFLOW) { + generate_traffic_light_states(env); + int num_reset = 0; + for (int x = 0; x < env->active_agent_count; x++) { + int agent_idx = env->active_agent_indices[x]; - if (screen_x >= 0 && screen_x <= client->width && screen_y >= 0 && screen_y <= client->height) { - char text[32]; - snprintf(text, sizeof(text), "%d", womd_track_idx); - int text_width = MeasureText(text, 20); - DrawText(text, screen_x - text_width / 2, screen_y, 20, PUFF_WHITE); + // Respawn agent at new random position + if (spawn_agent(env, agent_idx, num_reset)) { + num_reset++; + } else { + // Failed spawn: ensure agent is properly invalidated + invalidate_agent(&env->agents[agent_idx]); + env->agents[agent_idx].removed = 1; } } - } -} -void c_render(Drive *env, int view_mode, int draw_traces) { + if (num_reset != env->active_agent_count) { + printf("[GIGAFLOW ERROR] -> Only respawned %d out of %d agents during reset\n", num_reset, + env->active_agent_count); + } - // Create client on first render call - if (env->client == NULL) { - env->client = make_client(env); + // GIGAFLOW: spawn_agent already set positions, routes, paths, goals. + // Only need to generate reward coefs and compute initial metrics. + for (int x = 0; x < env->active_agent_count; x++) { + env->logs[x] = (Log){0}; + int agent_idx = env->active_agent_indices[x]; + Agent *agent = &env->agents[agent_idx]; + if (agent->removed) + continue; + reset_agent_metrics(env, agent_idx); + reset_agent_state(agent); + generate_reward_coefs(env, agent); + initialize_agent_progression(env, agent_idx); + compute_metrics(env, agent_idx); + } + compute_observations(env); + return; } - Client *client = env->client; + set_start_position(env); + for (int x = 0; x < env->active_agent_count; x++) { + env->logs[x] = (Log){0}; + int agent_idx = env->active_agent_indices[x]; + Agent *agent = &env->agents[agent_idx]; - if (env->render_mode == RENDER_HEADLESS) { // Headless rendering via ffmpeg - float map_width = env->grid_map->bottom_right_x - env->grid_map->top_left_x; - float map_height = env->grid_map->top_left_y - env->grid_map->bottom_right_y; + // Common resets + reset_agent_metrics(env, agent_idx); + reset_agent_state(agent); + generate_reward_coefs(env, agent); - Camera3D camera = {0}; + compute_goals(env, agent_idx); + initialize_agent_progression(env, agent_idx); + compute_metrics(env, agent_idx); + } + compute_observations(env); +} - if (view_mode == VIEW_MODE_SIM_STATE) { - // Orthographic bird's-eye view over the entire map (fully observable) - camera.position = (Vector3){0.0, 0.0, 400.0f}; // Above the scene - camera.target = (Vector3){0.0, 0.0, 0.0}; // Look at origin - camera.up = (Vector3){0.0f, -1.0f, 0.0f}; - camera.projection = CAMERA_ORTHOGRAPHIC; - camera.fovy = map_height; +void c_step(Drive *env) { + memset(env->rewards, 0, env->active_agent_count * sizeof(float)); + memset(env->terminals, 0, env->active_agent_count * sizeof(unsigned char)); + memset(env->truncations, 0, env->active_agent_count * sizeof(unsigned char)); - BeginDrawing(); - ClearBackground(ROAD_COLOR); - BeginMode3D(camera); + // Update masks for stopped/removed agents + for (int i = 0; i < env->active_agent_count; i++) { + int agent_idx = env->active_agent_indices[i]; + if (env->agents[agent_idx].stopped || env->agents[agent_idx].removed) { + env->masks[i] = 0; + } else { + env->masks[i] = 1; + } + } - if (draw_traces) { // Show logged trajectories of active agents and expert static agents - for (int i = 0; i < env->active_agent_count; i++) { - int idx = env->active_agent_indices[i]; - for (int t = env->init_steps; t < env->episode_length; t++) { - Color agent_color = LIGHTBLUE; - if (env->entities[idx].type == PEDESTRIAN) { - agent_color = LIGHT_ORANGE; - } else if (env->entities[idx].type == CYCLIST) { - agent_color = LIGHT_PURPLE; - } - DrawSphere( - (Vector3){env->entities[idx].traj_x[t], env->entities[idx].traj_y[t], Z_AGENT_DETAILS}, - 0.15f, agent_color); - } - } + env->timestep++; - for (int i = 0; i < env->expert_static_agent_count; i++) { - int idx = env->expert_static_agent_indices[i]; - for (int t = env->init_steps; t < env->episode_length; t++) { - DrawSphere( - (Vector3){env->entities[idx].traj_x[t], env->entities[idx].traj_y[t], Z_AGENT_DETAILS}, - 0.15f, EXPERT_REPLAY); - } - } + // -> 1. Check for episode termination + int early_reset = 0; + // Special early reset condition for GIGAFLOW + if (env->simulation_mode == SIMULATION_GIGAFLOW && env->termination_mode == 1) { + int count_inactive = 0; + for (int i = 0; i < env->active_agent_count; i++) { + int agent_idx = env->active_agent_indices[i]; + if (env->agents[agent_idx].removed || env->agents[agent_idx].stopped) { + count_inactive++; } + } + float ratio_inactive = (float)count_inactive / (float)env->active_agent_count; + // If more than inactive_agent_threshold of agents are removed/stopped, reset the environment + if (ratio_inactive > env->inactive_agent_threshold) { + early_reset = 1; + } + } - draw_scene(env, client, 1, 0, 0, 0); - - } else if (view_mode == VIEW_MODE_BEV_AGENT_OBS) { - // Orthographic bird's-eye view centered on the selected agent, - // showing only that agent's observations - int agent_idx = env->active_agent_indices[env->human_agent_idx]; - Entity *agent = &env->entities[agent_idx]; - - Camera3D camera = {0}; - camera.position = (Vector3){agent->x, agent->y, 400.0f}; - camera.target = (Vector3){agent->x, agent->y, 0.0f}; - camera.up = (Vector3){0.0f, -1.0f, 0.0f}; - camera.projection = CAMERA_ORTHOGRAPHIC; - camera.fovy = env->grid_map->vision_range * GRID_CELL_SIZE * 2.0f; - - BeginDrawing(); - ClearBackground(ROAD_COLOR); - BeginMode3D(camera); - draw_scene(env, client, 1, 1, 0, 0); + if (env->timestep == env->scenario_length || early_reset) { + for (int i = 0; i < env->active_agent_count; i++) { + env->truncations[i] = 1; + } + add_log(env); + c_reset(env); + return; + } - } else { // First-person perspective from a selected agent - int agent_idx = env->active_agent_indices[env->human_agent_idx]; - Entity *agent = &env->entities[agent_idx]; + // -> 2. Apply actions and move agents + // Move static experts + for (int i = 0; i < env->expert_static_agent_count; i++) { + int expert_idx = env->expert_static_agent_indices[i]; + move_expert(env, env->actions, expert_idx); + } + // Move active agents with policy actions + for (int i = 0; i < env->active_agent_count; i++) { + env->logs[i].score = 0.0f; + env->logs[i].episode_length += 1; + int agent_idx = env->active_agent_indices[i]; + move_dynamics(env, i, agent_idx); + // move_expert(env, env->actions, agent_idx); + } - Camera3D camera = {0}; - // Position camera behind and above the agent - camera.position = - (Vector3){agent->x - (25.0f * cosf(agent->heading)), agent->y - (25.0f * sinf(agent->heading)), 15.0f}; - camera.target = - (Vector3){agent->x + 40.0f * cosf(agent->heading), agent->y + 40.0f * sinf(agent->heading), 1.0f}; - camera.up = (Vector3){0.0f, 0.0f, 1.0f}; - camera.fovy = 60.0f; - camera.projection = CAMERA_PERSPECTIVE; + // -> 3. Compute metrics and rewards + for (int i = 0; i < env->active_agent_count; i++) { + int agent_idx = env->active_agent_indices[i]; - BeginDrawing(); - ClearBackground(ROAD_COLOR); - BeginMode3D(camera); - draw_scene(env, client, 0, 0, 0, 1); - } + if (env->agents[agent_idx].stopped || env->agents[agent_idx].removed) + continue; - EndDrawing(); + // Compute metrics + compute_metrics(env, agent_idx); - unsigned char *screen_data = rlReadScreenPixels((int)client->width, (int)client->height); - if (screen_data) { - write(client->recorder_pipefd[1], screen_data, (int)client->width * (int)client->height * 4); - RL_FREE(screen_data); - } - } else { // Pop-up window - BeginDrawing(); - ClearBackground(ROAD_COLOR); - BeginMode3D(client->camera); - handle_camera_controls(env->client); - draw_scene(env, client, 0, 0, 0, 0); + // Compute rewards + compute_rewards(env, i); + } - if (IsKeyPressed(KEY_TAB) && env->active_agent_count > 0) { - env->human_agent_idx = (env->human_agent_idx + 1) % env->active_agent_count; + // Mark terminals for stopped or removed agents + for (int i = 0; i < env->active_agent_count; i++) { + int agent_idx = env->active_agent_indices[i]; + if (env->agents[agent_idx].stopped || env->agents[agent_idx].removed) { + env->terminals[i] = 1; } + } - DrawText(TextFormat("Timestep: %d", env->timestep), 10, 50, 20, PUFF_WHITE); - DrawText(TextFormat("Controlling agent: %d", env->human_agent_idx), 10, 70, 20, PUFF_WHITE); - int human_idx = env->active_agent_indices[env->human_agent_idx]; - - Color action_color = IsKeyDown(KEY_LEFT_SHIFT) ? YELLOW : PUFF_WHITE; + // -> 5. Compute observations + compute_observations(env); - if (env->action_type == 0) { // discrete - int *action_array = (int *)env->actions; - int action_val = action_array[env->human_agent_idx]; - - if (env->dynamics_model == CLASSIC) { - int num_steer = 13; - int accel_idx = action_val / num_steer; - int steer_idx = action_val % num_steer; - float accel_value = ACCELERATION_VALUES[accel_idx]; - float steer_value = STEERING_VALUES[steer_idx]; - - DrawText(TextFormat("Acceleration: %.2f m/s^2", accel_value), 10, 110, 20, action_color); - DrawText(TextFormat("Steering: %.3f", steer_value), 10, 130, 20, action_color); - } else if (env->dynamics_model == JERK) { - int num_lat = 3; - int jerk_long_idx = action_val / num_lat; - int jerk_lat_idx = action_val % num_lat; - float jerk_long_value = JERK_LONG[jerk_long_idx]; - float jerk_lat_value = JERK_LAT[jerk_lat_idx]; - - DrawText(TextFormat("Longitudinal Jerk: %.2f m/s^3", jerk_long_value), 10, 110, 20, action_color); - DrawText(TextFormat("Lateral Jerk: %.2f m/s^3", jerk_lat_value), 10, 130, 20, action_color); + // -> 6. Update goals for agents that reached their goal + for (int i = 0; i < env->active_agent_count; i++) { + int agent_idx = env->active_agent_indices[i]; + Agent *agent = &env->agents[agent_idx]; + if (agent->metrics_array[REACHED_GOAL_IDX] > 0.0f) { + if (agent->current_goal_idx == env->num_target_waypoints) { + // Last goal reached - generate new set of goals + env->logs[i].num_goals_reached += 1; + compute_goals(env, agent_idx); + } else { + // Advance alias to next goal + agent->goal_position_x = agent->goal_positions_x[agent->current_goal_idx]; + agent->goal_position_y = agent->goal_positions_y[agent->current_goal_idx]; + agent->goal_position_z = agent->goal_positions_z[agent->current_goal_idx]; } - } else { // continuous - float (*action_array_f)[2] = (float (*)[2])env->actions; - DrawText(TextFormat("Acceleration: %.2f", action_array_f[env->human_agent_idx][0]), 10, 110, 20, - action_color); - DrawText(TextFormat("Steering: %.2f", action_array_f[env->human_agent_idx][1]), 10, 130, 20, action_color); - } - - int status_y = 150; - if (IsKeyDown(KEY_LEFT_SHIFT)) { - DrawText("[shift pressed]", 10, status_y, 20, YELLOW); - status_y += 20; - } - if (IsKeyDown(KEY_SPACE)) { - DrawText("[space pressed]", 10, status_y, 20, YELLOW); - status_y += 20; } - if (IsKeyDown(KEY_LEFT_CONTROL)) { - DrawText("[ctrl pressed]", 10, status_y, 20, YELLOW); - status_y += 20; - } - - DrawText("Controls: SHIFT + W/S - Accelerate/Brake, SHIFT + A/D - Steer, TAB - Switch Agent", 10, - client->height - 30, 20, PUFF_WHITE); - DrawText(TextFormat("Grid Rows: %d", env->grid_map->grid_rows), 10, status_y, 20, PUFF_WHITE); - DrawText(TextFormat("Grid Cols: %d", env->grid_map->grid_cols), 10, status_y + 20, 20, PUFF_WHITE); - EndDrawing(); } } -void close_client(Client *client) { - if (client->recorder_pid > 0) { - close(client->recorder_pipefd[1]); - waitpid(client->recorder_pid, NULL, 0); - } - for (int i = 0; i < 6; i++) - UnloadModel(client->cars[i]); - UnloadModel(client->cyclist); - UnloadModel(client->pedestrian); - CloseWindow(); - if (client->xvfb_pid > 0) { - kill(client->xvfb_pid, SIGTERM); - waitpid(client->xvfb_pid, NULL, 0); - unlink("/tmp/.X99-lock"); - unsetenv("DISPLAY"); - } - - free(client); -} +#include "render.h" diff --git a/pufferlib/ocean/drive/drive.py b/pufferlib/ocean/drive/drive.py index 571b6dce28..08300e990c 100644 --- a/pufferlib/ocean/drive/drive.py +++ b/pufferlib/ocean/drive/drive.py @@ -1,100 +1,188 @@ +import argparse +from pathlib import Path import numpy as np import gymnasium import json import struct import os import pufferlib -from enum import IntEnum from pufferlib.ocean.drive import binding -from multiprocessing import Pool, cpu_count -from tqdm import tqdm - - -class RenderView(IntEnum): - FULL_SIM_STATE = 0 # Orthographic top-down, fully observable simulator state - BEV_AGENT_OBS = 1 # Orthographic top-down, only show what the selected agent can observe - AGENT_PERSP = 2 # Third-person perspective following selected agent class Drive(pufferlib.PufferEnv): def __init__( self, - render_mode=RenderView.FULL_SIM_STATE, + render_mode=None, report_interval=1, width=1280, height=1024, human_agent_idx=0, - reward_vehicle_collision=-0.1, - reward_offroad_collision=-0.1, reward_goal=1.0, - reward_goal_post_respawn=0.5, - goal_behavior=0, - goal_target_distance=10.0, + reward_vehicle_collision=3.0, + reward_offroad_collision=3.0, + reward_comfort=0.05, + reward_lane_align=0.025, + reward_vel_align=1.0, + reward_lane_center=0.0038, + reward_center_bias=0.0, + reward_velocity=0.0025, + reward_reverse=0.005, + reward_traffic_light_violation=1.0, + reward_timestep=0.000025, + reward_overspeed=0.05, + reward_ade=0.0, + min_waypoint_spacing=20.0, + max_waypoint_spacing=60.0, + num_target_waypoints=3, goal_radius=2.0, - goal_speed=20.0, collision_behavior=0, offroad_behavior=0, + traffic_light_behavior=0, dt=0.1, - episode_length=None, - termination_mode=None, + spawn_initial_speed=0.0, + goal_speed_threshold=3.0, + scenario_length=None, resample_frequency=91, num_maps=100, num_agents=512, + min_agents_per_env=32, + max_agents_per_env=64, action_type="discrete", dynamics_model="classic", + simulation_mode="gigaflow", + termination_mode=0, + inactive_agent_threshold=0.4, buf=None, seed=1, init_steps=0, + eval_mode=0, + num_eval_scenarios=16, init_mode="create_all_valid", control_mode="control_vehicles", - max_controlled_agents=32, - map_dir="resources/drive/binaries/training", + map_dir=None, + target_type="static", + reward_conditioning=False, + reward_randomization=False, + compute_eval_metrics=True, + split_network=False, + use_rear_axle=False, + max_lane_segment_observations=32, + max_boundary_segment_observations=32, + max_partner_observations=16, + max_traffic_light_observations=10, + max_stop_sign_observations=10, + starting_map=0, ): - # env self.dt = dt + self.spawn_initial_speed = float(spawn_initial_speed) + self.goal_speed_threshold = float(goal_speed_threshold) + self.reward_conditioning = reward_conditioning + self.reward_randomization = reward_randomization + self.compute_eval_metrics = compute_eval_metrics + self.split_network = split_network self.render_mode = render_mode self.num_maps = num_maps self.report_interval = report_interval + self.reward_goal = reward_goal self.reward_vehicle_collision = reward_vehicle_collision self.reward_offroad_collision = reward_offroad_collision - self.reward_goal = reward_goal - self.reward_goal_post_respawn = reward_goal_post_respawn + self.reward_comfort = reward_comfort + self.reward_lane_align = reward_lane_align + self.reward_vel_align = reward_vel_align + self.reward_lane_center = reward_lane_center + self.reward_center_bias = reward_center_bias + self.reward_velocity = reward_velocity + self.reward_reverse = reward_reverse + self.reward_traffic_light_violation = reward_traffic_light_violation + self.reward_timestep = reward_timestep + self.reward_overspeed = reward_overspeed + self.reward_ade = reward_ade self.goal_radius = goal_radius - self.goal_speed = goal_speed - self.goal_behavior = goal_behavior - self.goal_target_distance = goal_target_distance + self.min_waypoint_spacing = min_waypoint_spacing + self.max_waypoint_spacing = max_waypoint_spacing + if num_target_waypoints > binding.MAX_TARGET_WAYPOINTS: + num_target_waypoints = binding.MAX_TARGET_WAYPOINTS + self.num_target_waypoints = num_target_waypoints + self.target_type_str = target_type + if target_type == "static": + self.target_type = binding.TARGET_STATIC + elif target_type == "dynamic": + self.target_type = binding.TARGET_DYNAMIC + else: + raise ValueError(f"target_type must be 'static' or 'dynamic'. Got: {target_type}") self.collision_behavior = collision_behavior self.offroad_behavior = offroad_behavior + self.traffic_light_behavior = traffic_light_behavior self.human_agent_idx = human_agent_idx - self.episode_length = episode_length - self.termination_mode = termination_mode + self.scenario_length = scenario_length self.resample_frequency = resample_frequency self.dynamics_model = dynamics_model - self.max_controlled_agents = max_controlled_agents - - # Observation space calculation - self.ego_features = {"classic": binding.EGO_FEATURES_CLASSIC, "jerk": binding.EGO_FEATURES_JERK}.get( - dynamics_model - ) - + if dynamics_model == "classic": + self.dynamics_model_flag = 0 + elif dynamics_model == "jerk": + self.dynamics_model_flag = 1 + else: + raise ValueError(f"dynamics_model must be 'classic' or 'jerk'. Got: {dynamics_model}") + self.eval_mode = eval_mode + self.num_eval_scenarios = num_eval_scenarios + self.termination_mode = termination_mode + self.inactive_agent_threshold = inactive_agent_threshold + self.rng = np.random.default_rng(seed) + self.min_agents_per_env = min_agents_per_env + self.max_agents_per_env = max_agents_per_env + + # Observation space calculation based on target_type + self.ego_features = { + "classic": binding.EGO_FEATURES_CLASSIC, + "jerk": binding.EGO_FEATURES_JERK, + }.get(dynamics_model) + + self.use_rear_axle = use_rear_axle # Extract observation shapes from constants - # These need to be defined in C, since they determine the shape of the arrays - self.max_road_objects = binding.MAX_ROAD_SEGMENT_OBSERVATIONS - self.max_partner_objects = binding.MAX_AGENTS - 1 + self.max_lane_segment_observations = max_lane_segment_observations + self.max_boundary_segment_observations = max_boundary_segment_observations + self.max_partner_observations = max_partner_observations + self.max_traffic_light_observations = max_traffic_light_observations + self.max_stop_sign_observations = max_stop_sign_observations self.partner_features = binding.PARTNER_FEATURES self.road_features = binding.ROAD_FEATURES + self.traffic_light_features = binding.TRAFFIC_LIGHT_FEATURES + self.stop_sign_features = binding.STOP_SIGN_FEATURES + self.num_reward_coefs = binding.NUM_REWARD_COEFS if reward_conditioning else 0 + + # Target features based on target_type + if target_type == "static": + self.target_features = binding.STATIC_TARGET_FEATURES + else: + self.target_features = binding.DYNAMIC_TARGET_FEATURES + self.target_dim = num_target_waypoints * self.target_features self.num_obs = ( self.ego_features - + self.max_partner_objects * self.partner_features - + self.max_road_objects * self.road_features + + self.num_reward_coefs + + self.target_dim + + self.max_partner_observations * self.partner_features + + self.max_lane_segment_observations * self.road_features + + self.max_boundary_segment_observations * self.road_features + + self.max_traffic_light_observations * self.traffic_light_features + + self.max_stop_sign_observations * self.stop_sign_features ) + self.single_observation_space = gymnasium.spaces.Box(low=-1, high=1, shape=(self.num_obs,), dtype=np.float32) self.init_steps = init_steps self.init_mode_str = init_mode self.control_mode_str = control_mode + self.simulation_mode_str = simulation_mode self.map_dir = map_dir + self.map_files = sorted(os.path.join(map_dir, f) for f in os.listdir(map_dir) if f.endswith(".bin")) + + if self.simulation_mode_str == "gigaflow": + self.simulation_mode = 0 + elif self.simulation_mode_str == "replay": + self.simulation_mode = 1 + else: + raise ValueError(f"simulation_mode must be one of 'gigaflow' or 'replay'. Got: {self.simulation_mode_str}") if self.control_mode_str == "control_vehicles": self.control_mode = 0 @@ -104,11 +192,9 @@ def __init__( self.control_mode = 2 elif self.control_mode_str == "control_sdc_only": self.control_mode = 3 - elif self.control_mode_str == "control_mixed_play": - self.control_mode = 4 else: raise ValueError( - f"control_mode must be one of 'control_vehicles', 'control_wosac', 'control_agents' or 'control_mixed_play'. Got: {self.control_mode_str}" + f"control_mode must be one of 'control_vehicles', 'control_wosac', or 'control_agents'. Got: {self.control_mode_str}" ) if self.init_mode_str == "create_all_valid": self.init_mode = 0 @@ -120,6 +206,7 @@ def __init__( ) if action_type == "discrete": + self._action_type_flag = 0 if dynamics_model == "classic": # Joint action space (assume dependence) self.single_action_space = gymnasium.spaces.MultiDiscrete([7 * 13]) @@ -131,45 +218,57 @@ def __init__( else: raise ValueError(f"dynamics_model must be 'classic' or 'jerk'. Got: {dynamics_model}") elif action_type == "continuous": + self._action_type_flag = 1 self.single_action_space = gymnasium.spaces.Box(low=-1, high=1, shape=(2,), dtype=np.float32) else: raise ValueError(f"action_space must be 'discrete' or 'continuous'. Got: {action_type}") - self._action_type_flag = 0 if action_type == "discrete" else 1 - # Check if resources directory exists - binary_path = f"{map_dir}/map_000.bin" - if not os.path.exists(binary_path): + if not self.map_files: raise FileNotFoundError( - f"Required directory {binary_path} not found. Please ensure the Drive maps are downloaded and installed correctly per docs." + f"No .bin files found in {map_dir}. Please ensure the Drive maps are downloaded and installed correctly per docs." ) # Check maps availability - available_maps = len([name for name in os.listdir(map_dir) if name.endswith(".bin")]) + available_maps = len(self.map_files) if num_maps > available_maps: - raise ValueError( - f"num_maps ({num_maps}) exceeds available maps in directory ({available_maps}). Please reduce num_maps or add more maps to resources/drive/binaries." + raise ValueError(f"num_maps ({num_maps}) exceeds available maps in {map_dir} ({available_maps}).") + self.starting_map_counter = starting_map + self.starting_map_counter_init = starting_map + + # Calculate dynamic batch size for Eval + Replay mode + self.current_num_eval_scenarios = self.num_eval_scenarios + if self.eval_mode: + self.current_num_eval_scenarios = min( + self.num_eval_scenarios, + self.num_eval_scenarios + self.starting_map_counter_init - self.starting_map_counter, ) # Iterate through all maps to count total agents that can be initialized for each map agent_offsets, map_ids, num_envs = binding.shared( - map_dir=map_dir, + map_files=self.map_files, num_agents=num_agents, num_maps=num_maps, + starting_map_counter=self.starting_map_counter, + eval_mode=self.eval_mode, init_mode=self.init_mode, control_mode=self.control_mode, + simulation_mode=self.simulation_mode, init_steps=self.init_steps, - goal_behavior=self.goal_behavior, - goal_target_distance=self.goal_target_distance, - max_controlled_agents=self.max_controlled_agents, + seed=self.random_seed, + min_agents_per_env=self.min_agents_per_env, + max_agents_per_env=self.max_agents_per_env, + num_eval_scenarios=self.current_num_eval_scenarios, # Use the dynamic size here ) + # In eval mode, don't wrap counter - allows termination condition to work correctly + self.starting_map_counter = self.starting_map_counter + num_envs - self.num_agents = agent_offsets[-1] + self.num_agents = num_agents self.agent_offsets = agent_offsets self.map_ids = map_ids self.num_envs = num_envs super().__init__(buf=buf) - self.env_ids = [] + env_ids = [] for i in range(num_envs): cur = agent_offsets[i] nxt = agent_offsets[i + 1] @@ -179,125 +278,183 @@ def __init__( self.rewards[cur:nxt], self.terminals[cur:nxt], self.truncations[cur:nxt], - seed, - action_type=self._action_type_flag, - human_agent_idx=human_agent_idx, - reward_vehicle_collision=reward_vehicle_collision, - reward_offroad_collision=reward_offroad_collision, - reward_goal=reward_goal, - reward_goal_post_respawn=reward_goal_post_respawn, - goal_radius=goal_radius, - goal_speed=goal_speed, - goal_behavior=self.goal_behavior, - goal_target_distance=self.goal_target_distance, - collision_behavior=self.collision_behavior, - offroad_behavior=self.offroad_behavior, - dt=dt, - episode_length=(int(episode_length) if episode_length is not None else None), - termination_mode=(int(self.termination_mode) if self.termination_mode is not None else 0), - map_id=map_ids[i], - max_agents=nxt - cur, - ini_file="pufferlib/config/ocean/drive.ini", - init_steps=init_steps, - init_mode=self.init_mode, - control_mode=self.control_mode, - map_dir=map_dir, - max_controlled_agents=self.max_controlled_agents, - render_mode=render_mode, - ) - self.env_ids.append(env_id) - - self.c_envs = binding.vectorize(*self.env_ids) - - def reset(self, seed=0): - binding.vec_reset(self.c_envs, seed) - self.tick = 0 - self.truncations[:] = 0 - return self.observations, [] - - def resample_maps(self): - """Resample environment maps.""" - self.tick = 0 - binding.vec_close(self.c_envs) - agent_offsets, map_ids, num_envs = binding.shared( - num_agents=self.num_agents, - num_maps=self.num_maps, - init_mode=self.init_mode, - control_mode=self.control_mode, - init_steps=self.init_steps, - goal_behavior=self.goal_behavior, - goal_target_distance=self.goal_target_distance, - goal_speed=self.goal_speed, - map_dir=self.map_dir, - max_controlled_agents=self.max_controlled_agents, - ) - self.agent_offsets = agent_offsets - self.map_ids = map_ids - self.num_envs = num_envs - self.env_ids = [] - seed = np.random.randint(0, 2**32 - 1) - for i in range(num_envs): - cur = agent_offsets[i] - nxt = agent_offsets[i + 1] - env_id = binding.env_init( - self.observations[cur:nxt], - self.actions[cur:nxt], - self.rewards[cur:nxt], - self.terminals[cur:nxt], - self.truncations[cur:nxt], - seed, + self.masks[cur:nxt], + self.random_seed, action_type=self._action_type_flag, + dynamics_model=self.dynamics_model_flag, human_agent_idx=self.human_agent_idx, + reward_goal=self.reward_goal, reward_vehicle_collision=self.reward_vehicle_collision, reward_offroad_collision=self.reward_offroad_collision, - reward_goal=self.reward_goal, - reward_goal_post_respawn=self.reward_goal_post_respawn, - goal_radius=self.goal_radius, - goal_behavior=self.goal_behavior, - goal_target_distance=self.goal_target_distance, - goal_speed=self.goal_speed, + reward_comfort=self.reward_comfort, + reward_lane_align=self.reward_lane_align, + reward_vel_align=self.reward_vel_align, + reward_lane_center=self.reward_lane_center, + reward_center_bias=self.reward_center_bias, + reward_velocity=self.reward_velocity, + reward_reverse=self.reward_reverse, + reward_traffic_light_violation=self.reward_traffic_light_violation, + reward_timestep=self.reward_timestep, + reward_overspeed=self.reward_overspeed, + reward_ade=self.reward_ade, collision_behavior=self.collision_behavior, offroad_behavior=self.offroad_behavior, + traffic_light_behavior=self.traffic_light_behavior, + goal_radius=self.goal_radius, + min_waypoint_spacing=self.min_waypoint_spacing, + max_waypoint_spacing=self.max_waypoint_spacing, + num_target_waypoints=self.num_target_waypoints, + target_type=self.target_type, + use_rear_axle=self.use_rear_axle, + max_lane_segment_observations=self.max_lane_segment_observations, + max_boundary_segment_observations=self.max_boundary_segment_observations, + max_partner_observations=self.max_partner_observations, + max_traffic_light_observations=self.max_traffic_light_observations, + max_stop_sign_observations=self.max_stop_sign_observations, dt=self.dt, - episode_length=(int(self.episode_length) if self.episode_length is not None else None), - map_id=map_ids[i], + spawn_initial_speed=self.spawn_initial_speed, + goal_speed_threshold=self.goal_speed_threshold, + scenario_length=(int(self.scenario_length) if self.scenario_length is not None else None), + termination_mode=int(self.termination_mode), + inactive_agent_threshold=float(self.inactive_agent_threshold), + map_file=self.map_files[map_ids[i]], max_agents=nxt - cur, + max_agents_per_env=self.max_agents_per_env, ini_file="pufferlib/config/ocean/drive.ini", init_steps=self.init_steps, init_mode=self.init_mode, control_mode=self.control_mode, - map_dir=self.map_dir, - termination_mode=(int(self.termination_mode) if self.termination_mode is not None else 0), - max_controlled_agents=self.max_controlled_agents, - render_mode=self.render_mode, + simulation_mode=self.simulation_mode, + reward_conditioning=self.reward_conditioning, + reward_randomization=self.reward_randomization, + compute_eval_metrics=self.compute_eval_metrics, + eval_mode=self.eval_mode, ) - self.env_ids.append(env_id) - self.c_envs = binding.vectorize(*self.env_ids) + env_ids.append(env_id) - binding.vec_reset(self.c_envs, seed) - self.terminals[:] = 1 - self.truncations[:] = 1 + self.c_envs = binding.vectorize(*env_ids) + binding.vec_reset(self.c_envs, self.random_seed) - def step(self, actions, per_env_logs=False): - self.terminals[:] = 0 + @property + def random_seed(self): + return int(self.rng.integers(0, 2**24)) + + def reset(self, seed=0): + binding.vec_reset(self.c_envs, seed) + self.tick = 0 self.truncations[:] = 0 + return self.observations, [] + + def step(self, actions): self.actions[:] = actions binding.vec_step(self.c_envs) self.tick += 1 info = [] if self.tick % self.report_interval == 0: - if per_env_logs: # Get the stats for every separate env - logs = self.get_env_logs() - if any(logs): - info = logs - else: # Default: Aggregate across vectorized envs - log = binding.vec_log(self.c_envs, self.num_agents) - if log: - info.append(log) - + log = binding.vec_log(self.c_envs, self.num_agents) + if log: + info.append(log) + # print(log) if self.tick > 0 and self.resample_frequency > 0 and self.tick % self.resample_frequency == 0: - self.resample_maps() + self.tick = 0 + will_resample = 1 + if will_resample: + # Calculate dynamic batch size for Eval + Replay mode + self.current_num_eval_scenarios = self.num_eval_scenarios + if self.eval_mode: + self.current_num_eval_scenarios = min( + self.num_eval_scenarios, + self.num_eval_scenarios + self.starting_map_counter_init - self.starting_map_counter, + ) + if self.current_num_eval_scenarios == 0: + return (self.observations, self.rewards, self.terminals, self.truncations, info) + binding.vec_close(self.c_envs) + agent_offsets, map_ids, num_envs = binding.shared( + num_agents=self.num_agents, + num_maps=self.num_maps, + starting_map_counter=self.starting_map_counter, + eval_mode=self.eval_mode, + init_mode=self.init_mode, + control_mode=self.control_mode, + simulation_mode=self.simulation_mode, + init_steps=self.init_steps, + map_files=self.map_files, + seed=self.random_seed, + min_agents_per_env=self.min_agents_per_env, + max_agents_per_env=self.max_agents_per_env, + num_eval_scenarios=self.current_num_eval_scenarios, # Use the dynamic size here + ) + # In eval mode, don't wrap counter - allows termination condition to work correctly + self.starting_map_counter = self.starting_map_counter + num_envs + env_ids = [] + for i in range(num_envs): + cur = agent_offsets[i] + nxt = agent_offsets[i + 1] + env_id = binding.env_init( + self.observations[cur:nxt], + self.actions[cur:nxt], + self.rewards[cur:nxt], + self.terminals[cur:nxt], + self.truncations[cur:nxt], + self.masks[cur:nxt], + self.random_seed, + action_type=self._action_type_flag, + dynamics_model=self.dynamics_model_flag, + human_agent_idx=self.human_agent_idx, + reward_goal=self.reward_goal, + reward_vehicle_collision=self.reward_vehicle_collision, + reward_offroad_collision=self.reward_offroad_collision, + reward_comfort=self.reward_comfort, + reward_lane_align=self.reward_lane_align, + reward_vel_align=self.reward_vel_align, + reward_lane_center=self.reward_lane_center, + reward_center_bias=self.reward_center_bias, + reward_velocity=self.reward_velocity, + reward_reverse=self.reward_reverse, + reward_traffic_light_violation=self.reward_traffic_light_violation, + reward_timestep=self.reward_timestep, + reward_overspeed=self.reward_overspeed, + reward_ade=self.reward_ade, + collision_behavior=self.collision_behavior, + offroad_behavior=self.offroad_behavior, + traffic_light_behavior=self.traffic_light_behavior, + goal_radius=self.goal_radius, + min_waypoint_spacing=self.min_waypoint_spacing, + max_waypoint_spacing=self.max_waypoint_spacing, + num_target_waypoints=self.num_target_waypoints, + target_type=self.target_type, + use_rear_axle=self.use_rear_axle, + max_lane_segment_observations=self.max_lane_segment_observations, + max_boundary_segment_observations=self.max_boundary_segment_observations, + max_partner_observations=self.max_partner_observations, + max_traffic_light_observations=self.max_traffic_light_observations, + max_stop_sign_observations=self.max_stop_sign_observations, + dt=self.dt, + spawn_initial_speed=self.spawn_initial_speed, + goal_speed_threshold=self.goal_speed_threshold, + scenario_length=(int(self.scenario_length) if self.scenario_length is not None else None), + termination_mode=int(self.termination_mode), + inactive_agent_threshold=float(self.inactive_agent_threshold), + map_file=self.map_files[map_ids[i]], + max_agents=nxt - cur, + max_agents_per_env=self.max_agents_per_env, + ini_file="pufferlib/config/ocean/drive.ini", + init_steps=self.init_steps, + init_mode=self.init_mode, + control_mode=self.control_mode, + simulation_mode=self.simulation_mode, + reward_conditioning=self.reward_conditioning, + reward_randomization=self.reward_randomization, + compute_eval_metrics=self.compute_eval_metrics, + eval_mode=self.eval_mode, + ) + env_ids.append(env_id) + self.c_envs = binding.vectorize(*env_ids) + + binding.vec_reset(self.c_envs, self.random_seed) + # Map resampling is an external reset boundary (dataset/map switch). Treat as truncation. + self.truncations[:] = 1 return (self.observations, self.rewards, self.terminals, self.truncations, info) def get_global_agent_state(self): @@ -341,15 +498,13 @@ def get_ground_truth_trajectories(self): num_agents = self.num_agents trajectories = { - "x": np.zeros((num_agents, self.episode_length - self.init_steps), dtype=np.float32), - "y": np.zeros((num_agents, self.episode_length - self.init_steps), dtype=np.float32), - "z": np.zeros((num_agents, self.episode_length - self.init_steps), dtype=np.float32), - "heading": np.zeros((num_agents, self.episode_length - self.init_steps), dtype=np.float32), - "valid": np.zeros((num_agents, self.episode_length - self.init_steps), dtype=np.int32), + "x": np.zeros((num_agents, self.scenario_length - self.init_steps), dtype=np.float32), + "y": np.zeros((num_agents, self.scenario_length - self.init_steps), dtype=np.float32), + "z": np.zeros((num_agents, self.scenario_length - self.init_steps), dtype=np.float32), + "heading": np.zeros((num_agents, self.scenario_length - self.init_steps), dtype=np.float32), + "valid": np.zeros((num_agents, self.scenario_length - self.init_steps), dtype=np.int32), "id": np.zeros(num_agents, dtype=np.int32), - "is_vehicle": np.zeros(num_agents, dtype=bool), - "is_track_to_predict": np.zeros(num_agents, dtype=bool), - "scenario_id": np.zeros(num_agents, dtype="S16"), + "scenario_id": np.zeros(num_agents, dtype=np.int32), } binding.vec_get_global_ground_truth_trajectories( @@ -360,16 +515,12 @@ def get_ground_truth_trajectories(self): trajectories["heading"], trajectories["valid"], trajectories["id"], - trajectories["is_vehicle"], - trajectories["is_track_to_predict"], trajectories["scenario_id"], ) for key in trajectories: trajectories[key] = trajectories[key][:, None] - trajectories["scenario_id"] = trajectories["scenario_id"].astype(str) - return trajectories def get_road_edge_polylines(self): @@ -385,7 +536,7 @@ def get_road_edge_polylines(self): "x": np.zeros(total_points, dtype=np.float32), "y": np.zeros(total_points, dtype=np.float32), "lengths": np.zeros(num_polylines, dtype=np.int32), - "scenario_id": np.zeros(num_polylines, dtype="S16"), + "scenario_id": np.zeros(num_polylines, dtype=np.int32), } binding.vec_get_road_edge_polylines( @@ -396,29 +547,19 @@ def get_road_edge_polylines(self): polylines["scenario_id"], ) - polylines["scenario_id"] = polylines["scenario_id"].astype(str) - return polylines - def render(self, view_mode: RenderView = RenderView.FULL_SIM_STATE, draw_traces: bool = True, env_id: int = 0): - binding.vec_render(self.c_envs, int(view_mode), draw_traces, env_id) + def render(self): + binding.vec_render(self.c_envs, 0) def close(self): binding.vec_close(self.c_envs) - def env_log(self, env_idx): - """Get log statistics for a single environment.""" - num_agents = self.agent_offsets[env_idx + 1] - self.agent_offsets[env_idx] - return binding.env_log(self.env_ids[env_idx], num_agents) - - def get_env_logs(self): - """Get log statistics for all environments (unaggregated).""" - return [self.env_log(i) for i in range(self.num_envs)] - - @property - def scenario_ids(self) -> list[str]: - """Return scenario ID string for each env, stripping null padding.""" - return [s.rstrip("\x00") for s in binding.vec_get_scenario_ids(self.c_envs)] + def get_state(self): + try: + return binding.vec_get(self.c_envs) + except Exception: + return binding.env_get(self.c_envs) def calculate_area(p1, p2, p3): @@ -426,13 +567,7 @@ def calculate_area(p1, p2, p3): return 0.5 * abs((p1["x"] - p3["x"]) * (p2["y"] - p1["y"]) - (p1["x"] - p2["x"]) * (p3["y"] - p1["y"])) -def dist(a, b): - dx = a["x"] - b["x"] - dy = a["y"] - b["y"] - return dx * dx + dy * dy - - -def simplify_polyline(geometry, polyline_reduction_threshold, max_segment_length): +def simplify_polyline(geometry, polyline_reduction_threshold): """Simplify the given polyline using a method inspired by Visvalingham-Whyatt, optimized for Python.""" num_points = len(geometry) if num_points < 3: @@ -461,7 +596,8 @@ def simplify_polyline(geometry, polyline_reduction_threshold, max_segment_length point2 = geometry[k_1] point3 = geometry[k_2] area = calculate_area(point1, point2, point3) - if area < polyline_reduction_threshold and dist(point1, point3) <= max_segment_length: + + if area < polyline_reduction_threshold: skip[k_1] = True skip_changed = True k = k_2 @@ -471,29 +607,13 @@ def simplify_polyline(geometry, polyline_reduction_threshold, max_segment_length return [geometry[i] for i in range(num_points) if not skip[i]] -def save_map_binary(map_data, output_file, unique_map_id): +def save_map_binary(map_data, output_file): trajectory_length = 91 """Saves map data in a binary format readable by C""" with open(output_file, "wb") as f: - # Get metadata - metadata = map_data.get("metadata", {}) - sdc_track_index = metadata.get("sdc_track_index", -1) # -1 as default if not found - tracks_to_predict = metadata.get("tracks_to_predict", []) - - # Write original scenario_id with fallback to placeholder - scenario_id = map_data.get("scenario_id", f"map_{unique_map_id:03d}") - f.write(struct.pack("16s", scenario_id.encode("utf-8"))) - - # Write sdc_track_index - f.write(struct.pack("i", sdc_track_index)) - - # Write tracks_to_predict info (indices only) - f.write(struct.pack("i", len(tracks_to_predict))) - for track in tracks_to_predict: - track_index = track.get("track_index", -1) - f.write(struct.pack("i", track_index)) - # Count total entities + print(len(map_data.get("objects", []))) + print(len(map_data.get("roads", []))) num_objects = len(map_data.get("objects", [])) num_roads = len(map_data.get("roads", [])) # num_entities = num_objects + num_roads @@ -502,9 +622,6 @@ def save_map_binary(map_data, output_file, unique_map_id): # f.write(struct.pack('i', num_entities)) # Write objects for obj in map_data.get("objects", []): - # Write unique map id - f.write(struct.pack("i", unique_map_id)) - # Write base entity data obj_type = obj.get("type", 1) if obj_type == "vehicle": @@ -514,7 +631,7 @@ def save_map_binary(map_data, output_file, unique_map_id): elif obj_type == "cyclist": obj_type = 3 f.write(struct.pack("i", obj_type)) # type - f.write(struct.pack("i", obj.get("id", 0))) # id + # f.write(struct.pack("i", obj.get("id", 0))) # id f.write(struct.pack("i", trajectory_length)) # array_size # Write position arrays positions = obj.get("position", []) @@ -564,8 +681,6 @@ def save_map_binary(map_data, output_file, unique_map_id): # Write roads for idx, road in enumerate(map_data.get("roads", [])): - f.write(struct.pack("i", unique_map_id)) - geometry = road.get("geometry", []) road_type = road.get("map_element_id", 0) road_type_word = road.get("type", 0) @@ -575,7 +690,7 @@ def save_map_binary(map_data, output_file, unique_map_id): road_type = 15 # breakpoint() if len(geometry) > 10 and road_type <= 16: - geometry = simplify_polyline(geometry, 0.1, 250) + geometry = simplify_polyline(geometry, 0.1) size = len(geometry) # breakpoint() if road_type >= 0 and road_type <= 3: @@ -594,14 +709,13 @@ def save_map_binary(map_data, output_file, unique_map_id): road_type = 10 # Write base entity data f.write(struct.pack("i", road_type)) # type - f.write(struct.pack("i", road.get("id", 0))) # id + # f.write(struct.pack("i", road.get("id", 0))) # id f.write(struct.pack("i", size)) # array_size # Write position arrays for coord in ["x", "y", "z"]: for point in geometry: f.write(struct.pack("f", float(point.get(coord, 0.0)))) - # Write scalar fields f.write(struct.pack("f", float(road.get("width", 0.0)))) f.write(struct.pack("f", float(road.get("length", 0.0)))) @@ -613,92 +727,80 @@ def save_map_binary(map_data, output_file, unique_map_id): f.write(struct.pack("i", road.get("mark_as_expert", 0))) -def load_map(map_name, unique_map_id, binary_output=None): +def load_map(map_name, binary_output=None): """Loads a JSON map and optionally saves it as binary""" with open(map_name, "r") as f: map_data = json.load(f) if binary_output: - save_map_binary(map_data, binary_output, unique_map_id) - + save_map_binary(map_data, binary_output) -def _process_single_map(args): - """Worker function to process a single map file""" - i, map_path, binary_path = args - try: - load_map(str(map_path), i, str(binary_path)) - return (i, map_path.name, True, None) - except Exception as e: - return (i, map_path.name, False, str(e)) - - -def process_all_maps( - data_folder="data/processed/training", - max_maps=50_000, - num_workers=None, -): - """Process all maps and save them as binaries using multiprocessing - - Args: - data_folder: Path to the folder containing JSON map files - max_maps: Maximum number of maps to process - num_workers: Number of parallel workers (defaults to cpu_count()) - """ - from pathlib import Path - - if num_workers is None: - num_workers = cpu_count() - - # Path to the training data - data_dir = Path(data_folder) - dataset_name = data_dir.name +def process_all_maps(dataset_path: str, max_file_to_process: int = 1000): + """Process all maps from a local path (or GCS) and save them as binaries.""" # Create the binaries directory if it doesn't exist - binary_dir = Path(f"resources/drive/binaries/{dataset_name}") + binary_dir = Path("pufferlib/resources/drive/binaries") binary_dir.mkdir(parents=True, exist_ok=True) - # Get all JSON files in the training directory - json_files = sorted(data_dir.glob("*.json")) - - # Prepare arguments for parallel processing - tasks = [] - for i, map_path in enumerate(json_files[:max_maps]): - binary_file = f"map_{i:03d}.bin" - binary_path = binary_dir / binary_file - tasks.append((i, map_path, binary_path)) - - # Process maps in parallel with progress bar - with Pool(num_workers) as pool: - results = list( - tqdm(pool.imap(_process_single_map, tasks), total=len(tasks), desc="Processing maps", unit="map") - ) - - # Collect statistics - successful = sum(1 for _, _, success, _ in results if success) - failed = sum(1 for _, _, success, _ in results if not success) - - if failed > 0: - print(f"\nFailed {failed}/{len(results)} files:") - for i, name, success, error in results: - if not success: - print(f" {name}: {error}") + # --- GCS FUSE --- + if dataset_path.startswith("gs://") and os.path.exists("/gcs/"): + print("Vertex AI GCS FUSE mount detected. Translating GCS URI to local path.") + dataset_path = dataset_path.replace("gs://", "/gcs/") + print(f"Using mounted dataset path: {dataset_path}") + + file_iterator = None + fs = None # Will hold the gcsfs filesystem object if needed + + path = Path(dataset_path) + print(f"Searching for JSON map files in local path: {path.resolve()}") + # Use rglob for recursive globbing to match the GCS '**' behavior + file_iterator = sorted(path.rglob("*.json")) + print(f"Found {len(file_iterator)} JSON files locally.") + + file_count = 0 + # Process each JSON file from the appropriate source + for i, item in enumerate(file_iterator): + if i >= max_file_to_process: + print(f"Reached file limit of {max_file_to_process}.") + break + + map_path_str = "" + try: + # if is_gcs_stream: + # # item is a path string from gcsfs.glob, e.g., "my-bucket/path/file.json" + # map_path_str = f"gs://{item}" + # # Use 'with' to ensure the stream is automatically closed + # with fs.open(item, "rt", encoding="utf-8") as stream: + # map_data = json.load(stream) + # else: + # item is a Path object from Path.rglob + map_path_str = str(item) + # Use 'with' for local files too (good practice) + with open(map_path_str, "r") as f: + map_data = json.load(f) + + map_name = Path(map_path_str).name + binary_file = f"map_{i:03d}.bin" + binary_path = binary_dir / binary_file + + print(f"Processing {map_name} -> {binary_file}") + save_map_binary(map_data, str(binary_path)) + file_count += 1 + + except Exception as e: + print(f"Error processing {map_path_str}: {e}") + continue + + print(f"Found and processed {file_count} JSON files.") def test_performance(timeout=10, atn_cache=1024, num_agents=1024): import time - env = Drive( - num_agents=num_agents, - num_maps=1, - control_mode="control_vehicles", - init_mode="create_all_valid", - init_steps=0, - episode_length=91, - ) - + env = Drive(num_agents=num_agents) env.reset() - tick = 0 + num_agents = 1024 actions = np.stack( [np.random.randint(0, space.n + 1, (atn_cache, num_agents)) for space in env.single_action_space], axis=-1 ) @@ -710,15 +812,14 @@ def test_performance(timeout=10, atn_cache=1024, num_agents=1024): tick += 1 print(f"SPS: {num_agents * tick / (time.time() - start)}") - env.close() if __name__ == "__main__": # test_performance() - # Process the train dataset - process_all_maps(data_folder="data/processed/training") - # Process the validation/test dataset - # process_all_maps(data_folder="data/processed/validation") - # # Process the validation_interactive dataset - # process_all_maps(data_folder="data/processed/validation_interactive") + parser = argparse.ArgumentParser(description="Process maps for PufferDrive.") + parser.add_argument( + "--data_dir", type=str, default="data/train", help="Path to the directory containing JSON map files." + ) + args = parser.parse_args() + process_all_maps(args.data_dir) diff --git a/pufferlib/ocean/drive/drivenet.h b/pufferlib/ocean/drive/drivenet.h index 5719081bb1..f364db25cb 100644 --- a/pufferlib/ocean/drive/drivenet.h +++ b/pufferlib/ocean/drive/drivenet.h @@ -1,5 +1,4 @@ #include -#include "drive.h" #include "puffernet.h" #include #include @@ -8,16 +7,6 @@ #include #include -#define NN_INPUT_SIZE 64 -#define NN_HIDDEN_SIZE 256 - -// Note: This C neural network implementation is designed specifically for lightweight -// browser demos and is not intended for training purposes. -// -// For development and experimentation, it is recommended to use the render structure -// defined in eval() inside pufferl.py, since it uses the torch net. This makes modifying, -// testing, and iterating on the model easier and less error-prone. - typedef struct DriveNet DriveNet; struct DriveNet { int num_agents; @@ -55,15 +44,10 @@ struct DriveNet { DriveNet *init_drivenet(Weights *weights, int num_agents, int dynamics_model) { DriveNet *net = calloc(1, sizeof(DriveNet)); - // Use constants directly from drive.h - int ego_dim = (dynamics_model == JERK) ? EGO_FEATURES_JERK : EGO_FEATURES_CLASSIC; - int max_partners = MAX_AGENTS - 1; - int max_road_obs = MAX_ROAD_SEGMENT_OBSERVATIONS; - int partner_features = PARTNER_FEATURES; - int road_features = ROAD_FEATURES; - int input_size = NN_INPUT_SIZE; - int hidden_size = NN_HIDDEN_SIZE; - int road_feat_onehot = road_features + 6; // one-hot extra 6 features for road + int hidden_size = 256; + int input_size = 64; + + int ego_dim = (dynamics_model == JERK) ? 10 : 7; // Determine action space size based on dynamics model int action_size, logit_sizes[2]; @@ -81,26 +65,25 @@ DriveNet *init_drivenet(Weights *weights, int num_agents, int dynamics_model) { net->num_agents = num_agents; net->ego_dim = ego_dim; net->obs_self = calloc(num_agents * ego_dim, sizeof(float)); - net->obs_partner = calloc(num_agents * max_partners * partner_features, sizeof(float)); - net->obs_road = calloc(num_agents * max_road_obs * road_feat_onehot, sizeof(float)); - net->partner_linear_output = calloc(num_agents * max_partners * input_size, sizeof(float)); - net->road_linear_output = calloc(num_agents * max_road_obs * input_size, sizeof(float)); - net->partner_linear_output_two = calloc(num_agents * max_partners * input_size, sizeof(float)); - net->road_linear_output_two = calloc(num_agents * max_road_obs * input_size, sizeof(float)); - net->partner_layernorm_output = calloc(num_agents * max_partners * input_size, sizeof(float)); - net->road_layernorm_output = calloc(num_agents * max_road_obs * input_size, sizeof(float)); - + net->obs_partner = calloc(num_agents * 63 * 7, sizeof(float)); + net->obs_road = calloc(num_agents * 200 * 13, sizeof(float)); + net->partner_linear_output = calloc(num_agents * 63 * input_size, sizeof(float)); + net->road_linear_output = calloc(num_agents * 200 * input_size, sizeof(float)); + net->partner_linear_output_two = calloc(num_agents * 63 * input_size, sizeof(float)); + net->road_linear_output_two = calloc(num_agents * 200 * input_size, sizeof(float)); + net->partner_layernorm_output = calloc(num_agents * 63 * input_size, sizeof(float)); + net->road_layernorm_output = calloc(num_agents * 200 * input_size, sizeof(float)); net->ego_encoder = make_linear(weights, num_agents, ego_dim, input_size); net->ego_layernorm = make_layernorm(weights, num_agents, input_size); net->ego_encoder_two = make_linear(weights, num_agents, input_size, input_size); - net->road_encoder = make_linear(weights, num_agents, road_feat_onehot, input_size); + net->road_encoder = make_linear(weights, num_agents, 13, input_size); net->road_layernorm = make_layernorm(weights, num_agents, input_size); net->road_encoder_two = make_linear(weights, num_agents, input_size, input_size); - net->partner_encoder = make_linear(weights, num_agents, partner_features, input_size); + net->partner_encoder = make_linear(weights, num_agents, 7, input_size); net->partner_layernorm = make_layernorm(weights, num_agents, input_size); net->partner_encoder_two = make_linear(weights, num_agents, input_size, input_size); - net->partner_max = make_max_dim1(num_agents, max_partners, input_size); - net->road_max = make_max_dim1(num_agents, max_road_obs, input_size); + net->partner_max = make_max_dim1(num_agents, 63, input_size); + net->road_max = make_max_dim1(num_agents, 200, input_size); net->cat1 = make_cat_dim1(num_agents, input_size, input_size); net->cat2 = make_cat_dim1(num_agents, input_size + input_size, input_size); net->gelu = make_gelu(num_agents, 3 * input_size); @@ -108,9 +91,9 @@ DriveNet *init_drivenet(Weights *weights, int num_agents, int dynamics_model) { net->relu = make_relu(num_agents, hidden_size); net->actor = make_linear(weights, num_agents, hidden_size, action_size); net->value_fn = make_linear(weights, num_agents, hidden_size, 1); - net->lstm = make_lstm(weights, num_agents, hidden_size, NN_HIDDEN_SIZE); - memset(net->lstm->state_h, 0, num_agents * NN_HIDDEN_SIZE * sizeof(float)); - memset(net->lstm->state_c, 0, num_agents * NN_HIDDEN_SIZE * sizeof(float)); + net->lstm = make_lstm(weights, num_agents, hidden_size, 256); + memset(net->lstm->state_h, 0, num_agents * 256 * sizeof(float)); + memset(net->lstm->state_c, 0, num_agents * 256 * sizeof(float)); net->multidiscrete = make_multidiscrete(num_agents, logit_sizes, action_dim); return net; } @@ -150,21 +133,16 @@ void free_drivenet(DriveNet *net) { void forward(DriveNet *net, float *observations, int *actions) { int ego_dim = net->ego_dim; - int max_partners = MAX_AGENTS - 1; - int max_road_obs = MAX_ROAD_SEGMENT_OBSERVATIONS; - int partner_features = PARTNER_FEATURES; - int road_features = ROAD_FEATURES; - int road_feat_onehot = road_features + 6; // one-hot extra 6 features for road // Clear previous observations memset(net->obs_self, 0, net->num_agents * ego_dim * sizeof(float)); - memset(net->obs_partner, 0, net->num_agents * max_partners * partner_features * sizeof(float)); - memset(net->obs_road, 0, net->num_agents * max_road_obs * road_feat_onehot * sizeof(float)); + memset(net->obs_partner, 0, net->num_agents * 63 * 7 * sizeof(float)); + memset(net->obs_road, 0, net->num_agents * 200 * 13 * sizeof(float)); for (int b = 0; b < net->num_agents; b++) { - int b_offset = b * (ego_dim + max_partners * partner_features + max_road_obs * road_features); + int b_offset = b * (ego_dim + 63 * 7 + 200 * 7); int partner_offset = b_offset + ego_dim; - int road_offset = b_offset + ego_dim + max_partners * partner_features; + int road_offset = b_offset + ego_dim + 63 * 7; // Process self observation for (int i = 0; i < ego_dim; i++) { @@ -172,26 +150,22 @@ void forward(DriveNet *net, float *observations, int *actions) { } // Process partner observation - for (int i = 0; i < max_partners; i++) { - for (int j = 0; j < partner_features; j++) { - net->obs_partner[b * max_partners * partner_features + i * partner_features + j] = - observations[partner_offset + i * partner_features + j]; + for (int i = 0; i < 63; i++) { + for (int j = 0; j < 7; j++) { + net->obs_partner[b * 63 * 7 + i * 7 + j] = observations[partner_offset + i * 7 + j]; } } // Process road observation - for (int i = 0; i < MAX_ROAD_SEGMENT_OBSERVATIONS; i++) { + for (int i = 0; i < 200; i++) { for (int j = 0; j < 7; j++) { - net->obs_road[b * MAX_ROAD_SEGMENT_OBSERVATIONS * ROAD_FEATURES_ONEHOT + i * ROAD_FEATURES_ONEHOT + j] = - observations[road_offset + i * 7 + j]; + net->obs_road[b * 200 * 13 + i * 13 + j] = observations[road_offset + i * 7 + j]; } for (int j = 0; j < 7; j++) { if (j == observations[road_offset + i * 7 + 6]) { - net->obs_road[b * MAX_ROAD_SEGMENT_OBSERVATIONS * ROAD_FEATURES_ONEHOT + i * ROAD_FEATURES_ONEHOT + - 6 + j] = 1.0f; + net->obs_road[b * 200 * 13 + i * 13 + 6 + j] = 1.0f; } else { - net->obs_road[b * MAX_ROAD_SEGMENT_OBSERVATIONS * ROAD_FEATURES_ONEHOT + i * ROAD_FEATURES_ONEHOT + - 6 + j] = 0.0f; + net->obs_road[b * 200 * 13 + i * 13 + 6 + j] = 0.0f; } } } @@ -202,63 +176,56 @@ void forward(DriveNet *net, float *observations, int *actions) { layernorm(net->ego_layernorm, net->ego_encoder->output); linear(net->ego_encoder_two, net->ego_layernorm->output); for (int b = 0; b < net->num_agents; b++) { - for (int obj = 0; obj < max_partners; obj++) { + for (int obj = 0; obj < 63; obj++) { // Get the 7 features for this object - float *obj_features = &net->obs_partner[b * max_partners * partner_features + obj * partner_features]; + float *obj_features = &net->obs_partner[b * 63 * 7 + obj * 7]; // Apply linear layer to this object _linear(obj_features, net->partner_encoder->weights, net->partner_encoder->bias, - &net->partner_linear_output[b * max_partners * NN_INPUT_SIZE + obj * NN_INPUT_SIZE], 1, - partner_features, NN_INPUT_SIZE); + &net->partner_linear_output[b * 63 * 64 + obj * 64], 1, 7, 64); } } for (int b = 0; b < net->num_agents; b++) { - for (int obj = 0; obj < max_partners; obj++) { - float *after_first = &net->partner_linear_output[b * max_partners * NN_INPUT_SIZE + obj * NN_INPUT_SIZE]; + for (int obj = 0; obj < 63; obj++) { + float *after_first = &net->partner_linear_output[b * 63 * 64 + obj * 64]; _layernorm(after_first, net->partner_layernorm->weights, net->partner_layernorm->bias, - &net->partner_layernorm_output[b * max_partners * NN_INPUT_SIZE + obj * NN_INPUT_SIZE], 1, - NN_INPUT_SIZE); + &net->partner_layernorm_output[b * 63 * 64 + obj * 64], 1, 64); } } for (int b = 0; b < net->num_agents; b++) { - for (int obj = 0; obj < max_partners; obj++) { + for (int obj = 0; obj < 63; obj++) { // Get the 7 features for this object - float *obj_features = - &net->partner_layernorm_output[b * max_partners * NN_INPUT_SIZE + obj * NN_INPUT_SIZE]; + float *obj_features = &net->partner_layernorm_output[b * 63 * 64 + obj * 64]; // Apply linear layer to this object _linear(obj_features, net->partner_encoder_two->weights, net->partner_encoder_two->bias, - &net->partner_linear_output_two[b * max_partners * NN_INPUT_SIZE + obj * NN_INPUT_SIZE], 1, - NN_INPUT_SIZE, NN_INPUT_SIZE); + &net->partner_linear_output_two[b * 63 * 64 + obj * 64], 1, 64, 64); } } // Process road objects: apply linear to each object individually for (int b = 0; b < net->num_agents; b++) { - for (int obj = 0; obj < max_road_obs; obj++) { + for (int obj = 0; obj < 200; obj++) { // Get the 13 features for this object - float *obj_features = &net->obs_road[b * max_road_obs * ROAD_FEATURES_ONEHOT + obj * ROAD_FEATURES_ONEHOT]; + float *obj_features = &net->obs_road[b * 200 * 13 + obj * 13]; // Apply linear layer to this object _linear(obj_features, net->road_encoder->weights, net->road_encoder->bias, - &net->road_linear_output[b * max_road_obs * NN_INPUT_SIZE + obj * NN_INPUT_SIZE], 1, - ROAD_FEATURES_ONEHOT, NN_INPUT_SIZE); + &net->road_linear_output[b * 200 * 64 + obj * 64], 1, 13, 64); } } // Apply layer norm and second linear to each road object for (int b = 0; b < net->num_agents; b++) { - for (int obj = 0; obj < max_road_obs; obj++) { - float *after_first = &net->road_linear_output[b * max_road_obs * NN_INPUT_SIZE + obj * NN_INPUT_SIZE]; + for (int obj = 0; obj < 200; obj++) { + float *after_first = &net->road_linear_output[b * 200 * 64 + obj * 64]; _layernorm(after_first, net->road_layernorm->weights, net->road_layernorm->bias, - &net->road_layernorm_output[b * max_road_obs * NN_INPUT_SIZE + obj * NN_INPUT_SIZE], 1, - NN_INPUT_SIZE); + &net->road_layernorm_output[b * 200 * 64 + obj * 64], 1, 64); } } for (int b = 0; b < net->num_agents; b++) { - for (int obj = 0; obj < max_road_obs; obj++) { - float *after_first = &net->road_layernorm_output[b * max_road_obs * NN_INPUT_SIZE + obj * NN_INPUT_SIZE]; + for (int obj = 0; obj < 200; obj++) { + float *after_first = &net->road_layernorm_output[b * 200 * 64 + obj * 64]; _linear(after_first, net->road_encoder_two->weights, net->road_encoder_two->bias, - &net->road_linear_output_two[b * max_road_obs * NN_INPUT_SIZE + obj * NN_INPUT_SIZE], 1, - NN_INPUT_SIZE, NN_INPUT_SIZE); + &net->road_linear_output_two[b * 200 * 64 + obj * 64], 1, 64, 64); } } diff --git a/pufferlib/ocean/drive/render.h b/pufferlib/ocean/drive/render.h new file mode 100644 index 0000000000..c3a91661ae --- /dev/null +++ b/pufferlib/ocean/drive/render.h @@ -0,0 +1,791 @@ +#ifndef RENDER_H +#define RENDER_H + +#include +#include "rlgl.h" + +#define MAX_AGENTS 64 + +const Color STONE_GRAY = (Color){80, 80, 80, 255}; +const Color PUFF_RED = (Color){187, 0, 0, 255}; +const Color PUFF_CYAN = (Color){0, 187, 187, 255}; +const Color PUFF_WHITE = (Color){241, 241, 241, 241}; +const Color PUFF_BACKGROUND = (Color){6, 24, 24, 255}; +const Color PUFF_BACKGROUND2 = (Color){18, 72, 72, 255}; +const Color LIGHTGREEN = (Color){152, 255, 152, 255}; + +typedef struct Client { + float width; + float height; + Texture2D puffers; + Vector3 camera_target; + float camera_zoom; + Camera3D camera; + Model cars[6]; + Model cyclist; + Model pedestrian; + ModelAnimation *cycle_anim; + int car_assignments[MAX_AGENTS]; // To keep car model assignments consistent per vehicle + Vector3 default_camera_position; + Vector3 default_camera_target; +} Client; + +Client *make_client(Drive *env) { + Client *client = (Client *)calloc(1, sizeof(Client)); + client->width = 1280; + client->height = 704; + SetConfigFlags(FLAG_MSAA_4X_HINT); + InitWindow(client->width, client->height, "PufferLib Ray GPU Drive"); + SetTargetFPS(30); + client->puffers = LoadTexture("resources/puffers_128.png"); + client->cars[0] = LoadModel("resources/drive/RedCar.glb"); + client->cars[1] = LoadModel("resources/drive/WhiteCar.glb"); + client->cars[2] = LoadModel("resources/drive/BlueCar.glb"); + client->cars[3] = LoadModel("resources/drive/YellowCar.glb"); + client->cars[4] = LoadModel("resources/drive/GreenCar.glb"); + client->cars[5] = LoadModel("resources/drive/GreyCar.glb"); + client->cyclist = LoadModel("resources/drive/cyclist.glb"); + client->pedestrian = LoadModel("resources/drive/pedestrian.glb"); + int animCountCyc = 0; + client->cycle_anim = LoadModelAnimations("resources/drive/cyclist.glb", &animCountCyc); + for (int i = 0; i < MAX_AGENTS; i++) { + client->car_assignments[i] = (rand() % 4) + 1; + } + // Get initial target position from first active agent + Vector3 target_pos = { + 0, + 0, // Y is up + 1 // Z is depth + }; + + // Set up camera to look at target from above and behind + client->default_camera_position = (Vector3){ + 0, // Same X as target + 120.0f, // 20 units above target + 175.0f // 20 units behind target + }; + client->default_camera_target = target_pos; + client->camera.position = client->default_camera_position; + client->camera.target = client->default_camera_target; + client->camera.up = (Vector3){0.0f, -1.0f, 0.0f}; // Y is up + client->camera.fovy = 45.0f; + client->camera.projection = CAMERA_PERSPECTIVE; + client->camera_zoom = 1.0f; + return client; +} + +// Camera control functions +void handle_camera_controls(Client *client) { + static Vector2 prev_mouse_pos = {0}; + static bool is_dragging = false; + float camera_move_speed = 0.5f; + + // Handle mouse drag for camera movement + if (IsMouseButtonPressed(MOUSE_BUTTON_LEFT)) { + prev_mouse_pos = GetMousePosition(); + is_dragging = true; + } + + if (IsMouseButtonReleased(MOUSE_BUTTON_LEFT)) { + is_dragging = false; + } + + if (is_dragging) { + Vector2 current_mouse_pos = GetMousePosition(); + Vector2 delta = {(current_mouse_pos.x - prev_mouse_pos.x) * camera_move_speed, + -(current_mouse_pos.y - prev_mouse_pos.y) * camera_move_speed}; + + // Update camera position (only X and Y) + client->camera.position.x += delta.x; + client->camera.position.y += delta.y; + + // Update camera target (only X and Y) + client->camera.target.x += delta.x; + client->camera.target.y += delta.y; + + prev_mouse_pos = current_mouse_pos; + } + + // Handle mouse wheel for zoom + float wheel = GetMouseWheelMove(); + if (wheel != 0) { + float zoom_factor = 1.0f - (wheel * 0.1f); + // Calculate the current direction vector from target to position + Vector3 direction = {client->camera.position.x - client->camera.target.x, + client->camera.position.y - client->camera.target.y, + client->camera.position.z - client->camera.target.z}; + + // Scale the direction vector by the zoom factor + direction.x *= zoom_factor; + direction.y *= zoom_factor; + direction.z *= zoom_factor; + + // Update the camera position based on the scaled direction + client->camera.position.x = client->camera.target.x + direction.x; + client->camera.position.y = client->camera.target.y + direction.y; + client->camera.position.z = client->camera.target.z + direction.z; + } +} + +void draw_agent_obs(Drive *env, int agent_index, int mode, int obs_only, int lasers) { + // Diamond dimensions + float diamond_height = 3.0f; // Total height of diamond + float diamond_width = 1.5f; // Width of diamond + float diamond_z = 8.0f; // Base Z position + + // Define diamond points + Vector3 top_point = (Vector3){0.0f, 0.0f, diamond_z + diamond_height / 2}; // Top point + Vector3 bottom_point = (Vector3){0.0f, 0.0f, diamond_z - diamond_height / 2}; // Bottom point + Vector3 front_point = (Vector3){0.0f, diamond_width / 2, diamond_z}; // Front point + Vector3 back_point = (Vector3){0.0f, -diamond_width / 2, diamond_z}; // Back point + Vector3 left_point = (Vector3){-diamond_width / 2, 0.0f, diamond_z}; // Left point + Vector3 right_point = (Vector3){diamond_width / 2, 0.0f, diamond_z}; // Right point + + // Draw the diamond faces + // Top pyramid + + if (mode == 0) { + DrawTriangle3D(top_point, front_point, right_point, PUFF_CYAN); // Front-right face + DrawTriangle3D(top_point, right_point, back_point, PUFF_CYAN); // Back-right face + DrawTriangle3D(top_point, back_point, left_point, PUFF_CYAN); // Back-left face + DrawTriangle3D(top_point, left_point, front_point, PUFF_CYAN); // Front-left face + + // Bottom pyramid + DrawTriangle3D(bottom_point, right_point, front_point, PUFF_CYAN); // Front-right face + DrawTriangle3D(bottom_point, back_point, right_point, PUFF_CYAN); // Back-right face + DrawTriangle3D(bottom_point, left_point, back_point, PUFF_CYAN); // Back-left face + DrawTriangle3D(bottom_point, front_point, left_point, PUFF_CYAN); // Front-left face + } + if (!IsKeyDown(KEY_LEFT_CONTROL) && obs_only == 0) { + return; + } + + int ego_dim = (env->dynamics_model == JERK) ? EGO_FEATURES_JERK : EGO_FEATURES_CLASSIC; + if (env->max_stop_sign_observations > 0) + ego_dim += 1; + int num_reward_coefs = env->reward_conditioning ? NUM_REWARD_COEFS : 0; + int target_features = (env->target_type == TARGET_STATIC) ? env->num_target_waypoints * STATIC_TARGET_FEATURES + : env->num_target_waypoints * DYNAMIC_TARGET_FEATURES; + int max_obs = compute_observation_size(env); + float (*observations)[max_obs] = (float (*)[max_obs])env->observations; + float *agent_obs = &observations[agent_index][0]; + // self + int active_idx = env->active_agent_indices[agent_index]; + float heading_self = env->agents[active_idx].sim_heading; + float heading_self_x = cosf(heading_self); + float heading_self_y = sinf(heading_self); + float px = env->agents[active_idx].sim_x; + float py = env->agents[active_idx].sim_y; + // draw goal (first target waypoint, in ego frame) + if (env->num_target_waypoints > 0) { + int goal_obs_idx = ego_dim + num_reward_coefs; + float goal_x = agent_obs[goal_obs_idx] * MAX_GOAL_POSITION; + float goal_y = agent_obs[goal_obs_idx + 1] * MAX_GOAL_POSITION; + if (mode == 0) { + DrawSphere((Vector3){goal_x, goal_y, 1}, 0.5f, LIGHTGREEN); + DrawCircle3D((Vector3){goal_x, goal_y, 0.1f}, env->goal_radius, (Vector3){0, 0, 1}, 90.0f, + Fade(LIGHTGREEN, 0.3f)); + } + + if (mode == 1) { + float goal_x_world = px + (goal_x * heading_self_x - goal_y * heading_self_y); + float goal_y_world = py + (goal_x * heading_self_y + goal_y * heading_self_x); + DrawSphere((Vector3){goal_x_world, goal_y_world, 1}, 0.5f, LIGHTGREEN); + DrawCircle3D((Vector3){goal_x_world, goal_y_world, 0.1f}, env->goal_radius, (Vector3){0, 0, 1}, 90.0f, + Fade(LIGHTGREEN, 0.3f)); + } + } + // First draw other agent observations + int obs_idx = ego_dim + num_reward_coefs + target_features; // Start after ego, conditioning, and target obs + for (int j = 0; j < env->max_partner_observations; j++) { + if (agent_obs[obs_idx] == 0 || agent_obs[obs_idx + 1] == 0) { + obs_idx += PARTNER_FEATURES; + continue; + } + // Draw position of other agents + float x = agent_obs[obs_idx] * 50; + float y = agent_obs[obs_idx + 1] * 50; + if (lasers && mode == 0) { + DrawLine3D((Vector3){0, 0, 0}, (Vector3){x, y, 1}, ORANGE); + } + + float partner_x = px + (x * heading_self_x - y * heading_self_y); + float partner_y = py + (x * heading_self_y + y * heading_self_x); + if (lasers && mode == 1) { + DrawLine3D((Vector3){px, py, 1}, (Vector3){partner_x, partner_y, 1}, ORANGE); + } + + float half_width = 0.5 * agent_obs[obs_idx + 2] * MAX_VEH_WIDTH; + float half_len = 0.5 * agent_obs[obs_idx + 3] * MAX_VEH_LEN; + float theta_x = agent_obs[obs_idx + 4]; + float theta_y = agent_obs[obs_idx + 5]; + float partner_angle = atan2f(theta_y, theta_x); + float cos_heading = cosf(partner_angle); + float sin_heading = sinf(partner_angle); + Vector3 corners[4] = { + (Vector3){x + (half_len * cos_heading - half_width * sin_heading), + y + (half_len * sin_heading + half_width * cos_heading), 1}, + (Vector3){x + (half_len * cos_heading + half_width * sin_heading), + y + (half_len * sin_heading - half_width * cos_heading), 1}, + (Vector3){x + (-half_len * cos_heading + half_width * sin_heading), + y + (-half_len * sin_heading - half_width * cos_heading), 1}, + (Vector3){x + (-half_len * cos_heading - half_width * sin_heading), + y + (-half_len * sin_heading + half_width * cos_heading), 1}, + }; + + if (mode == 0) { + for (int j = 0; j < 4; j++) { + DrawLine3D(corners[j], corners[(j + 1) % 4], ORANGE); + } + } + + if (mode == 1) { + Vector3 world_corners[4]; + for (int j = 0; j < 4; j++) { + float lx = corners[j].x; + float ly = corners[j].y; + + world_corners[j].x = px + (lx * heading_self_x - ly * heading_self_y); + world_corners[j].y = py + (lx * heading_self_y + ly * heading_self_x); + world_corners[j].z = 1; + } + for (int j = 0; j < 4; j++) { + DrawLine3D(world_corners[j], world_corners[(j + 1) % 4], ORANGE); + } + } + + // draw an arrow above the car pointing in the direction that the partner is going + float arrow_length = 2.5f; + float arrow_x = x + arrow_length * cosf(partner_angle); + float arrow_y = y + arrow_length * sinf(partner_angle); + float arrow_x_world; + float arrow_y_world; + if (mode == 0) { + DrawLine3D((Vector3){x, y, 1}, (Vector3){arrow_x, arrow_y, 1}, PUFF_WHITE); + } + if (mode == 1) { + arrow_x_world = px + (arrow_x * heading_self_x - arrow_y * heading_self_y); + arrow_y_world = py + (arrow_x * heading_self_y + arrow_y * heading_self_x); + DrawLine3D((Vector3){partner_x, partner_y, 1}, (Vector3){arrow_x_world, arrow_y_world, 1}, PUFF_WHITE); + } + // Calculate perpendicular offsets for arrow head + float arrow_size = 0.3f; // Size of the arrow head + float dx = arrow_x - x; + float dy = arrow_y - y; + float length = sqrtf(dx * dx + dy * dy); + if (length > 0) { + // Normalize direction vector + dx /= length; + dy /= length; + + // Calculate perpendicular vector + float perp_x = -dy * arrow_size; + float perp_y = dx * arrow_size; + + float arrow_x_end1 = arrow_x - dx * arrow_size + perp_x; + float arrow_y_end1 = arrow_y - dy * arrow_size + perp_y; + float arrow_x_end2 = arrow_x - dx * arrow_size - perp_x; + float arrow_y_end2 = arrow_y - dy * arrow_size - perp_y; + + // Draw the two lines forming the arrow head + if (mode == 0) { + DrawLine3D((Vector3){arrow_x, arrow_y, 1}, (Vector3){arrow_x_end1, arrow_y_end1, 1}, PUFF_WHITE); + DrawLine3D((Vector3){arrow_x, arrow_y, 1}, (Vector3){arrow_x_end2, arrow_y_end2, 1}, PUFF_WHITE); + } + + if (mode == 1) { + float arrow_x_end1_world = px + (arrow_x_end1 * heading_self_x - arrow_y_end1 * heading_self_y); + float arrow_y_end1_world = py + (arrow_x_end1 * heading_self_y + arrow_y_end1 * heading_self_x); + float arrow_x_end2_world = px + (arrow_x_end2 * heading_self_x - arrow_y_end2 * heading_self_y); + float arrow_y_end2_world = py + (arrow_x_end2 * heading_self_y + arrow_y_end2 * heading_self_x); + DrawLine3D((Vector3){arrow_x_world, arrow_y_world, 1}, + (Vector3){arrow_x_end1_world, arrow_y_end1_world, 1}, PUFF_WHITE); + DrawLine3D((Vector3){arrow_x_world, arrow_y_world, 1}, + (Vector3){arrow_x_end2_world, arrow_y_end2_world, 1}, PUFF_WHITE); + } + } + + obs_idx += PARTNER_FEATURES; + } + // Then draw lane segment observations (obs_idx is now at lane obs start after partner loop) + int lane_obs_start = obs_idx; + for (int k = 0; k < env->max_lane_segment_observations; k++) { + int entity_idx = lane_obs_start + k * ROAD_FEATURES; + if (agent_obs[entity_idx] == 0 && agent_obs[entity_idx + 1] == 0) { + continue; + } + Color lineColor = PUFF_CYAN; + // For road segments, draw line between start and end points + float x_middle = agent_obs[entity_idx] * 50; + float y_middle = agent_obs[entity_idx + 1] * 50; + float rel_angle_x = (agent_obs[entity_idx + 4]); + float rel_angle_y = (agent_obs[entity_idx + 5]); + float rel_angle = atan2f(rel_angle_y, rel_angle_x); + float segment_length = agent_obs[entity_idx + 2] * MAX_ROAD_SEGMENT_LENGTH; + // Calculate endpoint using the relative angle directly + // Calculate endpoint directly + float x_start = x_middle - segment_length * cosf(rel_angle); + float y_start = y_middle - segment_length * sinf(rel_angle); + float x_end = x_middle + segment_length * cosf(rel_angle); + float y_end = y_middle + segment_length * sinf(rel_angle); + + if (lasers && mode == 0) { + DrawLine3D((Vector3){0, 0, 0}, (Vector3){x_middle, y_middle, 1}, lineColor); + } + + if (mode == 1) { + float x_middle_world = px + (x_middle * heading_self_x - y_middle * heading_self_y); + float y_middle_world = py + (x_middle * heading_self_y + y_middle * heading_self_x); + float x_start_world = px + (x_start * heading_self_x - y_start * heading_self_y); + float y_start_world = py + (x_start * heading_self_y + y_start * heading_self_x); + float x_end_world = px + (x_end * heading_self_x - y_end * heading_self_y); + float y_end_world = py + (x_end * heading_self_y + y_end * heading_self_x); + DrawCube((Vector3){x_middle_world, y_middle_world, 1}, 0.5f, 0.5f, 0.5f, lineColor); + DrawLine3D((Vector3){x_start_world, y_start_world, 1}, (Vector3){x_end_world, y_end_world, 1}, BLUE); + if (lasers) + DrawLine3D((Vector3){px, py, 1}, (Vector3){x_middle_world, y_middle_world, 1}, lineColor); + } + if (mode == 0) { + DrawCube((Vector3){x_middle, y_middle, 1}, 0.5f, 0.5f, 0.5f, lineColor); + DrawLine3D((Vector3){x_start, y_start, 1}, (Vector3){x_end, y_end, 1}, BLUE); + } + } +} + +void draw_road_edge(Drive *env, float start_x, float start_y, float end_x, float end_y, Color lineColor) { + Color CURB_TOP = (Color){220, 220, 220, 255}; // Top surface - lightest + Color CURB_SIDE = (Color){180, 180, 180, 255}; // Side faces - medium + Color CURB_BOTTOM = (Color){160, 160, 160, 255}; + // Calculate curb dimensions + float curb_height = 0.5f; // Height of the curb + float curb_width = 0.3f; // Width/thickness of the curb + float road_z = 0.2f; // Ensure z-level for roads is below agents + + // Calculate direction vector between start and end + Vector3 direction = {end_x - start_x, end_y - start_y, 0.0f}; + + // Calculate length of the segment + float length = sqrtf(direction.x * direction.x + direction.y * direction.y); + + // Normalize direction vector + Vector3 normalized_dir = {direction.x / length, direction.y / length, 0.0f}; + + // Calculate perpendicular vector for width + Vector3 perpendicular = {-normalized_dir.y, normalized_dir.x, 0.0f}; + + // Calculate the four bottom corners of the curb + Vector3 b1 = {start_x - perpendicular.x * curb_width / 2, start_y - perpendicular.y * curb_width / 2, road_z}; + Vector3 b2 = {start_x + perpendicular.x * curb_width / 2, start_y + perpendicular.y * curb_width / 2, road_z}; + Vector3 b3 = {end_x + perpendicular.x * curb_width / 2, end_y + perpendicular.y * curb_width / 2, road_z}; + Vector3 b4 = {end_x - perpendicular.x * curb_width / 2, end_y - perpendicular.y * curb_width / 2, road_z}; + + // Draw the curb faces + // Bottom face + DrawTriangle3D(b1, b2, b3, CURB_BOTTOM); + DrawTriangle3D(b1, b3, b4, CURB_BOTTOM); + + // Top face (raised by curb_height) + Vector3 t1 = {b1.x, b1.y, b1.z + curb_height}; + Vector3 t2 = {b2.x, b2.y, b2.z + curb_height}; + Vector3 t3 = {b3.x, b3.y, b3.z + curb_height}; + Vector3 t4 = {b4.x, b4.y, b4.z + curb_height}; + DrawTriangle3D(t1, t3, t2, CURB_TOP); + DrawTriangle3D(t1, t4, t3, CURB_TOP); + + // Side faces + DrawTriangle3D(b1, t1, b2, CURB_SIDE); + DrawTriangle3D(t1, t2, b2, CURB_SIDE); + DrawTriangle3D(b2, t2, b3, CURB_SIDE); + DrawTriangle3D(t2, t3, b3, CURB_SIDE); + DrawTriangle3D(b3, t3, b4, CURB_SIDE); + DrawTriangle3D(t3, t4, b4, CURB_SIDE); + DrawTriangle3D(b4, t4, b1, CURB_SIDE); + DrawTriangle3D(t4, t1, b1, CURB_SIDE); +} + +void draw_scene(Drive *env, Client *client, int mode, int obs_only, int lasers, int show_grid) { + // Draw a grid to help with orientation + // DrawGrid(20, 1.0f); + DrawLine3D((Vector3){env->grid_map->top_left_x, env->grid_map->top_left_y, 0}, + (Vector3){env->grid_map->bottom_right_x, env->grid_map->top_left_y, 0}, PUFF_CYAN); + DrawLine3D((Vector3){env->grid_map->top_left_x, env->grid_map->bottom_right_y, 0}, + (Vector3){env->grid_map->top_left_x, env->grid_map->top_left_y, 0}, PUFF_CYAN); + DrawLine3D((Vector3){env->grid_map->bottom_right_x, env->grid_map->bottom_right_y, 0}, + (Vector3){env->grid_map->bottom_right_x, env->grid_map->top_left_y, 0}, PUFF_CYAN); + DrawLine3D((Vector3){env->grid_map->top_left_x, env->grid_map->bottom_right_y, 0}, + (Vector3){env->grid_map->bottom_right_x, env->grid_map->bottom_right_y, 0}, PUFF_CYAN); + + for (int i = 0; i < env->num_total_agents; i++) { + Agent *agent = &env->agents[i]; + // Draw objects + // Check if this vehicle is an active agent + bool is_active_agent = false; + bool is_static_agent = false; + int agent_index = -1; + for (int j = 0; j < env->active_agent_count; j++) { + if (env->active_agent_indices[j] == i) { + is_active_agent = true; + agent_index = j; + break; + } + } + for (int j = 0; j < env->static_agent_count; j++) { + if (env->static_agent_indices[j] == i) { + is_static_agent = true; + break; + } + } + if (!is_active_agent && !is_static_agent) { + continue; + } + Vector3 position; + float heading; + position = (Vector3){agent->sim_x, agent->sim_y, 1}; + heading = agent->sim_heading; + // Create size vector + Vector3 size = {agent->sim_length, agent->sim_width, agent->sim_height}; + + bool is_expert = (!is_active_agent) && (agent->mark_as_expert == 1); + + // Save current transform + if (mode == 1) { + float cos_heading = cosf(heading); + float sin_heading = sinf(heading); + + // Calculate half dimensions + float half_len = agent->sim_length * 0.5f; + float half_width = agent->sim_width * 0.5f; + + // Calculate the four corners of the collision box + Vector3 corners[4] = { + (Vector3){position.x + (half_len * cos_heading - half_width * sin_heading), + position.y + (half_len * sin_heading + half_width * cos_heading), position.z}, + + (Vector3){position.x + (half_len * cos_heading + half_width * sin_heading), + position.y + (half_len * sin_heading - half_width * cos_heading), position.z}, + (Vector3){position.x + (-half_len * cos_heading + half_width * sin_heading), + position.y + (-half_len * sin_heading - half_width * cos_heading), position.z}, + (Vector3){position.x + (-half_len * cos_heading - half_width * sin_heading), + position.y + (-half_len * sin_heading + half_width * cos_heading), position.z}, + + }; + + if (agent_index == env->human_agent_idx && !agent->metrics_array[REACHED_GOAL_IDX]) { + draw_agent_obs(env, agent_index, mode, obs_only, lasers); + } + if ((obs_only || IsKeyDown(KEY_LEFT_CONTROL)) && agent_index != env->human_agent_idx) { + continue; + } + + // --- Draw the car --- + Color car_color = GRAY; // default for static + if (is_expert) + car_color = GOLD; // expert replay + if (is_active_agent) + car_color = BLUE; // policy-controlled + if (is_active_agent && (agent->metrics_array[COLLISION_IDX] > 0 || agent->metrics_array[OFFROAD_IDX] > 0 || + agent->metrics_array[RED_LIGHT_IDX] > 0)) + car_color = RED; + rlSetLineWidth(3.0f); + for (int j = 0; j < 4; j++) { + DrawLine3D(corners[j], corners[(j + 1) % 4], car_color); + } + // --- Draw a heading arrow pointing forward --- + Vector3 arrowStart = position; + Vector3 arrowEnd = {position.x + cos_heading * half_len * 1.5f, // extend arrow beyond car + position.y + sin_heading * half_len * 1.5f, position.z}; + + DrawLine3D(arrowStart, arrowEnd, car_color); + DrawSphere(arrowEnd, 0.2f, car_color); // arrow tip + + } else { + rlPushMatrix(); + // Translate to position, rotate around Y axis, then draw + rlTranslatef(position.x, position.y, position.z); + rlRotatef(heading * RAD2DEG, 0.0f, 0.0f, 1.0f); // Convert radians to degrees + // Determine color based on status + Model car_model = client->cars[5]; + if (is_active_agent) { + car_model = client->cars[client->car_assignments[i % 64]]; + } + if (is_active_agent && (agent->metrics_array[COLLISION_IDX] > 0 || agent->metrics_array[OFFROAD_IDX] > 0 || + agent->metrics_array[RED_LIGHT_IDX] > 0)) { + car_model = client->cars[0]; // Collided agent + } + // Draw obs for human selected agent + if (agent_index == env->human_agent_idx && !agent->metrics_array[REACHED_GOAL_IDX]) { + draw_agent_obs(env, agent_index, mode, obs_only, lasers); + } + // Draw cube for cars static and active + // Calculate scale factors based on desired size and model dimensions + + BoundingBox bounds = GetModelBoundingBox(car_model); + Vector3 model_size = {bounds.max.x - bounds.min.x, bounds.max.y - bounds.min.y, + bounds.max.z - bounds.min.z}; + Vector3 scale = {size.x / model_size.x, size.y / model_size.y, size.z / model_size.z}; + if ((obs_only || IsKeyDown(KEY_LEFT_CONTROL)) && agent_index != env->human_agent_idx) { + rlPopMatrix(); + continue; + } + + DrawModelEx(car_model, (Vector3){0, 0, 0}, (Vector3){1, 0, 0}, 90.0f, scale, WHITE); + { + float cos_heading = cosf(heading); + float sin_heading = sinf(heading); + float half_len = agent->sim_length * 0.5f; + float half_width = agent->sim_width * 0.5f; + Vector3 corners[4] = { + (Vector3){0 + (half_len * cos_heading - half_width * sin_heading), + 0 + (half_len * sin_heading + half_width * cos_heading), 0}, + (Vector3){0 + (half_len * cos_heading + half_width * sin_heading), + 0 + (half_len * sin_heading - half_width * cos_heading), 0}, + (Vector3){0 + (-half_len * cos_heading + half_width * sin_heading), + 0 + (-half_len * sin_heading - half_width * cos_heading), 0}, + (Vector3){0 + (-half_len * cos_heading - half_width * sin_heading), + 0 + (-half_len * sin_heading + half_width * cos_heading), 0}, + }; + Color wire_color = GRAY; // static + if (!is_active_agent && agent->mark_as_expert == 1) + wire_color = GOLD; // expert replay + if (is_active_agent) + wire_color = BLUE; // policy + if (is_active_agent && + (agent->metrics_array[COLLISION_IDX] > 0 || agent->metrics_array[OFFROAD_IDX] > 0 || + agent->metrics_array[RED_LIGHT_IDX] > 0)) + wire_color = RED; + rlSetLineWidth(2.0f); + for (int j = 0; j < 4; j++) { + DrawLine3D(corners[j], corners[(j + 1) % 4], wire_color); + } + } + rlPopMatrix(); + } + + // FPV Camera Control + if (IsKeyDown(KEY_SPACE) && env->human_agent_idx == agent_index) { + if (agent->metrics_array[REACHED_GOAL_IDX]) { + env->human_agent_idx = rand() % env->active_agent_count; + } + Vector3 camera_position = + (Vector3){position.x - (25.0f * cosf(heading)), position.y - (25.0f * sinf(heading)), position.z + 15}; + + Vector3 camera_target = + (Vector3){position.x + 40.0f * cosf(heading), position.y + 40.0f * sinf(heading), position.z - 5.0f}; + client->camera.position = camera_position; + client->camera.target = camera_target; + client->camera.up = (Vector3){0, 0, 1}; + } + if (IsKeyReleased(KEY_SPACE)) { + client->camera.position = client->default_camera_position; + client->camera.target = client->default_camera_target; + client->camera.up = (Vector3){0, 0, 1}; + } + // Draw goal position for active agents + + if (!is_active_agent || agent->sim_valid == 0) { + continue; + } + if (!IsKeyDown(KEY_LEFT_CONTROL) && obs_only == 0) { + DrawSphere((Vector3){agent->goal_position_x, agent->goal_position_y, 1}, 0.5f, DARKGREEN); + + DrawCircle3D((Vector3){agent->goal_position_x, agent->goal_position_y, 0.1f}, env->goal_radius, + (Vector3){0, 0, 1}, 90.0f, Fade(LIGHTGREEN, 0.3f)); + } + } + for (int i = 0; i < env->num_road_elements; i++) { + RoadMapElement *element = &env->road_elements[i]; + + for (int j = 0; j < element->segment_length - 1; j++) { + Vector3 start = {element->x[j], element->y[j], 1}; + Vector3 end = {element->x[j + 1], element->y[j + 1], 1}; + Color lineColor = GRAY; + + if (is_road_lane(element->type)) + lineColor = GRAY; + else if (is_road_line(element->type)) + lineColor = BLUE; + else if (is_road_edge(element->type)) + lineColor = WHITE; + else if (element->type == DRIVEWAY) + lineColor = RED; + if (!IsKeyDown(KEY_LEFT_CONTROL) && obs_only == 0) { + draw_road_edge(env, start.x, start.y, end.x, end.y, lineColor); + } + } + } + if (show_grid) { + // Draw grid cells using the stored bounds + float grid_start_x = env->grid_map->top_left_x; + float grid_start_y = env->grid_map->bottom_right_y; + for (int i = 0; i < env->grid_map->grid_cols; i++) { + for (int j = 0; j < env->grid_map->grid_rows; j++) { + float x = grid_start_x + i * GRID_CELL_SIZE; + float y = grid_start_y + j * GRID_CELL_SIZE; + DrawCubeWires((Vector3){x + GRID_CELL_SIZE / 2, y + GRID_CELL_SIZE / 2, 1}, GRID_CELL_SIZE, + GRID_CELL_SIZE, 0.1f, PUFF_BACKGROUND2); + } + } + } + + EndMode3D(); + + // Draw track indices for the tracks to predict + if (mode == 1 && env->control_mode == CONTROL_WOSAC) { + float map_height = env->grid_map->top_left_y - env->grid_map->bottom_right_y; + float pixels_per_world_unit = client->height / map_height; + + for (int i = 0; i < env->active_agent_count; i++) { + int agent_idx = env->active_agent_indices[i]; + int womd_track_idx = env->tracks_to_predict[i]; + + float raw_x = -env->agents[agent_idx].sim_x * pixels_per_world_unit; + float raw_y = env->agents[agent_idx].sim_y * pixels_per_world_unit; + + int screen_x = (int)raw_x + client->width / 2 + 20; + int screen_y = (int)raw_y + client->height / 2 - 25; + + if (screen_x >= 0 && screen_x <= client->width && screen_y >= 0 && screen_y <= client->height) { + char text[32]; + snprintf(text, sizeof(text), "%d", womd_track_idx); + int text_width = MeasureText(text, 20); + DrawText(text, screen_x - text_width / 2, screen_y, 20, PUFF_WHITE); + } + } + } +} + +void saveTopDownImage(Drive *env, Client *client, const char *filename, RenderTexture2D target, int map_height, int obs, + int lasers, int trajectories, int frame_count, float *path, int log_trajectories, int show_grid) { + // Top-down orthographic camera + Camera3D camera = {0}; + camera.position = (Vector3){0.0f, 0.0f, 500.0f}; // above the scene + camera.target = (Vector3){0.0f, 0.0f, 0.0f}; // look at origin + camera.up = (Vector3){0.0f, -1.0f, 0.0f}; + camera.fovy = map_height; + camera.projection = CAMERA_ORTHOGRAPHIC; + Color road = (Color){35, 35, 37, 255}; + + BeginTextureMode(target); + ClearBackground(road); + BeginMode3D(camera); + rlEnableDepthTest(); + + // Draw log trajectories FIRST (in background at lower Z-level) + if (log_trajectories) { + for (int i = 0; i < env->num_total_agents; i++) { + Agent *agent = &env->agents[i]; + for (int j = 0; j < agent->trajectory_length; j++) { + float x = agent->log_trajectory_x[j]; + float y = agent->log_trajectory_y[j]; + float valid = agent->log_valid[j]; + if (!valid) + continue; + DrawSphere((Vector3){x, y, 0.5f}, 0.3f, Fade(LIGHTGREEN, 0.6f)); + } + } + } + + // Draw current path trajectories SECOND (slightly higher than log trajectories) + if (trajectories) { + for (int i = 0; i < frame_count; i++) { + DrawSphere((Vector3){path[i * 2], path[i * 2 + 1], 0.8f}, 0.5f, YELLOW); + } + } + + // Draw main scene LAST (on top) + draw_scene(env, client, 1, obs, lasers, show_grid); + + EndMode3D(); + EndTextureMode(); + + // save to file + Image img = LoadImageFromTexture(target.texture); + ImageFlipVertical(&img); + ExportImage(img, filename); + UnloadImage(img); +} + +void saveAgentViewImage(Drive *env, Client *client, const char *filename, RenderTexture2D target, int map_height, + int obs_only, int lasers, int show_grid) { + // Agent perspective camera following the human agent + int agent_idx = env->active_agent_indices[env->human_agent_idx]; + Agent *agent = &env->agents[agent_idx]; + + Camera3D camera = {0}; + // Position camera behind and above the agent + camera.position = (Vector3){agent->sim_x - (25.0f * cosf(agent->sim_heading)), + agent->sim_y - (25.0f * sinf(agent->sim_heading)), 15.0f}; + camera.target = (Vector3){agent->sim_x + 40.0f * cosf(agent->sim_heading), + agent->sim_y + 40.0f * sinf(agent->sim_heading), 1.0f}; + camera.up = (Vector3){0.0f, 0.0f, 1.0f}; + camera.fovy = 45.0f; + camera.projection = CAMERA_PERSPECTIVE; + + Color road = (Color){35, 35, 37, 255}; + + BeginTextureMode(target); + ClearBackground(road); + BeginMode3D(camera); + rlEnableDepthTest(); + draw_scene(env, client, 0, obs_only, lasers, show_grid); // mode=0 for agent view + EndMode3D(); + EndTextureMode(); + + // Save to file + Image img = LoadImageFromTexture(target.texture); + ImageFlipVertical(&img); + ExportImage(img, filename); + UnloadImage(img); +} + +void c_render(Drive *env) { + if (env->client == NULL) { + env->client = make_client(env); + } + Client *client = env->client; + BeginDrawing(); + Color road = (Color){35, 35, 37, 255}; + ClearBackground(road); + BeginMode3D(client->camera); + handle_camera_controls(env->client); + draw_scene(env, client, 0, 0, 0, 0); + // Draw debug info + DrawText(TextFormat("Camera Position: (%.2f, %.2f, %.2f)", client->camera.position.x, client->camera.position.y, + client->camera.position.z), + 10, 10, 20, PUFF_WHITE); + DrawText(TextFormat("Camera Target: (%.2f, %.2f, %.2f)", client->camera.target.x, client->camera.target.y, + client->camera.target.z), + 10, 30, 20, PUFF_WHITE); + DrawText(TextFormat("Timestep: %d", env->timestep), 10, 50, 20, PUFF_WHITE); + // acceleration & steering + int human_idx = env->active_agent_indices[env->human_agent_idx]; + DrawText(TextFormat("Controlling Agent: %d", env->human_agent_idx), 10, 70, 20, PUFF_WHITE); + DrawText(TextFormat("Agent Index: %d", human_idx), 10, 90, 20, PUFF_WHITE); + // Controls help + DrawText("Controls: W/S - Accelerate/Brake, A/D - Steer, 1-4 - Switch Agent", 10, client->height - 30, 20, + PUFF_WHITE); + // acceleration & steering + if (env->action_type == 1) { // continuous (float) + float (*action_array_f)[2] = (float (*)[2])env->actions; + DrawText(TextFormat("Acceleration: %.2f", action_array_f[env->human_agent_idx][0]), 10, 110, 20, PUFF_WHITE); + DrawText(TextFormat("Steering: %.2f", action_array_f[env->human_agent_idx][1]), 10, 130, 20, PUFF_WHITE); + } else { // discrete (int) + int (*action_array)[2] = (int (*)[2])env->actions; + DrawText(TextFormat("Acceleration: %d", action_array[env->human_agent_idx][0]), 10, 110, 20, PUFF_WHITE); + DrawText(TextFormat("Steering: %d", action_array[env->human_agent_idx][1]), 10, 130, 20, PUFF_WHITE); + } + DrawText(TextFormat("Grid Rows: %d", env->grid_map->grid_rows), 10, 150, 20, PUFF_WHITE); + DrawText(TextFormat("Grid Cols: %d", env->grid_map->grid_cols), 10, 170, 20, PUFF_WHITE); + EndDrawing(); +} + +void close_client(Client *client) { + for (int i = 0; i < 6; i++) { + UnloadModel(client->cars[i]); + } + UnloadTexture(client->puffers); + CloseWindow(); + free(client); +} + +#endif // RENDER_H diff --git a/pufferlib/ocean/env_binding.h b/pufferlib/ocean/env_binding.h index c2c0c10f03..5963e1dcb4 100644 --- a/pufferlib/ocean/env_binding.h +++ b/pufferlib/ocean/env_binding.h @@ -1,9 +1,8 @@ -#include "env_config.h" #include #include // Forward declarations for env-specific functions supplied by user -static int my_log(PyObject *dict, Log *log); +static int my_log(PyObject *dict, Env *env, Log *log, float n); static int my_init(Env *env, PyObject *args, PyObject *kwargs); static PyObject *my_shared(PyObject *self, PyObject *args, PyObject *kwargs); @@ -43,8 +42,8 @@ static Env *unpack_env(PyObject *args) { // Python function to initialize the environment static PyObject *env_init(PyObject *self, PyObject *args, PyObject *kwargs) { - if (PyTuple_Size(args) != 6) { - PyErr_SetString(PyExc_TypeError, "Environment requires 5 arguments"); + if (PyTuple_Size(args) != 7) { + PyErr_SetString(PyExc_TypeError, "Environment requires 7 positional arguments"); return NULL; } @@ -130,7 +129,19 @@ static PyObject *env_init(PyObject *self, PyObject *args, PyObject *kwargs) { } env->truncations = PyArray_DATA(truncations); - PyObject *seed_arg = PyTuple_GetItem(args, 5); + PyObject *msk = PyTuple_GetItem(args, 5); + if (!PyObject_TypeCheck(msk, &PyArray_Type)) { + PyErr_SetString(PyExc_TypeError, "Masks must be a NumPy array"); + return NULL; + } + PyArrayObject *masks_array = (PyArrayObject *)msk; + if (!PyArray_ISCONTIGUOUS(masks_array)) { + PyErr_SetString(PyExc_ValueError, "Masks must be contiguous"); + return NULL; + } + env->masks = PyArray_DATA(masks_array); + + PyObject *seed_arg = PyTuple_GetItem(args, 6); if (!PyObject_TypeCheck(seed_arg, &PyLong_Type)) { PyErr_SetString(PyExc_TypeError, "seed must be an integer"); return NULL; @@ -198,34 +209,13 @@ static PyObject *env_step(PyObject *self, PyObject *args) { Py_RETURN_NONE; } -// Python function to render the environment +// Python function to step the environment static PyObject *env_render(PyObject *self, PyObject *args) { - int num_args = PyTuple_Size(args); - if (num_args != 3) { - PyErr_SetString(PyExc_TypeError, "env_render requires 3 arguments (env_handle, view_mode, draw_traces)"); - return NULL; - } - Env *env = unpack_env(args); if (!env) { return NULL; } - - PyObject *view_mode_arg = PyTuple_GetItem(args, 1); - if (!PyObject_TypeCheck(view_mode_arg, &PyLong_Type)) { - PyErr_SetString(PyExc_TypeError, "view_mode must be an integer"); - return NULL; - } - int view_mode = PyLong_AsLong(view_mode_arg); - - PyObject *show_traces_arg = PyTuple_GetItem(args, 2); - if (!PyObject_TypeCheck(show_traces_arg, &PyBool_Type)) { - PyErr_SetString(PyExc_TypeError, "draw_traces must be a boolean"); - return NULL; - } - bool draw_traces = PyObject_IsTrue(show_traces_arg); - - c_render(env, view_mode, draw_traces); + c_render(env); Py_RETURN_NONE; } @@ -514,7 +504,7 @@ static PyObject *vec_reset(PyObject *self, PyObject *args) { for (int i = 0; i < vec->num_envs; i++) { // Assumes each process has the same number of environments - srand(i + seed * vec->num_envs); + srand(i + seed); c_reset(vec->envs[i]); } Py_RETURN_NONE; @@ -540,8 +530,8 @@ static PyObject *vec_step(PyObject *self, PyObject *arg) { static PyObject *vec_render(PyObject *self, PyObject *args) { int num_args = PyTuple_Size(args); - if (num_args != 4) { - PyErr_SetString(PyExc_TypeError, "vec_render requires 4 arguments"); + if (num_args != 2) { + PyErr_SetString(PyExc_TypeError, "vec_render requires 2 arguments"); return NULL; } @@ -551,23 +541,14 @@ static PyObject *vec_render(PyObject *self, PyObject *args) { return NULL; } - if (!PyObject_TypeCheck(PyTuple_GetItem(args, 1), &PyLong_Type)) { - PyErr_SetString(PyExc_TypeError, "view_mode must be an integer"); - return NULL; - } - if (!PyObject_TypeCheck(PyTuple_GetItem(args, 2), &PyBool_Type)) { - PyErr_SetString(PyExc_TypeError, "draw_traces must be a boolean"); - return NULL; - } - if (!PyObject_TypeCheck(PyTuple_GetItem(args, 3), &PyLong_Type)) { + PyObject *env_id_arg = PyTuple_GetItem(args, 1); + if (!PyObject_TypeCheck(env_id_arg, &PyLong_Type)) { PyErr_SetString(PyExc_TypeError, "env_id must be an integer"); return NULL; } - int view_mode = PyLong_AsLong(PyTuple_GetItem(args, 1)); - bool draw_traces = PyObject_IsTrue(PyTuple_GetItem(args, 2)); - int env_id = PyLong_AsLong(PyTuple_GetItem(args, 3)); + int env_id = PyLong_AsLong(env_id_arg); - c_render(vec->envs[env_id], view_mode, draw_traces); + c_render(vec->envs[env_id]); Py_RETURN_NONE; } @@ -600,83 +581,125 @@ static PyObject *vec_log(PyObject *self, PyObject *args) { // horribly if Log has non-float data. PyObject *num_agents_arg = PyTuple_GetItem(args, 1); float num_agents = (float)PyLong_AsLong(num_agents_arg); - - Log aggregate = {0}; int num_keys = sizeof(Log) / sizeof(float); - for (int i = 0; i < vec->num_envs; i++) { - Env *env = vec->envs[i]; - for (int j = 0; j < num_keys; j++) { - ((float *)&aggregate)[j] += ((float *)&env->log)[j]; - } - } - PyObject *dict = PyDict_New(); + Env *env = vec->envs[0]; + if (env->eval_mode) { + PyObject *list = PyList_New(vec->num_envs); + PyObject *dict = PyDict_New(); - // Only log if we have at least num_agents worth of data - if (aggregate.n < num_agents) { - return dict; - } - - // Got enough data. Reset logs and return metrics - for (int i = 0; i < vec->num_envs; i++) { - Env *env = vec->envs[i]; - for (int j = 0; j < num_keys; j++) { - ((float *)&env->log)[j] = 0.0f; + if (env->log.n == 0) { + return dict; } - } - float n = aggregate.n; + // Got enough data. Reset logs and return metrics + for (int i = 0; i < vec->num_envs; i++) { + PyObject *dict = PyDict_New(); + Env *env = vec->envs[i]; + float n = env->log.n; + // Average across agents + for (int i = 0; i < num_keys; i++) { + ((float *)&env->log)[i] /= n; + } + my_log(dict, env, &env->log, n); + assign_to_dict(dict, "n", n); + // Add map_name to dict + if (env->map_name) { + PyObject *s = PyUnicode_FromString(env->map_name); + if (s != NULL) { + PyDict_SetItemString(dict, "map_name", s); + Py_DECREF(s); + } + } + + PyList_SetItem(list, i, dict); + } + // Reset logs to 0 after extracting metrics (prevents accumulation across episodes) + for (int i = 0; i < vec->num_envs; i++) { + Env *env = vec->envs[i]; + for (int j = 0; j < num_keys; j++) { + ((float *)&env->log)[j] = 0.0f; + } + } + return list; + } else { + Log aggregate = {0}; + for (int i = 0; i < vec->num_envs; i++) { + Env *env = vec->envs[i]; + for (int j = 0; j < num_keys; j++) { + ((float *)&aggregate)[j] += ((float *)&env->log)[j]; + } + } - // Average across agents - for (int i = 0; i < num_keys; i++) { - ((float *)&aggregate)[i] /= n; - } + PyObject *dict = PyDict_New(); + + // Only log if we have at least num_agents worth of data + Env *env = vec->envs[0]; + if (env->eval_mode) { + if (aggregate.n == 0) { + return dict; + } + } else { + if (aggregate.n < num_agents) { + return dict; + } + } - // Compute completion_rate from aggregated counts - aggregate.completion_rate = aggregate.goals_reached_this_episode / aggregate.goals_sampled_this_episode; + // Got enough data. Reset logs and return metrics + for (int i = 0; i < vec->num_envs; i++) { + Env *env = vec->envs[i]; + for (int j = 0; j < num_keys; j++) { + ((float *)&env->log)[j] = 0.0f; + } + } - // User populates dict - my_log(dict, &aggregate); - assign_to_dict(dict, "n", n); + float n = aggregate.n; - return dict; + // Average across agents + for (int i = 0; i < num_keys; i++) { + ((float *)&aggregate)[i] /= n; + } + // User populates dict + my_log(dict, env, &aggregate, n); + assign_to_dict(dict, "n", n); + return dict; + } } -static PyObject *env_log(PyObject *self, PyObject *args) { - int num_args = PyTuple_Size(args); - if (num_args != 2) { - PyErr_SetString(PyExc_TypeError, "env_log requires 2 arguments"); +static PyObject *vec_get(PyObject *self, PyObject *args) { + VecEnv *vec = unpack_vecenv(args); + if (!vec) { + PyErr_SetString(PyExc_ValueError, "Invalid VecEnv handle"); return NULL; } - Env *env = unpack_env(args); - if (!env) { + PyObject *list = PyList_New(vec->num_envs); + if (!list) return NULL; - } - // Aggregate this env's per-agent logs (same as vec_log but for one env) - // Note: breaks horribly if you don't use floats - Log aggregate = {0}; - int num_keys = sizeof(Log) / sizeof(float); - for (int j = 0; j < num_keys; j++) { - ((float *)&aggregate)[j] += ((float *)&env->log)[j]; - } - - PyObject *dict = PyDict_New(); - if (aggregate.n == 0.0f) { - return dict; - } - - // Average across agents in env - float n = aggregate.n; - for (int i = 0; i < num_keys; i++) { - ((float *)&aggregate)[i] /= n; + for (int i = 0; i < vec->num_envs; i++) { + Env *env = vec->envs[i]; + if (!env) { + Py_INCREF(Py_None); + PyList_SetItem(list, i, Py_None); + continue; + } + PyObject *dict = PyDict_New(); + if (!dict) { + Py_DECREF(list); + return NULL; + } + PyObject *res = my_get(dict, env); + if (res == NULL) { + Py_DECREF(dict); + Py_DECREF(list); + return NULL; + } + /* my_get returns the dict (or NULL on error) */ + PyList_SetItem(list, i, dict); } - aggregate.n = (float)env->active_agent_count; - - my_log(dict, &aggregate); - return dict; + return list; } static PyObject *vec_close(PyObject *self, PyObject *args) { @@ -694,19 +717,6 @@ static PyObject *vec_close(PyObject *self, PyObject *args) { Py_RETURN_NONE; } -static PyObject *vec_get_scenario_ids(PyObject *self, PyObject *args) { - VecEnv *vec = unpack_vecenv(args); - if (!vec) - return NULL; - - PyObject *list = PyList_New(vec->num_envs); - for (int i = 0; i < vec->num_envs; i++) { - // scenario_id is char[16], may not be null-terminated at byte 16 - PyList_SET_ITEM(list, i, PyUnicode_FromStringAndSize(vec->envs[i]->scenario_id, 16)); - } - return list; -} - static PyObject *get_global_agent_state(PyObject *self, PyObject *args) { if (PyTuple_Size(args) != 7) { PyErr_SetString(PyExc_TypeError, "get_global_agent_state requires 7 arguments"); @@ -807,8 +817,8 @@ static PyObject *vec_get_global_agent_state(PyObject *self, PyObject *args) { } static PyObject *get_ground_truth_trajectories(PyObject *self, PyObject *args) { - if (PyTuple_Size(args) != 9) { - PyErr_SetString(PyExc_TypeError, "get_ground_truth_trajectories requires 9 arguments"); + if (PyTuple_Size(args) != 7) { + PyErr_SetString(PyExc_TypeError, "get_ground_truth_trajectories requires 7 arguments"); return NULL; } @@ -826,13 +836,10 @@ static PyObject *get_ground_truth_trajectories(PyObject *self, PyObject *args) { PyObject *heading_arr = PyTuple_GetItem(args, 4); PyObject *valid_arr = PyTuple_GetItem(args, 5); PyObject *id_arr = PyTuple_GetItem(args, 6); - PyObject *is_vehicle_arr = PyTuple_GetItem(args, 7); - PyObject *is_track_to_predict_arr = PyTuple_GetItem(args, 8); - PyObject *scenario_id_arr = PyTuple_GetItem(args, 9); + PyObject *scenario_id_arr = PyTuple_GetItem(args, 7); if (!PyArray_Check(x_arr) || !PyArray_Check(y_arr) || !PyArray_Check(z_arr) || !PyArray_Check(heading_arr) || - !PyArray_Check(valid_arr) || !PyArray_Check(id_arr) || !PyArray_Check(is_vehicle_arr) || - !PyArray_Check(is_track_to_predict_arr) || !PyArray_Check(scenario_id_arr)) { + !PyArray_Check(valid_arr) || !PyArray_Check(id_arr) || !PyArray_Check(scenario_id_arr)) { PyErr_SetString(PyExc_TypeError, "All output arrays must be NumPy arrays"); return NULL; } @@ -843,19 +850,17 @@ static PyObject *get_ground_truth_trajectories(PyObject *self, PyObject *args) { float *heading_data = (float *)PyArray_DATA((PyArrayObject *)heading_arr); int *valid_data = (int *)PyArray_DATA((PyArrayObject *)valid_arr); int *id_data = (int *)PyArray_DATA((PyArrayObject *)id_arr); - bool *is_vehicle_data = (bool *)PyArray_DATA((PyArrayObject *)is_vehicle_arr); - bool *is_track_to_predict_data = (bool *)PyArray_DATA((PyArrayObject *)is_track_to_predict_arr); - char *scenario_id_data = (char *)PyArray_DATA((PyArrayObject *)scenario_id_arr); + int *scenario_id_data = (int *)PyArray_DATA((PyArrayObject *)scenario_id_arr); c_get_global_ground_truth_trajectories(drive, x_data, y_data, z_data, heading_data, valid_data, id_data, - is_vehicle_data, is_track_to_predict_data, scenario_id_data); + scenario_id_data); Py_RETURN_NONE; } static PyObject *vec_get_global_ground_truth_trajectories(PyObject *self, PyObject *args) { - if (PyTuple_Size(args) != 10) { - PyErr_SetString(PyExc_TypeError, "vec_get_global_ground_truth_trajectories requires 10 arguments"); + if (PyTuple_Size(args) != 8) { + PyErr_SetString(PyExc_TypeError, "vec_get_global_ground_truth_trajectories requires 8 arguments"); return NULL; } @@ -871,13 +876,10 @@ static PyObject *vec_get_global_ground_truth_trajectories(PyObject *self, PyObje PyObject *heading_arr = PyTuple_GetItem(args, 4); PyObject *valid_arr = PyTuple_GetItem(args, 5); PyObject *id_arr = PyTuple_GetItem(args, 6); - PyObject *is_vehicle_arr = PyTuple_GetItem(args, 7); - PyObject *is_track_to_predict_arr = PyTuple_GetItem(args, 8); - PyObject *scenario_id_arr = PyTuple_GetItem(args, 9); + PyObject *scenario_id_arr = PyTuple_GetItem(args, 7); if (!PyArray_Check(x_arr) || !PyArray_Check(y_arr) || !PyArray_Check(z_arr) || !PyArray_Check(heading_arr) || - !PyArray_Check(valid_arr) || !PyArray_Check(id_arr) || !PyArray_Check(is_vehicle_arr) || - !PyArray_Check(is_track_to_predict_arr) || !PyArray_Check(scenario_id_arr)) { + !PyArray_Check(valid_arr) || !PyArray_Check(id_arr) || !PyArray_Check(scenario_id_arr)) { PyErr_SetString(PyExc_TypeError, "All output arrays must be NumPy arrays"); return NULL; } @@ -888,8 +890,6 @@ static PyObject *vec_get_global_ground_truth_trajectories(PyObject *self, PyObje PyArrayObject *heading_array = (PyArrayObject *)heading_arr; PyArrayObject *valid_array = (PyArrayObject *)valid_arr; PyArrayObject *id_array = (PyArrayObject *)id_arr; - PyArrayObject *is_vehicle_array = (PyArrayObject *)is_vehicle_arr; - PyArrayObject *is_track_to_predict_array = (PyArrayObject *)is_track_to_predict_arr; PyArrayObject *scenario_id_array = (PyArrayObject *)scenario_id_arr; // Get base pointers to the arrays @@ -899,9 +899,7 @@ static PyObject *vec_get_global_ground_truth_trajectories(PyObject *self, PyObje float *heading_base = (float *)PyArray_DATA(heading_array); int *valid_base = (int *)PyArray_DATA(valid_array); int *id_base = (int *)PyArray_DATA(id_array); - bool *is_vehicle_base = (bool *)PyArray_DATA(is_vehicle_array); - bool *is_track_to_predict_base = (bool *)PyArray_DATA(is_track_to_predict_array); - char *scenario_id_base = (char *)PyArray_DATA(scenario_id_array); + int *scenario_id_base = (int *)PyArray_DATA(scenario_id_array); // Get number of timesteps from array shape npy_intp *x_shape = PyArray_DIMS(x_array); @@ -914,10 +912,9 @@ static PyObject *vec_get_global_ground_truth_trajectories(PyObject *self, PyObje for (int i = 0; i < vec->num_envs; i++) { Drive *drive = (Drive *)vec->envs[i]; - c_get_global_ground_truth_trajectories( - drive, &x_base[traj_offset], &y_base[traj_offset], &z_base[traj_offset], &heading_base[traj_offset], - &valid_base[traj_offset], &id_base[agent_offset], &is_vehicle_base[agent_offset], - &is_track_to_predict_base[agent_offset], &scenario_id_base[agent_offset * 16]); + c_get_global_ground_truth_trajectories(drive, &x_base[traj_offset], &y_base[traj_offset], &z_base[traj_offset], + &heading_base[traj_offset], &valid_base[traj_offset], + &id_base[agent_offset], &scenario_id_base[agent_offset]); // Move offsets forward agent_offset += drive->active_agent_count; @@ -967,7 +964,7 @@ static PyObject *vec_get_road_edge_polylines(PyObject *self, PyObject *args) { float *x_base = (float *)PyArray_DATA((PyArrayObject *)x_arr); float *y_base = (float *)PyArray_DATA((PyArrayObject *)y_arr); int *lengths_base = (int *)PyArray_DATA((PyArrayObject *)lengths_arr); - char *scenario_ids_base = (char *)PyArray_DATA((PyArrayObject *)scenario_ids_arr); + int *scenario_ids_base = (int *)PyArray_DATA((PyArrayObject *)scenario_ids_arr); int poly_offset = 0, pt_offset = 0; for (int i = 0; i < vec->num_envs; i++) { @@ -975,7 +972,7 @@ static PyObject *vec_get_road_edge_polylines(PyObject *self, PyObject *args) { int np, tp; c_get_road_edge_counts(drive, &np, &tp); c_get_road_edge_polylines(drive, &x_base[pt_offset], &y_base[pt_offset], &lengths_base[poly_offset], - &scenario_ids_base[poly_offset * 16]); + &scenario_ids_base[poly_offset]); poly_offset += np; pt_offset += tp; } @@ -1046,7 +1043,6 @@ static PyMethodDef methods[] = { {"env_close", env_close, METH_VARARGS, "Close the environment"}, {"env_get", env_get, METH_VARARGS, "Get the environment state"}, {"env_put", (PyCFunction)env_put, METH_VARARGS | METH_KEYWORDS, "Put stuff into env"}, - {"env_log", env_log, METH_VARARGS, "Log stats for a single environment"}, {"vectorize", vectorize, METH_VARARGS, "Make a vector of environment handles"}, {"vec_init", (PyCFunction)vec_init, METH_VARARGS | METH_KEYWORDS, "Initialize a vector of environments"}, {"vec_reset", vec_reset, METH_VARARGS, "Reset the vector of environments"}, @@ -1054,7 +1050,7 @@ static PyMethodDef methods[] = { {"vec_log", vec_log, METH_VARARGS, "Log the vector of environments"}, {"vec_render", vec_render, METH_VARARGS, "Render the vector of environments"}, {"vec_close", vec_close, METH_VARARGS, "Close the vector of environments"}, - {"vec_get_scenario_ids", vec_get_scenario_ids, METH_VARARGS, "Get scenario IDs for all envs"}, + {"vec_get", vec_get, METH_VARARGS, "Get attributes from each env in a VecEnv"}, {"shared", (PyCFunction)my_shared, METH_VARARGS | METH_KEYWORDS, "Shared state"}, {"get_global_agent_state", get_global_agent_state, METH_VARARGS, "Get global agent state"}, {"vec_get_global_agent_state", vec_get_global_agent_state, METH_VARARGS, "Get agent state from vectorized env"}, @@ -1080,15 +1076,22 @@ PyMODINIT_FUNC PyInit_binding(void) { } // Make constants accessible from Python - PyModule_AddIntConstant(m, "MAX_ROAD_SEGMENT_OBSERVATIONS", MAX_ROAD_SEGMENT_OBSERVATIONS); - PyModule_AddIntConstant(m, "MAX_AGENTS", MAX_AGENTS); - PyModule_AddIntConstant(m, "TRAJECTORY_LENGTH", TRAJECTORY_LENGTH); PyModule_AddIntConstant(m, "MAX_ENTITIES_PER_CELL", MAX_ENTITIES_PER_CELL); - PyModule_AddIntConstant(m, "ROAD_FEATURES", ROAD_FEATURES); PyModule_AddIntConstant(m, "PARTNER_FEATURES", PARTNER_FEATURES); + PyModule_AddIntConstant(m, "TRAFFIC_LIGHT_FEATURES", TRAFFIC_LIGHT_FEATURES); + PyModule_AddIntConstant(m, "NUM_TRAFFIC_LIGHT_STATES", NUM_TRAFFIC_LIGHT_STATES); + PyModule_AddIntConstant(m, "STOP_SIGN_FEATURES", STOP_SIGN_FEATURES); PyModule_AddIntConstant(m, "EGO_FEATURES_CLASSIC", EGO_FEATURES_CLASSIC); PyModule_AddIntConstant(m, "EGO_FEATURES_JERK", EGO_FEATURES_JERK); + PyModule_AddIntConstant(m, "STATIC_TARGET_FEATURES", STATIC_TARGET_FEATURES); + PyModule_AddIntConstant(m, "DYNAMIC_TARGET_FEATURES", DYNAMIC_TARGET_FEATURES); + PyModule_AddIntConstant(m, "MAX_TARGET_WAYPOINTS", MAX_TARGET_WAYPOINTS); + PyModule_AddIntConstant(m, "NUM_REWARD_COEFS", NUM_REWARD_COEFS); + PyModule_AddIntConstant(m, "TARGET_STATIC", TARGET_STATIC); + PyModule_AddIntConstant(m, "TARGET_DYNAMIC", TARGET_DYNAMIC); + PyObject_SetAttrString(m, "MULTI_LANE_FULL_SCORE_TIME", PyFloat_FromDouble(MULTI_LANE_FULL_SCORE_TIME)); + PyObject_SetAttrString(m, "MULTI_LANE_HALF_SCORE_TIME", PyFloat_FromDouble(MULTI_LANE_HALF_SCORE_TIME)); return m; } diff --git a/pufferlib/ocean/env_config.h b/pufferlib/ocean/env_config.h index 10f592077f..a709aec316 100644 --- a/pufferlib/ocean/env_config.h +++ b/pufferlib/ocean/env_config.h @@ -8,29 +8,48 @@ // Config struct for parsing INI files - contains all environment configuration typedef struct { - int render_mode; int action_type; int dynamics_model; float reward_vehicle_collision; float reward_offroad_collision; + float reward_traffic_light_violation; float reward_goal; - float reward_goal_post_respawn; - float reward_vehicle_collision_post_respawn; + float reward_ade; + float reward_overspeed; + float reward_comfort; + float reward_velocity; + float reward_lane_align; + float reward_vel_align; + float reward_lane_center; + float reward_center_bias; + float reward_timestep; + float reward_reverse; float goal_radius; - float goal_speed; int collision_behavior; int offroad_behavior; - int spawn_immunity_timer; + int traffic_light_behavior; float dt; - int goal_behavior; - float goal_target_distance; - int episode_length; + int target_type; + int scenario_length; int termination_mode; int init_steps; int init_mode; int control_mode; - int max_controlled_agents; + int simulation_mode; char map_dir[256]; + float min_waypoint_spacing; + float max_waypoint_spacing; + int num_target_waypoints; + int reward_conditioning; + int reward_randomization; + int compute_eval_metrics; + int max_agents_per_env; + int use_rear_axle; + int max_lane_segment_observations; + int max_boundary_segment_observations; + int max_partner_observations; + int max_traffic_light_observations; + int max_stop_sign_observations; } env_init_config; // INI file parser handler - parses all environment configuration from drive.ini @@ -56,70 +75,112 @@ static int handler(void *config, const char *section, const char *name, const ch printf("Warning: Unknown dynamics_model value '%s', defaulting to JERK\n", value); env_config->dynamics_model = 1; // Default to JERK } - } else if (MATCH("env", "goal_behavior")) { - env_config->goal_behavior = atoi(value); - } else if (MATCH("env", "goal_target_distance")) { - env_config->goal_target_distance = atof(value); + } else if (MATCH("env", "collision_behavior")) { + env_config->collision_behavior = atoi(value); + } else if (MATCH("env", "offroad_behavior")) { + env_config->offroad_behavior = atoi(value); + } else if (MATCH("env", "traffic_light_behavior")) { + env_config->traffic_light_behavior = atoi(value); + } else if (MATCH("env", "target_type")) { + if (strcmp(value, "\"static\"") == 0 || strcmp(value, "static") == 0) { + env_config->target_type = 0; // TARGET_STATIC + } else if (strcmp(value, "\"dynamic\"") == 0 || strcmp(value, "dynamic") == 0) { + env_config->target_type = 1; // TARGET_DYNAMIC + } else { + printf("Warning: Unknown target_type value '%s', defaulting to static\n", value); + env_config->target_type = 0; + } } else if (MATCH("env", "reward_vehicle_collision")) { env_config->reward_vehicle_collision = atof(value); } else if (MATCH("env", "reward_offroad_collision")) { env_config->reward_offroad_collision = atof(value); + } else if (MATCH("env", "reward_traffic_light_violation")) { + env_config->reward_traffic_light_violation = atof(value); } else if (MATCH("env", "reward_goal")) { env_config->reward_goal = atof(value); - } else if (MATCH("env", "reward_goal_post_respawn")) { - env_config->reward_goal_post_respawn = atof(value); - } else if (MATCH("env", "reward_vehicle_collision_post_respawn")) { - env_config->reward_vehicle_collision_post_respawn = atof(value); + } else if (MATCH("env", "reward_ade")) { + env_config->reward_ade = atof(value); + } else if (MATCH("env", "reward_overspeed")) { + env_config->reward_overspeed = atof(value); + } else if (MATCH("env", "reward_comfort")) { + env_config->reward_comfort = atof(value); + } else if (MATCH("env", "reward_velocity")) { + env_config->reward_velocity = atof(value); + } else if (MATCH("env", "reward_lane_align")) { + env_config->reward_lane_align = atof(value); + } else if (MATCH("env", "reward_vel_align")) { + env_config->reward_vel_align = atof(value); + } else if (MATCH("env", "reward_lane_center")) { + env_config->reward_lane_center = atof(value); + } else if (MATCH("env", "reward_center_bias")) { + env_config->reward_center_bias = atof(value); + } else if (MATCH("env", "reward_timestep")) { + env_config->reward_timestep = atof(value); + } else if (MATCH("env", "reward_reverse")) { + env_config->reward_reverse = atof(value); } else if (MATCH("env", "goal_radius")) { env_config->goal_radius = atof(value); - } else if (MATCH("env", "goal_speed")) { - env_config->goal_speed = atof(value); - } else if (MATCH("env", "collision_behavior")) { - env_config->collision_behavior = atoi(value); - } else if (MATCH("env", "offroad_behavior")) { - env_config->offroad_behavior = atoi(value); - } else if (MATCH("env", "spawn_immunity_timer")) { - env_config->spawn_immunity_timer = atoi(value); } else if (MATCH("env", "dt")) { env_config->dt = atof(value); - } else if (MATCH("env", "episode_length")) { - env_config->episode_length = atoi(value); + } else if (MATCH("env", "scenario_length")) { + env_config->scenario_length = atoi(value); } else if (MATCH("env", "termination_mode")) { env_config->termination_mode = atoi(value); } else if (MATCH("env", "init_steps")) { env_config->init_steps = atoi(value); + } else if (MATCH("env", "max_agents_per_env")) { + env_config->max_agents_per_env = atoi(value); } else if (MATCH("env", "init_mode")) { - if (strcmp(value, "\"create_all_valid\"") == 0 || strcmp(value, "create_all_valid") == 0) { - env_config->init_mode = 0; - } else if (strcmp(value, "\"create_only_controlled\"") == 0 || strcmp(value, "create_only_controlled") == 0) { - env_config->init_mode = 1; - } else { - printf("Warning: Unknown init_mode value '%s', defaulting to CREATE_ALL_VALID\n", value); - env_config->init_mode = 0; // Default to CREATE_ALL_VALID - } + env_config->init_mode = atoi(value); } else if (MATCH("env", "control_mode")) { - if (strcmp(value, "\"control_vehicles\"") == 0 || strcmp(value, "control_vehicles") == 0) { - env_config->control_mode = 0; - } else if (strcmp(value, "\"control_agents\"") == 0 || strcmp(value, "control_agents") == 0) { - env_config->control_mode = 1; - } else if (strcmp(value, "\"control_wosac\"") == 0 || strcmp(value, "control_wosac") == 0) { - env_config->control_mode = 2; - } else if (strcmp(value, "\"control_sdc_only\"") == 0 || strcmp(value, "control_sdc_only") == 0) { - env_config->control_mode = 3; - } else if (strcmp(value, "\"control_mixed_play\"") == 0 || strcmp(value, "control_mixed_play") == 0) { - env_config->control_mode = 4; - } else { - printf("Warning: Unknown control_mode value '%s', defaulting to CONTROL_VEHICLES\n", value); - env_config->control_mode = 0; // Default to CONTROL_VEHICLES - } + env_config->control_mode = atoi(value); + } else if (MATCH("env", "simulation_mode")) { + env_config->simulation_mode = atoi(value); } else if (MATCH("env", "map_dir")) { if (sscanf(value, "\"%255[^\"]\"", env_config->map_dir) != 1) { strncpy(env_config->map_dir, value, sizeof(env_config->map_dir) - 1); env_config->map_dir[sizeof(env_config->map_dir) - 1] = '\0'; } - // printf("Parsed map_dir: '%s'\n", env_config->map_dir); - } else if (MATCH("env", "max_controlled_agents")) { - env_config->max_controlled_agents = atoi(value); + } else if (MATCH("env", "min_waypoint_spacing")) { + env_config->min_waypoint_spacing = atof(value); + } else if (MATCH("env", "max_waypoint_spacing")) { + env_config->max_waypoint_spacing = atof(value); + } else if (MATCH("env", "num_target_waypoints")) { + env_config->num_target_waypoints = atoi(value); + } else if (MATCH("env", "reward_conditioning")) { + if (strcmp(value, "True") == 0 || strcmp(value, "true") == 0 || strcmp(value, "1") == 0) { + env_config->reward_conditioning = 1; + } else { + env_config->reward_conditioning = 0; + } + } else if (MATCH("env", "reward_randomization")) { + if (strcmp(value, "True") == 0 || strcmp(value, "true") == 0 || strcmp(value, "1") == 0) { + env_config->reward_randomization = 1; + } else { + env_config->reward_randomization = 0; + } + } else if (MATCH("env", "compute_eval_metrics")) { + if (strcmp(value, "True") == 0 || strcmp(value, "true") == 0 || strcmp(value, "1") == 0) { + env_config->compute_eval_metrics = 1; + } else { + env_config->compute_eval_metrics = 0; + } + } else if (MATCH("env", "use_rear_axle")) { + if (strcmp(value, "True") == 0 || strcmp(value, "true") == 0 || strcmp(value, "1") == 0) { + env_config->use_rear_axle = 1; + } else { + env_config->use_rear_axle = 0; + } + } else if (MATCH("env", "max_boundary_segment_observations")) { + env_config->max_boundary_segment_observations = atoi(value); + } else if (MATCH("env", "max_lane_segment_observations")) { + env_config->max_lane_segment_observations = atoi(value); + } else if (MATCH("env", "max_partner_observations")) { + env_config->max_partner_observations = atoi(value); + } else if (MATCH("env", "max_traffic_light_observations")) { + env_config->max_traffic_light_observations = atoi(value); + } else if (MATCH("env", "max_stop_sign_observations")) { + env_config->max_stop_sign_observations = atoi(value); } else { return 0; // Unknown section/name, indicate failure to handle } @@ -128,4 +189,8 @@ static int handler(void *config, const char *section, const char *name, const ch return 1; } +static int load_env_config(const char *ini_file, env_init_config *config) { + return ini_parse(ini_file, handler, config); +} + #endif // ENV_CONFIG_H diff --git a/pufferlib/ocean/environment.py b/pufferlib/ocean/environment.py index feba8bead2..a4843effcf 100644 --- a/pufferlib/ocean/environment.py +++ b/pufferlib/ocean/environment.py @@ -1,189 +1,7 @@ import importlib -import pufferlib.emulation +import pufferlib - -def lazy_import(module_path, attr): - """ - Returns a callable that, when called with any arguments, will - import the module, retrieve the attribute (usually a class or factory) - and then call it with the given arguments. - """ - return lambda *args, **kwargs: getattr(__import__(module_path, fromlist=[attr]), attr)(*args, **kwargs) - - -def make_foraging( - width=1080, height=720, num_agents=4096, horizon=512, discretize=True, food_reward=0.1, render_mode="rgb_array" -): - from .grid import grid - - init_fn = grid.init_foraging - reward_fn = grid.reward_foraging - return grid.PufferGrid( - width, - height, - num_agents, - horizon, - discretize=discretize, - food_reward=food_reward, - init_fn=init_fn, - reward_fn=reward_fn, - render_mode=render_mode, - ) - - -def make_predator_prey( - width=1080, height=720, num_agents=4096, horizon=512, discretize=True, food_reward=0.1, render_mode="rgb_array" -): - from .grid import grid - - init_fn = grid.init_predator_prey - reward_fn = grid.reward_predator_prey - return grid.PufferGrid( - width, - height, - num_agents, - horizon, - discretize=discretize, - food_reward=food_reward, - init_fn=init_fn, - reward_fn=reward_fn, - render_mode=render_mode, - ) - - -def make_group( - width=1080, height=720, num_agents=4096, horizon=512, discretize=True, food_reward=0.1, render_mode="rgb_array" -): - from .grid import grid - - init_fn = grid.init_group - reward_fn = grid.reward_group - return grid.PufferGrid( - width, - height, - num_agents, - horizon, - discretize=discretize, - food_reward=food_reward, - init_fn=init_fn, - reward_fn=reward_fn, - render_mode=render_mode, - ) - - -def make_puffer( - width=1080, height=720, num_agents=4096, horizon=512, discretize=True, food_reward=0.1, render_mode="rgb_array" -): - from .grid import grid - - init_fn = grid.init_puffer - reward_fn = grid.reward_puffer - return grid.PufferGrid( - width, - height, - num_agents, - horizon, - discretize=discretize, - food_reward=food_reward, - init_fn=init_fn, - reward_fn=reward_fn, - render_mode=render_mode, - ) - - -def make_puffergrid( - render_mode="raylib", vision_range=5, num_envs=4096, num_maps=1000, max_map_size=9, report_interval=128, buf=None -): - return PufferGrid(render_mode, vision_range, num_envs, num_maps, max_map_size, report_interval, buf) - - -def make_continuous(discretize=False, buf=None, **kwargs): - from . import sanity - - env = sanity.Continuous(discretize=discretize) - if not discretize: - env = pufferlib.ClipAction(env) - env = pufferlib.EpisodeStats(env) - return pufferlib.emulation.GymnasiumPufferEnv(env=env, buf=buf) - - -def make_squared(distance_to_target=3, num_targets=1, buf=None, **kwargs): - from . import sanity - - env = sanity.Squared(distance_to_target=distance_to_target, num_targets=num_targets, **kwargs) - env = pufferlib.EpisodeStats(env) - return pufferlib.emulation.GymnasiumPufferEnv(env=env, buf=buf, **kwargs) - - -def make_bandit(num_actions=10, reward_scale=1, reward_noise=1, buf=None): - from . import sanity - - env = sanity.Bandit(num_actions=num_actions, reward_scale=reward_scale, reward_noise=reward_noise) - env = pufferlib.EpisodeStats(env) - return pufferlib.emulation.GymnasiumPufferEnv(env=env, buf=buf) - - -def make_memory(mem_length=2, mem_delay=2, buf=None, **kwargs): - from . import sanity - - env = sanity.Memory(mem_length=mem_length, mem_delay=mem_delay) - env = pufferlib.EpisodeStats(env) - return pufferlib.emulation.GymnasiumPufferEnv(env=env, buf=buf) - - -def make_password(password_length=5, buf=None, **kwargs): - from . import sanity - - env = sanity.Password(password_length=password_length) - env = pufferlib.EpisodeStats(env) - return pufferlib.emulation.GymnasiumPufferEnv(env=env, buf=buf) - - -def make_performance(delay_mean=0, delay_std=0, bandwidth=1, buf=None, **kwargs): - from . import sanity - - env = sanity.Performance(delay_mean=delay_mean, delay_std=delay_std, bandwidth=bandwidth) - env = pufferlib.EpisodeStats(env) - return pufferlib.emulation.GymnasiumPufferEnv(env=env, buf=buf) - - -def make_performance_empiric(count_n=0, count_std=0, bandwidth=1, buf=None, **kwargs): - from . import sanity - - env = sanity.PerformanceEmpiric(count_n=count_n, count_std=count_std, bandwidth=bandwidth) - env = pufferlib.EpisodeStats(env) - return pufferlib.emulation.GymnasiumPufferEnv(env=env, buf=buf) - - -def make_stochastic(p=0.7, horizon=100, buf=None, **kwargs): - from . import sanity - - env = sanity.Stochastic(p=p, horizon=100) - env = pufferlib.EpisodeStats(env) - return pufferlib.emulation.GymnasiumPufferEnv(env=env, buf=buf) - - -def make_spaces(buf=None, **kwargs): - from . import sanity - - env = sanity.Spaces() - env = pufferlib.EpisodeStats(env) - return pufferlib.emulation.GymnasiumPufferEnv(env=env, buf=buf, **kwargs) - - -def make_multiagent(buf=None, **kwargs): - from . import sanity - - env = sanity.Multiagent() - env = pufferlib.MultiagentEpisodeStats(env) - return pufferlib.emulation.PettingZooPufferEnv(env=env, buf=buf) - - -MAKE_FUNCTIONS = { - "drive": "Drive", - "spaces": make_spaces, - "multiagent": make_multiagent, -} +MAKE_FUNCTIONS = {"drive": "Drive"} def env_creator(name="squared", *args, **kwargs): diff --git a/pufferlib/ocean/torch.py b/pufferlib/ocean/torch.py index 0d4d618cea..592e2c94ee 100644 --- a/pufferlib/ocean/torch.py +++ b/pufferlib/ocean/torch.py @@ -4,110 +4,330 @@ import pufferlib import pufferlib.models +from pufferlib.ocean.drive import binding from pufferlib.models import Default as Policy # noqa: F401 from pufferlib.models import Convolutional as Conv # noqa: F401 - Recurrent = pufferlib.models.LSTMWrapper +class DriveBackbone(nn.Module): + """ + Neural network backbone + Architecture features: + - Split Actor/Critic (configurable) + """ + + def _create_encoder(self, in_features, input_size, encoder_gigaflow, dropout=0.0): + if encoder_gigaflow: + return nn.Sequential( + pufferlib.pytorch.layer_init(nn.Linear(in_features, input_size)), + nn.LayerNorm(input_size), + nn.Tanh(), + nn.Dropout(dropout), + pufferlib.pytorch.layer_init(nn.Linear(input_size, input_size)), + ) + else: + return nn.Sequential( + pufferlib.pytorch.layer_init(nn.Linear(in_features, input_size)), + nn.LayerNorm(input_size), + pufferlib.pytorch.layer_init(nn.Linear(input_size, input_size)), + ) + + def __init__( + self, + env, + input_size, + backbone_hidden_size, + backbone_num_layers, + ego_dim, + encoder_gigaflow, + dropout, + ): + super().__init__() + + # Observation dimensions from environment config + self.max_partner_observations = env.max_partner_observations + self.partner_features_count = env.partner_features + # Road features size (lanes + boundaries) + self.max_lane_segment_observations = env.max_lane_segment_observations + self.max_boundary_segment_observations = env.max_boundary_segment_observations + self.road_features_count = env.road_features + # Traffic light size + self.max_traffic_light_observations = env.max_traffic_light_observations + self.traffic_light_features_count = env.traffic_light_features + self.traffic_light_features_after_onehot = env.traffic_light_features + (binding.NUM_TRAFFIC_LIGHT_STATES - 1) + # Stop sign size + self.max_stop_sign_observations = env.max_stop_sign_observations + self.stop_sign_features_count = env.stop_sign_features + # Conditioning size (reward coefficients + target info) + self.conditioning_dim = env.num_reward_coefs + env.target_dim + + num_feature_sets = 1 + + # 1. observations Encoders + # Each encoder projects raw features into a common input_size embedding space + self.ego_encoder = self._create_encoder(ego_dim, input_size, encoder_gigaflow) + if self.max_lane_segment_observations > 0: + self.lane_encoder = self._create_encoder( + self.road_features_count, + input_size, + encoder_gigaflow, + dropout=dropout, + ) + num_feature_sets += 1 + if self.max_boundary_segment_observations > 0: + self.boundary_encoder = self._create_encoder( + self.road_features_count, + input_size, + encoder_gigaflow, + dropout=dropout, + ) + num_feature_sets += 1 + if self.max_partner_observations > 0: + self.partner_encoder = self._create_encoder(self.partner_features_count, input_size, encoder_gigaflow) + num_feature_sets += 1 + if self.max_traffic_light_observations > 0: + self.traffic_light_encoder = self._create_encoder( + self.traffic_light_features_after_onehot, + input_size, + encoder_gigaflow, + ) + num_feature_sets += 1 + if self.max_stop_sign_observations > 0: + self.stop_sign_encoder = self._create_encoder( + self.stop_sign_features_count, + input_size, + encoder_gigaflow, + ) + num_feature_sets += 1 + if self.conditioning_dim > 0: + self.conditioning_encoder = self._create_encoder(self.conditioning_dim, input_size, encoder_gigaflow) + num_feature_sets += 1 + + # 2. Main Backbone MLP + backbone_layers = [] + bb_in = num_feature_sets * input_size + for _ in range(backbone_num_layers): + backbone_layers.append(nn.GELU()) + backbone_layers.append(pufferlib.pytorch.layer_init(nn.Linear(bb_in, backbone_hidden_size))) + bb_in = backbone_hidden_size + # Add final GELU before heads + backbone_layers.append(nn.GELU()) + self.backbone = nn.Sequential(*backbone_layers) + self.out_dim = backbone_hidden_size if backbone_num_layers > 0 else num_feature_sets * input_size + + def forward(self, observations, ego_dim): + # Extract and slice observations from the flat buffer + partner_dim = self.max_partner_observations * self.partner_features_count + lane_dim = self.max_lane_segment_observations * self.road_features_count + boundary_dim = self.max_boundary_segment_observations * self.road_features_count + traffic_light_dim = self.max_traffic_light_observations * self.traffic_light_features_count + stop_sign_dim = self.max_stop_sign_observations * self.stop_sign_features_count + + slide_idx = ego_dim + ego_observations = observations[:, :slide_idx] + + conditioning_observations = observations[:, slide_idx : slide_idx + self.conditioning_dim] + slide_idx += self.conditioning_dim + + partner_observations = observations[:, slide_idx : slide_idx + partner_dim] + slide_idx += partner_dim + + lane_observations = observations[:, slide_idx : slide_idx + lane_dim] + slide_idx += lane_dim + + boundary_observations = observations[:, slide_idx : slide_idx + boundary_dim] + slide_idx += boundary_dim + + traffic_light_observations = observations[:, slide_idx : slide_idx + traffic_light_dim] + slide_idx += traffic_light_dim + + stop_sign_observations = observations[:, slide_idx : slide_idx + stop_sign_dim] + + # Encode Ego State + ego_features = self.ego_encoder(ego_observations) + + feature_list = [ego_features] + + # Encode Lanes and Boundaries separately + if self.max_lane_segment_observations > 0: + lane_objects = lane_observations.view(-1, self.max_lane_segment_observations, self.road_features_count) + lane_features, _ = self.lane_encoder(lane_objects).max(dim=1) + feature_list.append(lane_features) + if self.max_boundary_segment_observations > 0: + boundary_objects = boundary_observations.view( + -1, self.max_boundary_segment_observations, self.road_features_count + ) + + boundary_features, _ = self.boundary_encoder(boundary_objects).max(dim=1) + feature_list.append(boundary_features) + + # Encode Partners + if self.max_partner_observations > 0: + partner_objects = partner_observations.view(-1, self.max_partner_observations, self.partner_features_count) + partner_encoded = self.partner_encoder(partner_objects) + partner_features, _ = partner_encoded.max(dim=1) + feature_list.append(partner_features) + + # Encode Traffic lights + if self.max_traffic_light_observations > 0: + traffic_light_objects = traffic_light_observations.view( + -1, self.max_traffic_light_observations, self.traffic_light_features_count + ) + traffic_light_continuous = traffic_light_objects[:, :, : self.traffic_light_features_count - 1] + traffic_light_categorical = traffic_light_objects[:, :, self.traffic_light_features_count - 1] + traffic_light_onehot = F.one_hot( + traffic_light_categorical.long(), + num_classes=binding.NUM_TRAFFIC_LIGHT_STATES, + ) + traffic_light_objects = torch.cat([traffic_light_continuous, traffic_light_onehot], dim=2) + traffic_light_features, _ = self.traffic_light_encoder(traffic_light_objects).max(dim=1) + feature_list.append(traffic_light_features) + + # Encode Stop Signs + if self.max_stop_sign_observations > 0: + stop_sign_objects = stop_sign_observations.view( + -1, self.max_stop_sign_observations, self.stop_sign_features_count + ) + stop_sign_features, _ = self.stop_sign_encoder(stop_sign_objects).max(dim=1) + feature_list.append(stop_sign_features) + + # Add optional features if enabled + if self.conditioning_dim > 0: + conditioning_features = self.conditioning_encoder(conditioning_observations) + feature_list.append(conditioning_features) + + # Concatenate all features and pass through main backbone + concat_features = torch.cat(feature_list, dim=1) + return self.backbone(concat_features) + + class Drive(nn.Module): - def __init__(self, env, input_size=128, hidden_size=128, **kwargs): + def __init__( + self, + env, + input_size: int, + backbone_hidden_size: int, + backbone_num_layers: int, + actor_hidden_size: int, + actor_num_layers: int, + critic_hidden_size: int, + critic_num_layers: int, + encoder_gigaflow: bool, + dropout: int, + split_network: bool, + ): super().__init__() - self.hidden_size = hidden_size - self.observation_size = env.single_observation_space.shape[0] - self.max_partner_objects = env.max_partner_objects - self.partner_features = env.partner_features - self.max_road_objects = env.max_road_objects - self.road_features = env.road_features - self.road_features_after_onehot = env.road_features + 6 # 6 is the number of one-hot encoded categories + + # Configuration flags from policy kwargs + self.split_network = split_network self.ego_dim = env.ego_features - self.ego_encoder = nn.Sequential( - pufferlib.pytorch.layer_init(nn.Linear(self.ego_dim, input_size)), - nn.LayerNorm(input_size), - # nn.ReLU(), - pufferlib.pytorch.layer_init(nn.Linear(input_size, input_size)), - ) - - self.road_encoder = nn.Sequential( - pufferlib.pytorch.layer_init(nn.Linear(self.road_features_after_onehot, input_size)), - nn.LayerNorm(input_size), - # nn.ReLU(), - pufferlib.pytorch.layer_init(nn.Linear(input_size, input_size)), - ) - - self.partner_encoder = nn.Sequential( - pufferlib.pytorch.layer_init(nn.Linear(self.partner_features, input_size)), - nn.LayerNorm(input_size), - # nn.ReLU(), - pufferlib.pytorch.layer_init(nn.Linear(input_size, input_size)), - ) - - self.shared_embedding = nn.Sequential( - nn.GELU(), - pufferlib.pytorch.layer_init(nn.Linear(3 * input_size, hidden_size)), - ) - self.is_continuous = isinstance(env.single_action_space, pufferlib.spaces.Box) + # Prepare arguments for the Backbone + backbone_args = { + "env": env, + "input_size": input_size, + "backbone_hidden_size": backbone_hidden_size, + "backbone_num_layers": backbone_num_layers, + "ego_dim": self.ego_dim, + "encoder_gigaflow": encoder_gigaflow, + "dropout": dropout, + } + + # Instantiate backbones + self.actor_backbone = DriveBackbone(**backbone_args) + # If split_network is True, create a separate backbone for the critic. + # Otherwise, share the same backbone for both. + if self.split_network: + self.critic_backbone = DriveBackbone(**backbone_args) + else: + self.critic_backbone = self.actor_backbone + + # Setup action and value heads + self.is_continuous = isinstance(env.single_action_space, pufferlib.spaces.Box) if self.is_continuous: self.atn_dim = (env.single_action_space.shape[0],) * 2 else: self.atn_dim = env.single_action_space.nvec.tolist() - self.actor = pufferlib.pytorch.layer_init(nn.Linear(hidden_size, sum(self.atn_dim)), std=0.01) - self.value_fn = pufferlib.pytorch.layer_init(nn.Linear(hidden_size, 1), std=1) + # n-layer MLP for actor head (num_layers = number of hidden layers) + backbone_out_dim = self.actor_backbone.out_dim + actor_head_layers = [] + actor_in = backbone_out_dim + for _ in range(actor_num_layers): + actor_head_layers.append(pufferlib.pytorch.layer_init(nn.Linear(actor_in, actor_hidden_size))) + actor_head_layers.append(nn.ReLU()) + actor_in = actor_hidden_size + actor_head_layers.append(pufferlib.pytorch.layer_init(nn.Linear(actor_in, sum(self.atn_dim)), std=0.01)) + self.actor_head = nn.Sequential(*actor_head_layers) + + # n-layer MLP for critic head (num_layers = number of hidden layers) + critic_head_layers = [] + critic_in = backbone_out_dim + for _ in range(critic_num_layers): + critic_head_layers.append(pufferlib.pytorch.layer_init(nn.Linear(critic_in, critic_hidden_size))) + critic_head_layers.append(nn.ReLU()) + critic_in = critic_hidden_size + critic_head_layers.append(pufferlib.pytorch.layer_init(nn.Linear(critic_in, 1), std=1)) + self.critic_head = nn.Sequential(*critic_head_layers) def forward(self, observations, state=None): - hidden = self.encode_observations(observations) - actions, value = self.decode_actions(hidden) + """ + Forward pass handling both Actor and Critic inference. + """ + # Forward pass for actor + actor_hidden = self.actor_backbone(observations, self.ego_dim) + + # Forward pass for critic (may use separate backbone) + if self.split_network: + critic_hidden = self.critic_backbone(observations, self.ego_dim) + else: + critic_hidden = actor_hidden + + # Compute actions + if self.is_continuous: + params = self.actor_head(actor_hidden) + loc, scale = torch.split(params, self.atn_dim, dim=1) + std = torch.nn.functional.softplus(scale) + 1e-4 + actions = torch.distributions.Normal(loc, std) + else: + actions = torch.split(self.actor_head(actor_hidden), self.atn_dim, dim=1) + + # Compute value + value = self.critic_head(critic_hidden) + return actions, value def forward_train(self, x, state=None): return self.forward(x, state) - def forward_eval(self, observations, state=None): - hidden = self.encode_observations(observations, state=state) - logits, values = self.decode_actions(hidden) - return logits, values + def forward_eval(self, x, state=None): + return self.forward(x, state) + # Required for PufferLib recurrent wrappers def encode_observations(self, observations, state=None): - ego_dim = self.ego_dim - partner_dim = self.max_partner_objects * self.partner_features - road_dim = self.max_road_objects * self.road_features - ego_obs = observations[:, :ego_dim] - partner_obs = observations[:, ego_dim : ego_dim + partner_dim] - road_obs = observations[:, ego_dim + partner_dim : ego_dim + partner_dim + road_dim] - - partner_objects = partner_obs.view(-1, self.max_partner_objects, self.partner_features) - - road_objects = road_obs.view(-1, self.max_road_objects, self.road_features) - road_continuous = road_objects[:, :, : self.road_features - 1] - road_categorical = road_objects[:, :, self.road_features - 1] - road_onehot = F.one_hot(road_categorical.long(), num_classes=7) # Shape: [batch, ROAD_MAX_OBJECTS, 7] - road_objects = torch.cat([road_continuous, road_onehot], dim=2) - ego_features = self.ego_encoder(ego_obs) - partner_features, _ = self.partner_encoder(partner_objects).max(dim=1) - road_features, _ = self.road_encoder(road_objects).max(dim=1) - - concat_features = torch.cat([ego_features, road_features, partner_features], dim=1) - - # Pass through shared embedding - embedding = F.relu(self.shared_embedding(concat_features)) - # embedding = self.shared_embedding(concat_features) - return embedding - - def decode_actions(self, flat_hidden): + assert not self.split_network, "LSTM wrapper doesn't support split_network=True" + return self.actor_backbone(observations, self.ego_dim) + + def decode_actions(self, hidden): + """ + USE ONLY FOR LSTM WRAPPER. + Decodes actions and value from the hidden state. + Args: + hidden: The hidden state for the actor (policy). + """ if self.is_continuous: - parameters = self.actor(flat_hidden) + parameters = self.actor_head(hidden) loc, scale = torch.split(parameters, self.atn_dim, dim=1) std = torch.nn.functional.softplus(scale) + 1e-4 action = torch.distributions.Normal(loc, std) else: - action = self.actor(flat_hidden) + action = self.actor_head(hidden) action = torch.split(action, self.atn_dim, dim=1) - value = self.value_fn(flat_hidden) + value = self.critic_head(hidden) return action, value diff --git a/pufferlib/pufferl.py b/pufferlib/pufferl.py index 2712ab02d1..2f78a42d79 100644 --- a/pufferlib/pufferl.py +++ b/pufferlib/pufferl.py @@ -3,12 +3,18 @@ # Distributed example: torchrun --standalone --nnodes=1 --nproc-per-node=6 -m pufferlib.pufferl train puffer_nmmo3 import contextlib +import copy +import numbers import warnings +import pandas as pd + + warnings.filterwarnings("error", category=RuntimeWarning) import os import sys +import traceback import glob import ast import time @@ -18,9 +24,10 @@ import argparse import importlib import configparser +from datetime import datetime from threading import Thread from collections import defaultdict, deque -from pathlib import Path +import yaml import numpy as np import psutil @@ -28,15 +35,14 @@ import torch import torch.distributed from torch.distributed.elastic.multiprocessing.errors import record -import torch.utils.cpp_extension import pufferlib import pufferlib.sweep import pufferlib.vector import pufferlib.pytorch -import pufferlib.utils +import pufferlib.viz -from pufferlib.ocean.benchmark.evaluator import Evaluator +import mediapy try: from pufferlib import _C @@ -50,6 +56,7 @@ from rich.table import Table from rich.console import Console from rich_argparse import RichHelpFormatter +from tqdm import tqdm rich.traceback.install(show_locals=False) @@ -57,56 +64,59 @@ signal.signal(signal.SIGINT, lambda sig, frame: os._exit(0)) -# Assume advantage kernel has been built if CUDA compiler is available -ADVANTAGE_CUDA = shutil.which("nvcc") is not None +from torch.utils.cpp_extension import CUDA_HOME, ROCM_HOME # noqa: E402 + +# Assume advantage kernel has been built if torch has been compiled with CUDA or HIP support +# and can find CUDA or HIP in the system +ADVANTAGE_CUDA = bool(CUDA_HOME or ROCM_HOME) +HIDDEN_DASHBOARD_METRICS = { + "comfort_score", + "driving_direction_score", + "making_progress_rate", + "multi_lane_score", + "multi_lane_time", + "speed_limit_compliance", + "ttc_within_bound_rate", +} class PuffeRL: - def __init__(self, config, vecenv, policy, logger=None, full_args=None): - self.full_args = full_args + def __init__(self, config, vecenv, policy, logger=None): # Backend perf optimization torch.set_float32_matmul_precision("high") torch.backends.cudnn.deterministic = config["torch_deterministic"] - torch.backends.cudnn.benchmark = True + torch.backends.cudnn.benchmark = not config["torch_deterministic"] + torch.use_deterministic_algorithms(config["torch_deterministic"], warn_only=True) # Reproducibility seed = config["seed"] - # random.seed(seed) - # np.random.seed(seed) - # torch.manual_seed(seed) # Vecenv info vecenv.async_reset(seed) obs_space = vecenv.single_observation_space atn_space = vecenv.single_action_space - # The number of concurrent agents running in the vectorized environments total_agents = vecenv.num_agents self.total_agents = total_agents # Experience - if config["batch_size"] == "auto" and config["rollout_horizon"] == "auto": - raise pufferlib.APIUsageError("Must specify batch_size or rollout_horizon") + if config["batch_size"] == "auto" and config["bptt_horizon"] == "auto": + raise pufferlib.APIUsageError("Must specify batch_size or bptt_horizon") elif config["batch_size"] == "auto": - config["batch_size"] = total_agents * config["rollout_horizon"] - elif config["rollout_horizon"] == "auto": - config["rollout_horizon"] = config["batch_size"] // total_agents + config["batch_size"] = total_agents * config["bptt_horizon"] + elif config["bptt_horizon"] == "auto": + config["bptt_horizon"] = config["batch_size"] // total_agents batch_size = config["batch_size"] - rollout_horizon = config["rollout_horizon"] - bptt_horizon = config["rollout_horizon"] # LSTM backprop horizon - config["bptt_horizon"] = bptt_horizon - segments = batch_size // rollout_horizon # Use rollout_horizon - - # Number of independent rollout sequences stored in the experience buffer + horizon = config["bptt_horizon"] + segments = batch_size // horizon self.segments = segments - if total_agents > segments: raise pufferlib.APIUsageError(f"Total agents {total_agents} <= segments {segments}") device = config["device"] self.observations = torch.zeros( segments, - rollout_horizon, + horizon, *obs_space.shape, dtype=pufferlib.pytorch.numpy_to_torch_dtype_dict[obs_space.dtype], pin_memory=device == "cuda" and config["cpu_offload"], @@ -114,21 +124,27 @@ def __init__(self, config, vecenv, policy, logger=None, full_args=None): ) self.actions = torch.zeros( segments, - rollout_horizon, + horizon, *atn_space.shape, device=device, dtype=pufferlib.pytorch.numpy_to_torch_dtype_dict[atn_space.dtype], ) - self.values = torch.zeros(segments, rollout_horizon, device=device) - self.logprobs = torch.zeros(segments, rollout_horizon, device=device) - self.rewards = torch.zeros(segments, rollout_horizon, device=device) - self.terminals = torch.zeros(segments, rollout_horizon, device=device) - self.truncations = torch.zeros(segments, rollout_horizon, device=device) - self.ratio = torch.ones(segments, rollout_horizon, device=device) - self.importance = torch.ones(segments, rollout_horizon, device=device) + self.values = torch.zeros(segments, horizon, device=device) + self.logprobs = torch.zeros(segments, horizon, device=device) + self.rewards = torch.zeros(segments, horizon, device=device) + self.terminals = torch.zeros(segments, horizon, device=device) + self.truncations = torch.zeros(segments, horizon, device=device) + self.ratio = torch.ones(segments, horizon, device=device) + self.importance = torch.ones(segments, horizon, device=device) + self.masks = torch.zeros(segments, horizon, device=device, dtype=torch.bool) self.ep_lengths = torch.zeros(total_agents, device=device, dtype=torch.int32) self.ep_indices = torch.arange(total_agents, device=device, dtype=torch.int32) self.free_idx = total_agents + self.render = config["render"] + self.render_interval = config["render_interval"] + + if self.render: + ensure_drive_binary() # LSTM if config["use_rnn"]: @@ -151,10 +167,10 @@ def __init__(self, config, vecenv, policy, logger=None, full_args=None): self.accumulate_minibatches = max(1, minibatch_size // max_minibatch_size) self.total_minibatches = int(config["update_epochs"] * batch_size / self.minibatch_size) - self.minibatch_segments = self.minibatch_size // rollout_horizon - if self.minibatch_segments * rollout_horizon != self.minibatch_size: + self.minibatch_segments = self.minibatch_size // horizon + if self.minibatch_segments * horizon != self.minibatch_size: raise pufferlib.APIUsageError( - f"minibatch_size {self.minibatch_size} must be divisible by bptt_horizon {rollout_horizon}" + f"minibatch_size {self.minibatch_size} must be divisible by bptt_horizon {horizon}" ) # Torch compile @@ -175,18 +191,32 @@ def __init__(self, config, vecenv, policy, logger=None, full_args=None): betas=(config["adam_beta1"], config["adam_beta2"]), eps=config["adam_eps"], ) + elif config["optimizer"] == "adamw": + optimizer = torch.optim.AdamW( + self.policy.parameters(), + lr=config["learning_rate"], + betas=(config["adam_beta1"], config["adam_beta2"]), + eps=config["adam_eps"], + ) elif config["optimizer"] == "muon": + import heavyball from heavyball import ForeachMuon warnings.filterwarnings(action="ignore", category=UserWarning, module=r"heavyball.*") - import heavyball.utils + heavyball.utils.compile_mode = "default" + + # # optionally a little bit better/faster alternative to newtonschulz iteration + # import heavyball.utils + # heavyball.utils.zeroth_power_mode = 'thinky_polar_express' - heavyball.utils.compile_mode = config["compile_mode"] if config["compile"] else None + # heavyball_momentum=True introduced in heavyball 2.1.1 + # recovers heavyball-1.7.2 behaviour - previously swept hyperparameters work well optimizer = ForeachMuon( self.policy.parameters(), lr=config["learning_rate"], betas=(config["adam_beta1"], config["adam_beta2"]), eps=config["adam_eps"], + heavyball_momentum=True, ) else: raise ValueError(f"Unknown optimizer: {config['optimizer']}") @@ -203,8 +233,6 @@ def __init__(self, config, vecenv, policy, logger=None, full_args=None): self.scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs) self.total_epochs = epochs - self.ent_coef_initial = config["ent_coef"] - # Automatic mixed precision precision = config["precision"] self.amp_context = contextlib.nullcontext() @@ -226,6 +254,8 @@ def __init__(self, config, vecenv, policy, logger=None, full_args=None): self.stats = defaultdict(list) self.last_stats = defaultdict(list) self.losses = {} + self.best_score = -float("inf") + self.ema_max = 0.0 # Dashboard self.model_size = sum(p.numel() for p in policy.parameters() if p.requires_grad) @@ -253,8 +283,8 @@ def evaluate(self): if config["use_rnn"]: for k in self.lstm_h: - self.lstm_h[k] = torch.zeros(self.lstm_h[k].shape, device=device) - self.lstm_c[k] = torch.zeros(self.lstm_c[k].shape, device=device) + self.lstm_h[k].zero_() + self.lstm_c[k].zero_() self.full_rows = 0 while self.full_rows < self.segments: @@ -264,7 +294,8 @@ def evaluate(self): profile("eval_misc", epoch) env_id = slice(env_id[0], env_id[-1] + 1) - self.global_step += int(mask.sum()) + self.global_step += env_id.stop - env_id.start + # self.global_step += int(mask.sum()) profile("eval_copy", epoch) o = torch.as_tensor(o) @@ -272,7 +303,8 @@ def evaluate(self): r = torch.as_tensor(r).to(device) # , non_blocking=True) d = torch.as_tensor(d).to(device) # , non_blocking=True) t = torch.as_tensor(t).to(device) # , non_blocking=True) - done_mask = (d + t).clamp(max=1) + done_mask = (d + t).clamp(max=1.0) + m = torch.as_tensor(mask).to(device) # , non_blocking=True) profile("eval_forward", epoch) with torch.no_grad(), self.amp_context: @@ -289,7 +321,7 @@ def evaluate(self): logits, value = self.policy.forward_eval(o_device, state) action, logprob, _ = pufferlib.pytorch.sample_logits(logits) - r = torch.clamp(r, -1, 1) + r = torch.sign(r) * torch.log1p(torch.abs(r)) profile("eval_copy", epoch) with torch.no_grad(): @@ -319,6 +351,7 @@ def evaluate(self): self.terminals[batch_rows, l] = done_mask.float() self.truncations[batch_rows, l] = t.float() self.values[batch_rows, l] = value.flatten() + self.masks[batch_rows, l] = m # Note: We are not yet handling masks in this version self.ep_lengths[env_id] += 1 @@ -358,7 +391,213 @@ def train(self): profile = self.profile epoch = self.epoch profile("train", epoch) + profile("train_misc", epoch, nest=True) losses = defaultdict(float) + config = self.config + ppo_granularity = config["ppo_granularity"] + if ppo_granularity == "auto": + ppo_granularity = "trajectory" if config["use_rnn"] else "transition" + if config["use_rnn"] and ppo_granularity == "transition": + raise ValueError("RNN requires trajectory-level training") + + if ppo_granularity == "trajectory": + explained_var = self._train_ppo_trajectory(losses, profile, epoch) + else: + explained_var = self._train_ppo_transition(losses, profile, epoch) + + profile("train_misc", epoch) + if config["anneal_lr"]: + self.scheduler.step() + + losses["explained_variance"] = explained_var + + profile.end() + logs = None + self.epoch += 1 + done_training = self.global_step >= config["total_timesteps"] + if done_training or self.global_step == 0 or time.time() > self.last_log_time + 0.25: + self.losses = losses + logs = self.mean_and_log() + self.print_dashboard() + self.stats = defaultdict(list) + self.last_log_time = time.time() + self.last_log_step = self.global_step + profile.clear() + + if self.epoch % config["checkpoint_interval"] == 0 or done_training: + self.save_checkpoint() + self.msg = f"Checkpoint saved at update {self.epoch}" + + if self.render and self.epoch % self.render_interval == 0: + model_dir = os.path.join(self.config["data_dir"], f"{self.config['env']}_{self.logger.run_id}") + model_files = glob.glob(os.path.join(model_dir, "models", "model_*.pt")) + + if model_files: + # Take the latest checkpoint + latest_cpt = max(model_files, key=os.path.getctime) + bin_path = f"{model_dir}.bin" + + # Export to .bin for rendering with raylib + try: + export_args = {"env_name": self.config["env"], "load_model_path": latest_cpt, **self.config} + + export( + args=export_args, + env_name=self.config["env"], + vecenv=self.vecenv, + policy=self.uncompiled_policy, + path=bin_path, + silent=True, + ) + pufferlib.utils.render_videos( + self.config, self.vecenv, self.logger, self.epoch, self.global_step, bin_path + ) + + except Exception as e: + print(f"Failed to export model weights: {e}") + + if self.config["eval"]["wosac_realism_eval"] and ( + self.epoch % self.config["eval"]["eval_interval"] == 0 or done_training + ): + pufferlib.utils.run_wosac_eval_in_subprocess(self.config, self.logger, self.global_step) + + if self.config["eval"]["human_replay_eval"] and ( + self.epoch % self.config["eval"]["eval_interval"] == 0 or done_training + ): + pufferlib.utils.run_human_replay_eval_in_subprocess(self.config, self.logger, self.global_step) + + if self.config["eval"]["wosac_realism_eval"] and ( + self.epoch % self.config["eval"]["eval_interval"] == 0 or done_training + ): + pufferlib.utils.run_wosac_eval_in_subprocess(self.config, self.logger, self.global_step) + + if self.config["eval"]["human_replay_eval"] and ( + self.epoch % self.config["eval"]["eval_interval"] == 0 or done_training + ): + pufferlib.utils.run_human_replay_eval_in_subprocess(self.config, self.logger, self.global_step) + + if self.config["eval"]["multi_scenario_eval"] and ( + self.epoch % self.config["eval"]["eval_interval"] == 0 or done_training + ): + # Get evaluation settings from config + eval_simulation_mode = self.config["eval"]["multi_scenario_simulation_mode"] + num_agents_eval = self.config["eval"]["num_agents"] + map_dir = self.config["eval"]["map_dir"] + + # Build eval_overrides using helper function + eval_overrides = build_eval_overrides( + simulation_mode=eval_simulation_mode, + num_agents=num_agents_eval, + num_scenarios=self.config["eval"]["multi_scenario_num_scenarios"], + map_dir=map_dir, + num_carla_maps=self.config["eval"].get("num_carla_maps", 8), + ) + + # Build eval args by applying overrides to training config + eval_args = load_eval_multi_scenarios_config( + env_name=self.config["env"], + model_path=None, # No saved model - using current policy in memory + eval_overrides=eval_overrides, + ) + # Add inline-specific settings + eval_args["global_step"] = self.global_step # Log by global step for TensorBoard + eval_args["num_scenarios"] = self.config["eval"]["multi_scenario_num_scenarios"] + eval_args["eval_simulation"] = eval_simulation_mode + + # Mark this as inline evaluation and set results folder in experiments + eval_args["inline_eval"] = True # Flag to indicate inline evaluation during training + experiment_name = f"{self.config['env']}_{self.logger.run_id}" + eval_args["load_model_path"] = os.path.join( + self.config["data_dir"], experiment_name, "models", f"inline_epoch_{self.epoch}.pt" + ) + # For inline eval, results go in experiments folder instead of benchmark + eval_args["eval_results_dir"] = os.path.join( + self.config["data_dir"], + experiment_name, + "validation", + f"epoch_{self.epoch}", + self.config["eval"]["multi_scenario_simulation_mode"], + ) + + # Call eval_multi_scenarios inline with current policy and logger + print(f"\n🔄 Running multi-scenario evaluation at step {self.global_step}...") + eval_multi_scenarios( + env_name=self.config["env"], + args=eval_args, + vecenv=None, # Let it create its own eval environment + policy=self.uncompiled_policy, # Pass current policy + logger=self.logger, # Pass logger for TensorBoard logging + metric_prefix="validation", # Use validation_ prefix + quiet=True, # Suppress verbose output during inline eval + ) + + return logs + + def _ppo_minibatch_obs(self, obs, idx): + obs_idx = idx.cpu() if obs.device.type == "cpu" and idx.device.type != "cpu" else idx + mb_obs = obs[obs_idx] + device = torch.device(self.config["device"]) + if mb_obs.device != device: + mb_obs = mb_obs.to(device, non_blocking=self.config["cpu_offload"]) + return mb_obs + + def _ppo_loss( + self, + mb_obs, + mb_actions, + mb_logprobs, + mb_values, + mb_returns, + mb_adv, + clip_coef, + vf_clip, + adv_weights=None, + unbiased_std=False, + ): + state = dict(action=mb_actions, lstm_h=None, lstm_c=None) + logits, newvalue = self.policy(mb_obs, state) + _, newlogprob, entropy = pufferlib.pytorch.sample_logits(logits, action=mb_actions) + + newlogprob = newlogprob.view_as(mb_logprobs) + newvalue = newvalue.view_as(mb_returns) + logratio = newlogprob - mb_logprobs + ratio = logratio.exp() + + with torch.no_grad(): + old_approx_kl = (-logratio).mean() + approx_kl = ((ratio - 1) - logratio).mean() + clipfrac = ((ratio - 1.0).abs() > clip_coef).float().mean() + + mb_adv = (mb_adv - mb_adv.mean()) / (mb_adv.std(unbiased=unbiased_std) + 1e-8) + if adv_weights is not None: + mb_adv = adv_weights * mb_adv + + pg_loss1 = -mb_adv * ratio + pg_loss2 = -mb_adv * torch.clamp(ratio, 1 - clip_coef, 1 + clip_coef) + pg_loss = torch.max(pg_loss1, pg_loss2).mean() + + v_clipped = mb_values + torch.clamp(newvalue - mb_values, -vf_clip, vf_clip) + v_loss_unclipped = (newvalue - mb_returns) ** 2 + v_loss_clipped = (v_clipped - mb_returns) ** 2 + v_loss = 0.5 * torch.max(v_loss_unclipped, v_loss_clipped).mean() + entropy_loss = entropy.mean() + loss = pg_loss + self.config["vf_coef"] * v_loss - self.config["ent_coef"] * entropy_loss + + return ( + loss, + newvalue, + ratio, + { + "policy_loss": pg_loss.item(), + "value_loss": v_loss.item(), + "entropy": entropy_loss.item(), + "old_approx_kl": old_approx_kl.item(), + "approx_kl": approx_kl.item(), + "clipfrac": clipfrac.item(), + }, + ) + + def _train_ppo_trajectory(self, losses, profile, epoch): config = self.config device = config["device"] @@ -370,7 +609,7 @@ def train(self): self.ratio[:] = 1 for mb in range(self.total_minibatches): - profile("train_misc", epoch, nest=True) + profile("train_misc", epoch) self.amp_context.__enter__() shape = self.values.shape @@ -387,100 +626,43 @@ def train(self): config["vtrace_c_clip"], ) - profile("train_copy", epoch) adv = advantages.abs().sum(axis=1) prio_weights = torch.nan_to_num(adv**a, 0, 0, 0) prio_probs = (prio_weights + 1e-6) / (prio_weights.sum() + 1e-6) idx = torch.multinomial(prio_probs, self.minibatch_segments) mb_prio = (self.segments * prio_probs[idx, None]) ** -anneal_beta - mb_obs = self.observations[idx] + + profile("train_copy", epoch) + mb_obs = self._ppo_minibatch_obs(self.observations, idx) mb_actions = self.actions[idx] mb_logprobs = self.logprobs[idx] - mb_rewards = self.rewards[idx] - mb_terminals = self.terminals[idx] - mb_truncations = self.truncations[idx] - mb_ratio = self.ratio[idx] mb_values = self.values[idx] mb_returns = advantages[idx] + mb_values - mb_advantages = advantages[idx] + mb_adv = advantages[idx] profile("train_forward", epoch) - if not config["use_rnn"]: - mb_obs = mb_obs.reshape(-1, *self.vecenv.single_observation_space.shape) - - state = dict( - action=mb_actions, - lstm_h=None, - lstm_c=None, - ) - - logits, newvalue = self.policy(mb_obs, state) - actions, newlogprob, entropy = pufferlib.pytorch.sample_logits(logits, action=mb_actions) - - profile("train_misc", epoch) - newlogprob = newlogprob.reshape(mb_logprobs.shape) - logratio = newlogprob - mb_logprobs - ratio = logratio.exp() - self.ratio[idx] = ratio.detach() - - with torch.no_grad(): - old_approx_kl = (-logratio).mean() - approx_kl = ((ratio - 1) - logratio).mean() - clipfrac = ((ratio - 1.0).abs() > config["clip_coef"]).float().mean() - - adv = advantages[idx] - adv = compute_puff_advantage( + loss, newvalue, ratio, stats = self._ppo_loss( + mb_obs, + mb_actions, + mb_logprobs, mb_values, - mb_rewards, - mb_terminals, - ratio, - adv, - config["gamma"], - config["gae_lambda"], - config["vtrace_rho_clip"], - config["vtrace_c_clip"], + mb_returns, + mb_adv, + clip_coef, + vf_clip, + adv_weights=mb_prio, + unbiased_std=True, ) - adv = mb_advantages - adv = mb_prio * (adv - adv.mean()) / (adv.std() + 1e-8) - - # Losses - pg_loss1 = -adv * ratio - pg_loss2 = -adv * torch.clamp(ratio, 1 - clip_coef, 1 + clip_coef) - pg_loss = torch.max(pg_loss1, pg_loss2).mean() - - newvalue = newvalue.view(mb_returns.shape) - v_clipped = mb_values + torch.clamp(newvalue - mb_values, -vf_clip, vf_clip) - v_loss_unclipped = (newvalue - mb_returns) ** 2 - v_loss_clipped = (v_clipped - mb_returns) ** 2 - v_loss = 0.5 * torch.max(v_loss_unclipped, v_loss_clipped).mean() - - entropy_loss = entropy.mean() - - # Get current entropy coefficient - if config["anneal_entropy"]: - # Cosine annealing from initial to 0.0 - current_ent_coef = 0.5 * self.ent_coef_initial * (1 + np.cos(np.pi * self.epoch / self.total_epochs)) - else: - current_ent_coef = config["ent_coef"] - - loss = pg_loss + config["vf_coef"] * v_loss - current_ent_coef * entropy_loss - + self.ratio[idx] = ratio.detach() self.amp_context.__enter__() # TODO: AMP needs some debugging - # This breaks vloss clipping? self.values[idx] = newvalue.detach().float() - # Logging profile("train_misc", epoch) - losses["policy_loss"] += pg_loss.item() / self.total_minibatches - losses["value_loss"] += v_loss.item() / self.total_minibatches - losses["entropy"] += entropy_loss.item() / self.total_minibatches - losses["old_approx_kl"] += old_approx_kl.item() / self.total_minibatches - losses["approx_kl"] += approx_kl.item() / self.total_minibatches - losses["clipfrac"] += clipfrac.item() / self.total_minibatches + for key, value in stats.items(): + losses[key] += value / self.total_minibatches losses["importance"] += ratio.mean().item() / self.total_minibatches - # Learn on accumulated minibatches profile("learn", epoch) loss.backward() if (mb + 1) % self.accumulate_minibatches == 0: @@ -488,56 +670,129 @@ def train(self): self.optimizer.step() self.optimizer.zero_grad() - # Reprioritize experience - profile("train_misc", epoch) - if config["anneal_lr"]: - self.scheduler.step() - y_pred = self.values.flatten() y_true = advantages.flatten() + self.values.flatten() var_y = y_true.var() - explained_var = torch.nan if var_y == 0 else 1 - (y_true - y_pred).var() / var_y - losses["explained_variance"] = explained_var.item() + return float("nan") if var_y == 0 else (1 - (y_true - y_pred).var() / var_y).item() - profile.end() - logs = None - self.epoch += 1 - done_training = self.global_step >= config["total_timesteps"] - if done_training or self.global_step == 0 or time.time() > self.last_log_time + 0.25: - logs = self.mean_and_log() - self.losses = losses - self.print_dashboard() - self.stats = defaultdict(list) - self.last_log_time = time.time() - self.last_log_step = self.global_step - profile.clear() + def _train_ppo_transition(self, losses, profile, epoch): + config = self.config + device = config["device"] - if self.epoch % config["checkpoint_interval"] == 0 or done_training: - self.save_checkpoint() - self.msg = f"Checkpoint saved at update {self.epoch}" + clip_coef = config["clip_coef"] + vf_clip = config["vf_clip_coef"] - if (self.epoch - 1) % self.config["eval"]["eval_interval"] == 0 or done_training: - human_replay_eval = self.config["eval"]["human_replay_eval"] - self_play_eval = self.config["eval"]["self_play_eval"] - - self.evaluator = Evaluator(self.full_args, self.logger) - if human_replay_eval: - self.evaluator.hr_env = load_env("puffer_drive", self.evaluator.hr_eval_config) - self.evaluator.rollout(self.uncompiled_policy, mode="human_replay") - self.evaluator.hr_env.close() - self.evaluator.log_videos(eval_mode="human_replay", epoch=self.epoch) - if self_play_eval: - self.evaluator.sp_env = load_env("puffer_drive", self.evaluator.sp_eval_config) - self.evaluator.rollout(self.uncompiled_policy, mode="self_play") - self.evaluator.sp_env.close() - self.evaluator.log_videos(eval_mode="self_play", epoch=self.epoch) - if human_replay_eval or self_play_eval: - self.evaluator.log_stats() - - del self.evaluator - - if self.config["eval"]["wosac_realism_eval"]: - pufferlib.utils.run_wosac_eval_in_subprocess(self.config, self.logger, self.global_step) + masks = self.masks.bool() + terminals = torch.maximum(self.terminals, (~masks).float()) + advantages = compute_puff_advantage( + self.values, + self.rewards, + terminals, + torch.ones_like(self.values, device=device), + torch.zeros_like(self.values, device=device), + config["gamma"], + config["gae_lambda"], + 1.0, + 1.0, + ) + advantages = advantages.masked_fill(~masks, 0.0) + returns = advantages + self.values + + flat_advantages_f = advantages.reshape(-1) + flat_masks_f = masks.reshape(-1).bool() + total_transitions = flat_masks_f.numel() + valid_idx = torch.nonzero(flat_masks_f, as_tuple=False).flatten() + + filter_metrics = { + "masked_fraction": 1.0 - (valid_idx.numel() / max(total_transitions, 1)), + "kept_fraction": 0.0, + "filtered_fraction": 1.0, + } + + ewma_beta = config["adv_filter_ewma_beta"] + threshold_scale = config["adv_filter_threshold_scale"] + valid_abs_adv = flat_advantages_f[valid_idx].abs() + current_max = valid_abs_adv.max().item() if valid_abs_adv.numel() > 0 else 0.0 + self.ema_max = current_max if epoch == 0 else ewma_beta * current_max + (1 - ewma_beta) * self.ema_max + threshold = threshold_scale * self.ema_max + + keep_mask = valid_abs_adv >= threshold + keep_idx = valid_idx[keep_mask] + num_valid, num_kept = valid_idx.numel(), keep_idx.numel() + + filter_metrics["kept_fraction"] = num_kept / max(num_valid, 1) + filter_metrics["filtered_fraction"] = 1.0 - filter_metrics["kept_fraction"] + + losses["filter_threshold"] = threshold + losses["ema_max"] = self.ema_max + losses.update(filter_metrics) + + obs_shape = self.vecenv.single_observation_space.shape + flat_obs = self.observations.reshape(-1, *obs_shape) + flat_actions = self.actions.reshape(-1, *self.actions.shape[2:]) + flat_logprobs = self.logprobs.reshape(-1) + flat_values = self.values.reshape(-1) + flat_returns = returns.reshape(-1) + flat_advantages = advantages.reshape(-1) + + self.optimizer.zero_grad() + total_minibatches = 0 + pending_minibatches = 0 + + for _ in range(config["update_epochs"]): + permutation = keep_idx[torch.randperm(keep_idx.numel(), device=keep_idx.device)] + for start in range(0, permutation.numel(), self.minibatch_size): + profile("train_copy", epoch) + mb_idx = permutation[start : start + self.minibatch_size] + mb_obs = self._ppo_minibatch_obs(flat_obs, mb_idx) + mb_actions = flat_actions[mb_idx] + mb_logprobs = flat_logprobs[mb_idx] + mb_values = flat_values[mb_idx] + mb_returns = flat_returns[mb_idx] + mb_adv = flat_advantages[mb_idx] + + profile("train_forward", epoch) + loss, _, _, stats = self._ppo_loss( + mb_obs, + mb_actions, + mb_logprobs, + mb_values, + mb_returns, + mb_adv, + clip_coef, + vf_clip, + unbiased_std=False, + ) + self.amp_context.__enter__() # TODO: AMP needs some debugging + + profile("train_misc", epoch) + for key, value in stats.items(): + losses[key] += value + + profile("learn", epoch) + loss.backward() + total_minibatches += 1 + pending_minibatches += 1 + + if pending_minibatches >= self.accumulate_minibatches: + torch.nn.utils.clip_grad_norm_(self.policy.parameters(), config["max_grad_norm"]) + self.optimizer.step() + self.optimizer.zero_grad() + pending_minibatches = 0 + + if pending_minibatches > 0: + torch.nn.utils.clip_grad_norm_(self.policy.parameters(), config["max_grad_norm"]) + self.optimizer.step() + self.optimizer.zero_grad() + + if total_minibatches > 0: + for key in ("policy_loss", "value_loss", "entropy", "old_approx_kl", "approx_kl", "clipfrac"): + losses[key] /= total_minibatches + + y_pred = flat_values[valid_idx] + y_true = flat_returns[valid_idx] + var_y = y_true.var(unbiased=False) + return float("nan") if var_y == 0 else (1 - (y_true - y_pred).var(unbiased=False) / var_y).item() def mean_and_log(self): config = self.config @@ -556,24 +811,18 @@ def mean_and_log(self): "SPS": dist_sum(self.sps, device), "agent_steps": agent_steps, "uptime": time.time() - self.start_time, - "epoch": int(dist_sum(self.epoch, device)), + "epoch": int(dist_sum(self.epoch, device)), # VB Why it is a sum ? "learning_rate": self.optimizer.param_groups[0]["lr"], - "ent_coef": ( - 0.5 * self.ent_coef_initial * (1 + np.cos(np.pi * self.epoch / self.total_epochs)) - if config["anneal_entropy"] - else config["ent_coef"] - ), **{f"environment/{k}": v for k, v in self.stats.items()}, **{f"losses/{k}": v for k, v in self.losses.items()}, **{f"performance/{k}": v["elapsed"] for k, v in self.profile}, + # **{f'environment/{k}': dist_mean(v, device) for k, v in self.stats.items()}, + # **{f'losses/{k}': dist_mean(v, device) for k, v in self.losses.items()}, + # **{f'performance/{k}': dist_sum(v['elapsed'], device) for k, v in self.profile}, } - if torch.distributed.is_initialized(): - if torch.distributed.get_rank() != 0: - self.logger.log(logs, agent_steps) - return logs - else: - return None + if torch.distributed.is_initialized() and torch.distributed.get_rank() != 0: + return None self.logger.log(logs, agent_steps) return logs @@ -581,24 +830,28 @@ def mean_and_log(self): def close(self): self.vecenv.close() self.utilization.stop() + if torch.distributed.is_initialized() and torch.distributed.get_rank() != 0: + return model_path = self.save_checkpoint() run_id = self.logger.run_id - path = os.path.join(self.config["data_dir"], f"{self.config['env']}_{run_id}.pt") + run_dir = os.path.join(self.config["data_dir"], f"{self.config['env']}_{run_id}") + path = os.path.join(run_dir, f"{self.config['env']}_{run_id}.pt") shutil.copy(model_path, path) return path def save_checkpoint(self): - if torch.distributed.is_initialized(): - if torch.distributed.get_rank() != 0: - return + if torch.distributed.is_initialized() and torch.distributed.get_rank() != 0: + return run_id = self.logger.run_id path = os.path.join(self.config["data_dir"], f"{self.config['env']}_{run_id}") if not os.path.exists(path): os.makedirs(path) + models_dir = os.path.join(path, "models") + os.makedirs(models_dir, exist_ok=True) model_name = f"model_{self.config['env']}_{self.epoch:06d}.pt" - model_path = os.path.join(path, model_name) + model_path = os.path.join(models_dir, model_name) if os.path.exists(model_path): return model_path @@ -615,15 +868,27 @@ def save_checkpoint(self): state_path = os.path.join(path, "trainer_state.pt") torch.save(state, state_path + ".tmp") os.rename(state_path + ".tmp", state_path) + + current_score = self.last_stats.get("puffer_score", self.last_stats.get("score", -float("inf"))) + + if current_score > self.best_score: + self.best_score = current_score + + best_state_file = os.path.join(path, f"best_models/best_trainer_state_{self.epoch:06d}.pt") + os.makedirs(os.path.dirname(best_state_file), exist_ok=True) + shutil.copy(model_path, best_state_file) + print(f"New best model saved at epoch {self.epoch} with puffer_score {self.best_score:.4f}") + return model_path - def print_dashboard(self, clear=False, idx=[0], c1="[cyan]", c2="[white]", b1="[bright_cyan]", b2="[bright_white]"): + def print_dashboard( + self, clear=False, idx=[0], c1="[cyan]", c2="[dim default]", b1="[bright_cyan]", b2="[default]" + ): config = self.config sps = dist_sum(self.sps, config["device"]) agent_steps = dist_sum(self.global_step, config["device"]) - if torch.distributed.is_initialized(): - if torch.distributed.get_rank() != 0: - return + if torch.distributed.is_initialized() and torch.distributed.get_rank() != 0: + return profile = self.profile console = Console() @@ -647,19 +912,19 @@ def print_dashboard(self, clear=False, idx=[0], c1="[cyan]", c2="[white]", b1="[ idx[0] = (idx[0] - 1) % 10 s = Table(box=None, expand=True) - remaining = "A hair past a freckle" + remaining = f"{b2}A hair past a freckle{c2}" if sps != 0: remaining = duration((config["total_timesteps"] - agent_steps) / sps, b2, c2) s.add_column(f"{c1}Summary", justify="left", vertical="top", width=10) s.add_column(f"{c1}Value", justify="right", vertical="top", width=14) - s.add_row(f"{c2}Env", f"{b2}{config['env']}") - s.add_row(f"{c2}Params", abbreviate(self.model_size, b2, c2)) - s.add_row(f"{c2}Steps", abbreviate(agent_steps, b2, c2)) - s.add_row(f"{c2}SPS", abbreviate(sps, b2, c2)) - s.add_row(f"{c2}Epoch", f"{b2}{self.epoch}") - s.add_row(f"{c2}Uptime", duration(self.uptime, b2, c2)) - s.add_row(f"{c2}Remaining", remaining) + s.add_row(f"{b2}Env", f"{b2}{config['env']}") + s.add_row(f"{b2}Params", abbreviate(self.model_size, b2, c2)) + s.add_row(f"{b2}Steps", abbreviate(agent_steps, b2, c2)) + s.add_row(f"{b2}SPS", abbreviate(sps, b2, c2)) + s.add_row(f"{b2}Epoch", f"{b2}{self.epoch}") + s.add_row(f"{b2}Uptime", duration(self.uptime, b2, c2)) + s.add_row(f"{b2}Remaining", remaining) delta = profile.eval["buffer"] + profile.train["buffer"] p = Table(box=None, expand=True, show_header=False) @@ -667,15 +932,15 @@ def print_dashboard(self, clear=False, idx=[0], c1="[cyan]", c2="[white]", b1="[ p.add_column(f"{c1}Time", justify="right", width=8) p.add_column(f"{c1}%", justify="right", width=4) p.add_row(*fmt_perf("Evaluate", b1, delta, profile.eval, b2, c2)) - p.add_row(*fmt_perf(" Forward", c2, delta, profile.eval_forward, b2, c2)) - p.add_row(*fmt_perf(" Env", c2, delta, profile.env, b2, c2)) - p.add_row(*fmt_perf(" Copy", c2, delta, profile.eval_copy, b2, c2)) - p.add_row(*fmt_perf(" Misc", c2, delta, profile.eval_misc, b2, c2)) + p.add_row(*fmt_perf(" Forward", b2, delta, profile.eval_forward, b2, c2)) + p.add_row(*fmt_perf(" Env", b2, delta, profile.env, b2, c2)) + p.add_row(*fmt_perf(" Copy", b2, delta, profile.eval_copy, b2, c2)) + p.add_row(*fmt_perf(" Misc", b2, delta, profile.eval_misc, b2, c2)) p.add_row(*fmt_perf("Train", b1, delta, profile.train, b2, c2)) - p.add_row(*fmt_perf(" Forward", c2, delta, profile.train_forward, b2, c2)) - p.add_row(*fmt_perf(" Learn", c2, delta, profile.learn, b2, c2)) - p.add_row(*fmt_perf(" Copy", c2, delta, profile.train_copy, b2, c2)) - p.add_row(*fmt_perf(" Misc", c2, delta, profile.train_misc, b2, c2)) + p.add_row(*fmt_perf(" Forward", b2, delta, profile.train_forward, b2, c2)) + p.add_row(*fmt_perf(" Learn", b2, delta, profile.learn, b2, c2)) + p.add_row(*fmt_perf(" Copy", b2, delta, profile.train_copy, b2, c2)) + p.add_row(*fmt_perf(" Misc", b2, delta, profile.train_misc, b2, c2)) l = Table( box=None, @@ -684,7 +949,7 @@ def print_dashboard(self, clear=False, idx=[0], c1="[cyan]", c2="[white]", b1="[ l.add_column(f"{c1}Losses", justify="left", width=16) l.add_column(f"{c1}Value", justify="right", width=8) for metric, value in self.losses.items(): - l.add_row(f"{c2}{metric}", f"{b2}{value:.3f}") + l.add_row(f"{b2}{metric}", f"{b2}{value:.3f}") monitor = Table(box=None, expand=True, pad_edge=False) monitor.add_row(s, p, l) @@ -705,13 +970,16 @@ def print_dashboard(self, clear=False, idx=[0], c1="[cyan]", c2="[white]", b1="[ self.last_stats = self.stats for metric, value in (self.stats or self.last_stats).items(): + if metric in HIDDEN_DASHBOARD_METRICS: + continue + try: # Discard non-numeric values int(value) except: continue u = left if i % 2 == 0 else right - u.add_row(f"{c2}{metric}", f"{b2}{value:.3f}") + u.add_row(f"{b2}{metric}", f"{b2}{value:.3f}") i += 1 if i == 30: break @@ -752,15 +1020,15 @@ def compute_puff_advantage( def abbreviate(num, b2, c2): if num < 1e3: - return str(num) + return f"{b2}{num}{c2}" elif num < 1e6: - return f"{num / 1e3:.1f}K" + return f"{b2}{num / 1e3:.1f}{c2}K" elif num < 1e9: - return f"{num / 1e6:.1f}M" + return f"{b2}{num / 1e6:.1f}{c2}M" elif num < 1e12: - return f"{num / 1e9:.1f}B" + return f"{b2}{num / 1e9:.1f}{c2}B" else: - return f"{num / 1e12:.2f}T" + return f"{b2}{num / 1e12:.2f}{c2}T" def duration(seconds, b2, c2): @@ -807,11 +1075,12 @@ def __getattr__(self, name): return self.profiles[name] def __call__(self, name, epoch, nest=False): - if epoch % self.frequency != 0: + # Skip profiling the first few epochs, which are noisy due to setup + if (epoch + 1) % self.frequency != 0: return - # if torch.cuda.is_available(): - # torch.cuda.synchronize() + if torch.cuda.is_available(): + torch.cuda.synchronize() tick = time.time() if len(self.stack) != 0 and not nest: @@ -823,12 +1092,13 @@ def __call__(self, name, epoch, nest=False): def pop(self, end): profile = self.profiles[self.stack.pop()] delta = end - profile["start"] - profile["elapsed"] += delta profile["delta"] += delta + # Multiply delta by freq to account for skipped epochs + profile["elapsed"] += delta * self.frequency def end(self): - # if torch.cuda.is_available(): - # torch.cuda.synchronize() + if torch.cuda.is_available(): + torch.cuda.synchronize() end = time.time() for i in range(len(self.stack)): @@ -863,9 +1133,13 @@ def run(self): time.sleep(self.delay) continue - self.gpu_util.append(torch.cuda.utilization()) - free, total = torch.cuda.mem_get_info() - self.gpu_mem.append(100 * (total - free) / total) + try: + self.gpu_util.append(torch.cuda.utilization()) + free, total = torch.cuda.mem_get_info() + self.gpu_mem.append(100 * (total - free) / total) + except (ModuleNotFoundError, RuntimeError): + self.gpu_util.append(0) + self.gpu_mem.append(0) else: self.gpu_util.append(0) self.gpu_mem.append(0) @@ -876,32 +1150,35 @@ def stop(self): self.stopped = True -def downsample(arr, m): - if len(arr) < m: - return arr +def downsample(data_list, num_points): + if not data_list or num_points <= 0: + return [] + if num_points == 1: + return [data_list[-1]] + if len(data_list) <= num_points: + return data_list + + last = data_list[-1] + data_list = data_list[:-1] + + data_np = np.array(data_list) + num_points -= 1 # one down for the last one - if m == 0: - return [arr[-1]] + n = (len(data_np) // num_points) * num_points + data_np = data_np[-n:] if n > 0 else data_np + downsampled = data_np.reshape(num_points, -1).mean(axis=1) - orig_arr = arr - last = arr[-1] - arr = arr[:-1] - arr = np.array(arr) - n = len(arr) - n = (n // m) * m - arr = arr[-n:] - downsampled = arr.reshape(m, -1).mean(axis=1) - return np.concatenate([downsampled, [last]]) + return downsampled.tolist() + [last] class NoLogger: - def __init__(self, args): - self.run_id = str(int(100 * time.time())) + def __init__(self, args, run_id=None): + self.run_id = run_id or str(int(time.time())) def log(self, logs, step): pass - def close(self, model_path): + def close(self, model_path, early_stop): pass @@ -925,13 +1202,19 @@ def __init__(self, args, load_id=None, mode="async"): self.neptune = neptune for k, v in pufferlib.unroll_nested_dict(args): neptune[k].append(v) + self.should_upload_model = not args["no_model_upload"] def log(self, logs, step): for k, v in logs.items(): self.neptune[k].append(v, step=step) - def close(self, model_path): + def upload_model(self, model_path): self.neptune["model"].track_files(model_path) + + def close(self, model_path, early_stop): + self.neptune["early_stop"] = early_stop + if self.should_upload_model: + self.upload_model(model_path) self.neptune.stop() def download(self): @@ -951,19 +1234,25 @@ def __init__(self, args, load_id=None, resume="allow"): save_code=False, resume=resume, config=args, - name=args.get("wandb_name"), tags=[args["tag"]] if args["tag"] is not None else [], + settings=wandb.Settings(console="off"), # stop sending dashboard to wandb ) self.wandb = wandb self.run_id = wandb.run.id + self.should_upload_model = not args["no_model_upload"] def log(self, logs, step): self.wandb.log(logs, step=step) - def close(self, model_path): + def upload_model(self, model_path): artifact = self.wandb.Artifact(self.run_id, type="model") artifact.add_file(model_path) self.wandb.run.log_artifact(artifact) + + def close(self, model_path, early_stop): + self.wandb.run.summary["early_stop"] = early_stop + if self.should_upload_model: + self.upload_model(model_path) self.wandb.finish() def download(self): @@ -973,9 +1262,118 @@ def download(self): return f"{data_dir}/{model_file}" -def train(env_name, args=None, vecenv=None, policy=None, logger=None): +class TensorBoardLogger: + def __init__(self, run_id, experiment_dir): + try: + from torch.utils.tensorboard import SummaryWriter + except ImportError: + raise ImportError("TensorBoardLogger requires tensorboard.") + + self.run_id = run_id + local_log_dir = experiment_dir + os.makedirs(local_log_dir, exist_ok=True) + print(f"[TensorBoardLogger] Logging locally to: {local_log_dir}") + self.local_writer = SummaryWriter(log_dir=local_log_dir) + + def log(self, logs, step): + for key, value in logs.items(): + if isinstance(value, (int, float)): + self.local_writer.add_scalar(key, value, step) + + def close(self, model_path, early_stop): + self.local_writer.close() + + +def _get_git_metadata(): + git_metadata = { + "commit_hash": None, + "commit_message": None, + "is_dirty": None, + } + + try: + repo_root = os.path.dirname(os.path.dirname(os.path.realpath(__file__))) + git_metadata["commit_hash"] = subprocess.check_output( + ["git", "rev-parse", "HEAD"], + cwd=repo_root, + stderr=subprocess.DEVNULL, + text=True, + ).strip() + git_metadata["commit_message"] = subprocess.check_output( + ["git", "log", "-1", "--pretty=%s"], + cwd=repo_root, + stderr=subprocess.DEVNULL, + text=True, + ).strip() + git_metadata["is_dirty"] = bool( + subprocess.check_output( + ["git", "status", "--porcelain", "--untracked-files=no"], + cwd=repo_root, + stderr=subprocess.DEVNULL, + text=True, + ).strip() + ) + except (OSError, subprocess.SubprocessError): + pass + + return git_metadata + + +def _save_experiment_config(args, path): + import yaml + import json + + experiment_dir = path + os.makedirs(experiment_dir, exist_ok=True) + + # Save config as yaml + config_yaml_path = os.path.join(experiment_dir, "config.yaml") + with open(config_yaml_path, "w") as f: + # Convert defaultdict to dict for cleaner output + config = json.loads(json.dumps(args)) + config["git"] = _get_git_metadata() + yaml.dump(config, f) + + +def train(env_name, args=None, vecenv=None, policy=None, logger=None, early_stop_fn=None): args = args or load_config(env_name) + # Fine-tuning: reload network, observation configuration from config.yaml and override the args --> only change new reward / new maps / new simulation mode + if args["load_model_path"]: + experiment_dir = os.path.dirname(args["load_model_path"]) + config_yaml_path = os.path.join(experiment_dir, "config.yaml") + KEYS_OF_INTEREST = { + "action_type", + "dynamics_model", + "target_type", + "num_target_waypoints", + "reward_conditioning", + "reward_randomization", + "trajectory_prediction_length", + "num_trajectory_scaling_factors", + "trajectory_scaling_factors", + "max_boundary_segment_observations", + "max_lane_segment_observations", + "max_partner_observations", + "max_traffic_light_observations", + "max_stop_sign_observations", + } + if os.path.exists(config_yaml_path): + print(f"Found config.yaml at {config_yaml_path}. Merging with defaults...") + with open(config_yaml_path, "r") as f: + yaml_config = yaml.safe_load(f) + + # Override Policy and RNN dimensions from model config + for section in ["policy", "rnn"]: + if section in yaml_config and isinstance(yaml_config[section], dict): + for k, v in yaml_config[section].items(): + args[section][k] = v + # Override ENV parameters for observation size from model config + if "env" in yaml_config and isinstance(yaml_config["env"], dict): + for k, v in yaml_config["env"].items(): + if k in KEYS_OF_INTEREST: + args["env"][k] = v + # Assume TorchRun DDP is used if LOCAL_RANK is set if "LOCAL_RANK" in os.environ: world_size = int(os.environ.get("WORLD_SIZE", 1)) @@ -987,6 +1385,10 @@ def train(env_name, args=None, vecenv=None, policy=None, logger=None): torch.cuda.set_device(local_rank) os.environ["CUDA_VISIBLE_DEVICES"] = str(local_rank) + train_seed = args["train"]["seed"] + if train_seed is None: + train_seed = time.time_ns() & 0xFFFFFFFF + torch.manual_seed(train_seed) vecenv = vecenv or load_env(env_name, args) policy = policy or load_policy(args, vecenv, env_name) @@ -1006,31 +1408,72 @@ def train(env_name, args=None, vecenv=None, policy=None, logger=None): logger = NeptuneLogger(args) elif args["wandb"]: logger = WandbLogger(args) + elif args["tb"]: + date_time = datetime.now().strftime("%Y%m%d-%H%M%S") + experiment_dir = os.path.join(args["train"]["data_dir"], rf"{env_name}_" + date_time) + logger = TensorBoardLogger( + run_id=date_time, + experiment_dir=experiment_dir, + ) train_config = dict(**args["train"], env=env_name, eval=args.get("eval", {})) - pufferl = PuffeRL(train_config, vecenv, policy, logger, full_args=args) + pufferl = PuffeRL(train_config, vecenv, policy, logger) + path = os.path.join(args["train"]["data_dir"], f"{env_name}_{pufferl.logger.run_id}") + _save_experiment_config(args, path) + + # Sweep needs data for early stopped runs, so send data when steps > 100M + logging_threshold = min(0.20 * train_config["total_timesteps"], 100_000_000) all_logs = [] + while pufferl.global_step < train_config["total_timesteps"]: if train_config["device"] == "cuda": torch.compiler.cudagraph_mark_step_begin() - pufferl.evaluate() + try: + pufferl.evaluate() + except Exception: + pufferl.vecenv.close() + pufferl.utilization.stop() + if torch.distributed.is_initialized(): + torch.distributed.destroy_process_group() + raise if train_config["device"] == "cuda": torch.compiler.cudagraph_mark_step_begin() - logs = pufferl.train() + try: + logs = pufferl.train() + except Exception: + pufferl.vecenv.close() + pufferl.utilization.stop() + if torch.distributed.is_initialized(): + torch.distributed.destroy_process_group() + raise if logs is not None: - if pufferl.global_step > 0.20 * train_config["total_timesteps"]: + should_stop_early = False + if early_stop_fn is not None: + should_stop_early = early_stop_fn(logs) + # This is hacky, but need to see if threshold looks reasonable + if "early_stop_threshold" in logs: + pufferl.logger.log( + {"environment/early_stop_threshold": logs["early_stop_threshold"]}, logs["agent_steps"] + ) + + if pufferl.global_step > logging_threshold: all_logs.append(logs) + if should_stop_early: + model_path = pufferl.close() + pufferl.logger.close(model_path, early_stop=True) + return all_logs + # Final eval. You can reset the env here, but depending on # your env, this can skew data (i.e. you only collect the shortest # rollouts within a fixed number of epochs) - i = 0 - stats = {} - while i < 32 or not stats: - stats = pufferl.evaluate() - i += 1 + # i = 0 + # stats = {} + # while i < 32 or not stats: + # stats = pufferl.evaluate() + # i += 1 logs = pufferl.mean_and_log() if logs is not None: @@ -1038,7 +1481,7 @@ def train(env_name, args=None, vecenv=None, policy=None, logger=None): pufferl.print_dashboard() model_path = pufferl.close() - pufferl.logger.close(model_path) + pufferl.logger.close(model_path, early_stop=False) return all_logs @@ -1099,18 +1542,47 @@ def eval(env_name, args=None, vecenv=None, policy=None): vecenv.close() return results_dict + elif human_replay_enabled: + args["env"]["map_dir"] = args["eval"]["map_dir"] + dataset_name = args["env"]["map_dir"].split("/")[-1] + print(f"Running human replay evaluation with {dataset_name} dataset.\n") + from pufferlib.ocean.benchmark.evaluator import HumanReplayEvaluator + + backend = args["eval"].get("backend", "PufferEnv") + args["env"]["map_dir"] = args["eval"]["map_dir"] + args["env"]["num_agents"] = args["eval"]["human_replay_num_agents"] + + args["vec"] = dict(backend=backend, num_envs=1) + args["env"]["control_mode"] = args["eval"]["human_replay_control_mode"] + args["env"]["episode_length"] = 91 # WOMD scenario length + + vecenv = vecenv or load_env(env_name, args) + policy = policy or load_policy(args, vecenv, env_name) + + print(f"Effective number of scenarios used: {len(vecenv.driver_env.agent_offsets) - 1}") + + evaluator = HumanReplayEvaluator(args) + + # Run rollouts with human replays + results = evaluator.rollout(args, vecenv, policy) + + import json + + print("HUMAN_REPLAY_METRICS_START") + print(json.dumps(results)) + print("HUMAN_REPLAY_METRICS_END") + + return results + else: # Standard evaluation: Render backend = args["vec"]["backend"] if backend != "PufferEnv": backend = "Serial" args["vec"] = dict(backend=backend, num_envs=1) - - # Create environment and policy vecenv = vecenv or load_env(env_name, args) policy = policy or load_policy(args, vecenv, env_name) - # Reset environment ob, info = vecenv.reset() driver = vecenv.driver_env num_agents = vecenv.observation_space.shape[0] @@ -1123,12 +1595,23 @@ def eval(env_name, args=None, vecenv=None, policy=None): lstm_c=torch.zeros(num_agents, policy.hidden_size, device=device), ) - if driver.render_mode == 1: - max_frames = 91 - frame_count = 0 - + frames = [] while True: - driver.render() + render = driver.render() + if len(frames) < args["save_frames"]: + frames.append(render) + + # Screenshot Ocean envs with F12, gifs with control + F12 + if driver.render_mode == "ansi": + print("\033[0;0H" + render + "\n") + time.sleep(1 / args["fps"]) + elif driver.render_mode == "rgb_array": + pass + # import cv2 + # render = cv2.cvtColor(render, cv2.COLOR_RGB2BGR) + # cv2.imshow('frame', render) + # cv2.waitKey(1) + # time.sleep(1/args['fps']) with torch.no_grad(): ob = torch.as_tensor(ob).to(device) @@ -1139,20 +1622,614 @@ def eval(env_name, args=None, vecenv=None, policy=None): if isinstance(logits, torch.distributions.Normal): action = np.clip(action, vecenv.action_space.low, vecenv.action_space.high) - ob, reward, done, truncated, info = vecenv.step(action) + ob = vecenv.step(action)[0] + + if len(frames) > 0 and len(frames) == args["save_frames"]: + import imageio + + imageio.mimsave(args["gif_path"], frames, fps=args["fps"], loop=0) + frames.append("Done") + + +def load_eval_multi_scenarios_config(env_name, model_path=None, eval_overrides=None): + """Load config for evaluation, merging experiment YAML with defaults.""" + args = load_config(env_name) + if model_path: + experiment_dir = os.path.dirname(os.path.dirname(model_path)) + config_yaml_path = os.path.join(experiment_dir, "config.yaml") + EXCLUDE_KEYS = eval_overrides["env"].keys() + # Override Policy and RNN dimensions from training config + if os.path.exists(config_yaml_path): + print(f"Found config.yaml at {config_yaml_path}. Merging with defaults...") + with open(config_yaml_path, "r") as f: + yaml_config = yaml.safe_load(f) + + for section in ["env", "policy", "rnn"]: + if section in yaml_config and isinstance(yaml_config[section], dict): + for k, v in yaml_config[section].items(): + if k not in EXCLUDE_KEYS: + args[section][k] = v + + # Also copy root-level keys like rnn_name, policy_name + for key in ["rnn_name", "policy_name"]: + if key in yaml_config: + args[key] = yaml_config[key] + + # Update use_rnn based on rnn_name + args["train"]["use_rnn"] = args["rnn_name"] is not None + + # Override env parameters from evaluation config + if eval_overrides: + for section, section_overrides in eval_overrides.items(): + if isinstance(section_overrides, dict): + for k, v in section_overrides.items(): + args[section][k] = v + else: + args[section] = section_overrides - if driver.render_mode == 1: - frame_count += 1 - if frame_count >= max_frames or done.all() or truncated.all(): - break + return args - vecenv.close() + +def build_eval_overrides(simulation_mode, num_agents, num_scenarios, map_dir=None, num_carla_maps=6): + """Build evaluation overrides for a given simulation mode. + + Args: + simulation_mode: "gigaflow" or "replay" + num_agents: agent slot budget for evaluation + map_dir: optional map directory, uses config value if not provided + """ + # Common reward coefficients (same for both modes) + common_env = { + "eval_mode": 1, + "collision_behavior": 1, + "offroad_behavior": 1, + "reward_randomization": False, + "min_agents_per_env": 30, + "max_agents_per_env": 30, + "reward_vehicle_collision": 3.0, + "reward_offroad_collision": 3.0, + "reward_ade": 0.0, + "reward_goal": 1.0, + "reward_overspeed": 0.05, + "reward_comfort": 0.05, + "reward_velocity": 0.0025, + "reward_lane_align": 0.025, + "reward_lane_center": 0.0038, + "reward_timestep": 0.000025, + } + + if simulation_mode == "gigaflow": + eval_overrides = { + "env": { + **common_env, + "simulation_mode": "gigaflow", + "resample_frequency": 500, + "scenario_length": 500, + "map_dir": "pufferlib/resources/drive/binaries/carla", + "num_maps": num_carla_maps, + "num_agents": num_agents, + "termination_mode": 0.0, + } + } + elif simulation_mode == "replay": + eval_overrides = { + "env": { + **common_env, + "simulation_mode": "replay", + "resample_frequency": 91, + "scenario_length": 91, + "map_dir": "pufferlib/resources/drive/binaries/eval", + "num_maps": num_scenarios, + "num_agents": num_agents, + "reward_traffic_light_violation": 1.0, + "termination_mode": 0.0, + } + } + else: + raise ValueError(f"Invalid simulation_mode: {simulation_mode}. Must be 'gigaflow' or 'replay'.") + + return eval_overrides + + +def verify_scenario_coverage(csv_path: str, num_scenarios: int) -> dict: + """ + Verify that episode_metrics.csv contains all expected scenarios. + + Args: + csv_path: Path to episode_metrics.csv + num_scenarios: Expected number of scenarios (e.g., 1000) + + Returns: + dict with keys: + - complete: bool - True if all scenarios present + - expected_count: number of expected scenarios + - found_count: number of unique scenarios found + - missing: sorted list of missing map names + - extra: sorted list of unexpected map names + - duplicates: dict mapping map_name -> count (if >1) + """ + df = pd.read_csv(csv_path) + + # Expected: map_000, map_001, ..., map_{num_scenarios-1} + expected = {f"map_{i:03d}" for i in range(num_scenarios)} + found = set(df["map_name"].unique()) + + missing = expected - found + extra = found - expected + + # Check for duplicates + counts = df["map_name"].value_counts() + duplicates = {name: count for name, count in counts.items() if count > 1} + + complete = len(missing) == 0 + + return { + "complete": complete, + "expected_count": num_scenarios, + "found_count": len(found), + "missing": sorted(missing), + "extra": sorted(extra), + "duplicates": duplicates, + } + + +def verify_scenario_coverage_gigaflow(csv_path: str, num_scenarios: int) -> dict: + """ + Verify gigaflow evaluation CSV: maps repeat across scenarios, so check total + row count rather than unique map names. + """ + df = pd.read_csv(csv_path) + total_rows = len(df) + complete = total_rows == num_scenarios + return { + "complete": complete, + "expected_count": num_scenarios, + "found_count": total_rows, + } + + +# Helper functions for eval_multi_scenarios and eval_multi_scenarios_render +def _export_metrics(global_infos, eval_folder, num_scenarios, quiet, verify_coverage=False, simulation_mode="replay"): + """Export episode and summary CSVs, return avg_infos dict.""" + # Episode Metrics + try: + df_episodes = pd.DataFrame(global_infos) + first_cols = ["episode_id", "map_name"] + other_cols = [col for col in df_episodes.columns if col not in first_cols] + new_col_order = first_cols + other_cols + df_episodes = df_episodes[new_col_order] + + if verify_coverage: + df_episodes = df_episodes.sort_values(by=["map_name", "episode_id"]) + + episode_csv_path = os.path.join(eval_folder, "episode_metrics.csv") + df_episodes.to_csv(episode_csv_path, index=False) + if not quiet: + print(f"\n✅ Per-episode metrics exported to {episode_csv_path}") + + if verify_coverage: + if simulation_mode == "gigaflow": + result = verify_scenario_coverage_gigaflow(episode_csv_path, num_scenarios) + if not quiet: + if result["complete"]: + print(f"✅ All {num_scenarios} episodes present in CSV") + else: + print( + f"⚠️ Episode count mismatch: expected {result['expected_count']}, found {result['found_count']}" + ) + else: + result = verify_scenario_coverage(episode_csv_path, num_scenarios) + if not quiet: + if result["complete"]: + print(f"✅ All {num_scenarios} scenarios present in CSV") + else: + print(f"⚠️ Scenario coverage incomplete:") + print(f" Expected: {result['expected_count']}, Found: {result['found_count']}") + if result["missing"]: + print(f" Missing ({len(result['missing'])}): {result['missing']}") + if result["extra"]: + print(f" Extra: {result['extra'][:10]}...") + if result["duplicates"]: + print(f" Duplicates: {len(result['duplicates'])} scenarios have multiple entries") + for name, count in sorted(result["duplicates"].items()): + print(f" {name}: {count} entries") + except Exception as e: + print(f"\n⚠️ Could not export per-episode CSV. Error: {e}") + print("Global infos data:", global_infos) + + # Evaluation average metrics + avg_infos = {} + for k, v in global_infos.items(): + if k == "num_scenarios": + avg_infos[k] = np.sum(v) + elif v and isinstance(v[0], numbers.Number): + avg_infos[k] = np.mean(v) + df_summary = pd.DataFrame(list(avg_infos.items()), columns=["Metric", "Average"]) + summary_csv_path = os.path.join(eval_folder, "evaluation_summary.csv") + df_summary.to_csv(summary_csv_path, index=False) + if not quiet: + print(f"\n✅ Average results exported to {summary_csv_path}") + print(df_summary.to_string(index=False)) + + return avg_infos + + +def _log_eval_metrics(logger, avg_infos, args, metric_prefix, quiet): + """Log metrics to TensorBoard/wandb if logger is provided.""" + if logger is None or args.get("global_step") is None: + return + + global_step = args["global_step"] + + # Create log dict with metric prefix (use / for TensorBoard grouping) + log_dict = {} + for metric_key, metric_value in avg_infos.items(): + if isinstance(metric_value, (int, float)): + log_dict[f"{metric_prefix}/{metric_key}"] = float(metric_value) + + # Log to TensorBoard if available + if hasattr(logger, "local_writer") and logger.local_writer: + for key, value in log_dict.items(): + logger.local_writer.add_scalar(key, value, global_step) + if not quiet: + print(f"✅ Logged {len(log_dict)} validation metrics to TensorBoard at step {global_step}") + + # Also log to wandb/neptune if available + if hasattr(logger, "log"): + logger.log(log_dict, global_step) + + +def eval_multi_scenarios( + env_name, args=None, vecenv=None, policy=None, logger=None, metric_prefix="validation", quiet=False +): + t0 = time.time() + + if args is None: + tmp_args = load_config(env_name) + model_path = tmp_args.get("load_model_path") + num_agents_eval = tmp_args["eval"]["num_agents"] + map_dir = tmp_args["eval"]["map_dir"] + + eval_overrides = build_eval_overrides( + tmp_args["eval_simulation"], + num_agents_eval, + tmp_args["num_scenarios"], + map_dir, + num_carla_maps=tmp_args.get("num_carla_maps", 6), + ) + args = load_eval_multi_scenarios_config(env_name, model_path, eval_overrides) + + # Reproducibility — same approach as training + seed = args["train"]["seed"] or 42 + np.random.seed(seed) + torch.manual_seed(seed) + + backend = args["vec"]["backend"] + num_scenarios = args["num_scenarios"] + + num_workers = min(args["vec"]["num_envs"], num_scenarios) + + # Distribute scenarios across workers + scenarios_per_worker = num_scenarios // num_workers + remainder = num_scenarios % num_workers + current_start = 0 + env_kwargs_list = [] + for j in range(num_workers): + worker_kwargs = copy.deepcopy(args["env"]) + worker_num_scenario = scenarios_per_worker + (1 if j < remainder else 0) + worker_kwargs["starting_map"] = current_start + worker_kwargs["num_eval_scenarios"] = worker_num_scenario + env_kwargs_list.append(worker_kwargs) + current_start += worker_num_scenario + + print(f"Distributing {num_scenarios} scenarios across {num_workers} workers:") + for j, w in enumerate(env_kwargs_list): + start = w["starting_map"] + count = w["num_eval_scenarios"] + print(f" Worker {j}: maps {start}-{start + count - 1} ({count} scenarios)") + + args["vec"] = dict(backend=backend, num_envs=num_workers, num_workers=num_workers, batch_size=num_workers) + + if vecenv is None: + package = args["package"] + module_name = "pufferlib.ocean" if package == "ocean" else f"pufferlib.environments.{package}" + env_module = importlib.import_module(module_name) + make_env = env_module.env_creator(env_name) + # Pass as lists to preserve per-worker env_kwargs + env_creators = [make_env] * num_workers + env_args = [[]] * num_workers + vecenv = pufferlib.vector.make(env_creators, env_args=env_args, env_kwargs=env_kwargs_list, **args["vec"]) + + policy = policy or load_policy(args, vecenv, env_name) + num_agents = vecenv.observation_space.shape[0] + device = args["train"]["device"] + + state = {} + if args["train"]["use_rnn"]: + state = dict( + lstm_h=torch.zeros(num_agents, policy.hidden_size, device=device), + lstm_c=torch.zeros(num_agents, policy.hidden_size, device=device), + ) + + # Folder for evaluation results + # For inline evaluation during training, use eval_results_dir in experiments folder + # For standalone evaluation, use benchmark folder + if "inline_eval" in args and args["inline_eval"] and "eval_results_dir" in args: + eval_folder = args["eval_results_dir"] + else: + # Standalone evaluation path (in benchmark folder) + model_path = args["load_model_path"] + model_filename_with_ext = os.path.basename(model_path) + model_name = os.path.splitext(model_filename_with_ext)[0] + models_dir = os.path.dirname(model_path) + experiment_dir = os.path.dirname(models_dir) + experiment_name = os.path.basename(experiment_dir) + eval_folder = os.path.join("benchmark", experiment_name, model_name, args["eval_simulation"]) + os.makedirs(eval_folder, exist_ok=True) + + global_infos = {} + scenarios_processed = 0 + vecenv.async_reset(42) + + ob, _, _, _, infos, _, _ = vecenv.recv() + with tqdm(total=num_scenarios, desc="Processing scenarios", disable=quiet) as pbar: + while scenarios_processed < num_scenarios: + # Reset LSTM + if args["train"]["use_rnn"]: + state = dict( + lstm_h=torch.zeros(num_agents, policy.hidden_size, device=device), + lstm_c=torch.zeros(num_agents, policy.hidden_size, device=device), + ) + + for _ in range(args["env"]["scenario_length"]): + with torch.no_grad(): + ob = torch.as_tensor(ob).to(device) + logits, _ = policy.forward_eval(ob, state) + action, _, _ = pufferlib.pytorch.sample_logits(logits, deterministic=True) + action = action.cpu().numpy().reshape(vecenv.action_space.shape) + + if isinstance(logits, torch.distributions.Normal): + action = np.clip(action, vecenv.action_space.low, vecenv.action_space.high) + + ob, _, _, _, infos = vecenv.step(action) + + # Multi-worker backend returns infos as list of lists (one per worker) + if infos and infos[0]: + for sub_env in infos: + for env_idx, summary in enumerate(sub_env): + env_map_name = summary["map_name"].split("/")[-1].split(".")[0] + summary["episode_id"] = env_idx + summary["map_name"] = env_map_name + scenarios_processed += 1 + pbar.update(1) + + for k, v in summary.items(): + if k not in global_infos: + global_infos[k] = [] + global_infos[k].append(v) + + avg_infos = _export_metrics( + global_infos, + eval_folder, + num_scenarios, + quiet, + verify_coverage=True, + simulation_mode=args["env"]["simulation_mode"], + ) + print(f"\nTotal evaluation time: {time.time() - t0:.2f} seconds for {num_scenarios} scenarios.") + _log_eval_metrics(logger, avg_infos, args, metric_prefix, quiet) + + # Close vectorized environment to avoid file descriptor leaks + vecenv.close() + + +def eval_multi_scenarios_render( + env_name, args=None, vecenv=None, policy=None, logger=None, metric_prefix="validation", quiet=False +): + # Set fixed seed for reproducible evaluation + np.random.seed(42) + torch.manual_seed(42) + + if args is None: + tmp_args = load_config(env_name) + model_path = tmp_args.get("load_model_path") + num_agents_eval = tmp_args["eval"]["num_agents"] + map_dir = tmp_args["eval"]["map_dir"] + eval_overrides = build_eval_overrides( + tmp_args["eval_simulation"], + num_agents_eval, + tmp_args["num_scenarios"], + map_dir, + num_carla_maps=tmp_args.get("num_carla_maps", 6), + ) + args = load_eval_multi_scenarios_config(env_name, model_path, eval_overrides) + + backend = args["vec"]["backend"] + if backend != "PufferEnv": + backend = "Serial" + + args["vec"] = dict(backend=backend, num_envs=1) + args["env"]["num_eval_scenarios"] = args["num_scenarios"] # first batch: fill as many scenarios as fit + + vecenv = vecenv or load_env(env_name, args) + + policy = policy or load_policy(args, vecenv, env_name) + num_agents = vecenv.observation_space.shape[0] + device = args["train"]["device"] + + state = {} + if args["train"]["use_rnn"]: + state = dict( + lstm_h=torch.zeros(num_agents, policy.hidden_size, device=device), + lstm_c=torch.zeros(num_agents, policy.hidden_size, device=device), + ) + + # Folder for evaluation results + # For inline evaluation during training, use eval_results_dir in experiments folder + # For standalone evaluation, use benchmark folder + if "inline_eval" in args and args["inline_eval"] and "eval_results_dir" in args: + eval_folder = args["eval_results_dir"] + else: + # Standalone evaluation path (in benchmark folder) + model_path = args["load_model_path"] + model_filename_with_ext = os.path.basename(model_path) + model_name = os.path.splitext(model_filename_with_ext)[0] + models_dir = os.path.dirname(model_path) + experiment_dir = os.path.dirname(models_dir) + experiment_name = os.path.basename(experiment_dir) + eval_folder = os.path.join("benchmark", experiment_name, model_name, args["eval_simulation"]) + os.makedirs(eval_folder, exist_ok=True) + + if args["render"]: + gif_folder = eval_folder + "/gif" + os.makedirs(gif_folder, exist_ok=True) + + global_infos = {} + num_scenarios = args["num_scenarios"] + + scenarios_processed = 0 + with tqdm(total=num_scenarios, desc="Processing scenarios", disable=quiet) as pbar: + while scenarios_processed < num_scenarios: + ob, _ = vecenv.reset() + + # Get initial states for all environments in the batch + scenarios = vecenv.get_state() + num_envs_in_batch = len(scenarios) + batch_start = scenarios_processed + + # Prepare batch_size_eval for the resample that fires at end of the step loop. + # That resample will load the NEXT batch, so cap it at remaining_after_this. + remaining_after_this = num_scenarios - scenarios_processed - num_envs_in_batch + vecenv.envs[0].batch_size_eval = max(1, remaining_after_this) + + map_names = [] + for env_idx in range(num_envs_in_batch): + map_names.append(scenarios[env_idx]["map_name"].split("/")[-1].split(".")[0]) + + # Reset LSTM + if args["train"]["use_rnn"]: + state = dict( + lstm_h=torch.zeros(num_agents, policy.hidden_size, device=device), + lstm_c=torch.zeros(num_agents, policy.hidden_size, device=device), + ) + + # Initialize histories as lists of lists (one list per environment) + if args["render"]: + agent_histories = [[] for _ in range(num_envs_in_batch)] + traffic_histories = [[] for _ in range(num_envs_in_batch)] + trajectory_histories = [[] for _ in range(num_envs_in_batch)] + all_agents_obs_histories = [[] for _ in range(num_envs_in_batch)] + + for t in range(args["env"]["scenario_length"]): + if args["render"]: + current_scenarios = vecenv.get_state() + start_obs_index = 0 + + # Loop through every environment in the batch to record its history + for env_idx in range(num_envs_in_batch): + env_scenario = current_scenarios[env_idx] + + agent_histories[env_idx].append( + pufferlib.viz.fill_agents_state( + env_scenario, use_trajectory="trajectory" in args["env"]["action_type"] + ) + ) + traffic_histories[env_idx].append(pufferlib.viz.fill_traffics_state(env_scenario, t)) + + if "trajectory" in args["env"]["action_type"]: + trajectory_histories[env_idx].append(pufferlib.viz.fill_trajectories(env_scenario, t)) + + # Collect observation dictionaries for ALL active agents in THIS environment at timestep t + if args["render_obs"]: + step_obs_dict = {} + if env_idx > 0: + start_obs_index += current_scenarios[env_idx - 1]["active_agent_count"] + for agent_idx in range(env_scenario["active_agent_count"]): + agent_id = env_scenario["active_agent_indices"][agent_idx] + step_obs_dict[int(agent_id)] = pufferlib.viz.extract_obs_frame( + ob, + env_scenario, + args, + timestep=t, + obs_index=start_obs_index + agent_idx, + agent_idx=agent_idx, + head_north=True, + ) + all_agents_obs_histories[env_idx].append(step_obs_dict) + + with torch.no_grad(): + ob = torch.as_tensor(ob).to(device) + logits, _ = policy.forward_eval(ob, state) + action, _, _ = pufferlib.pytorch.sample_logits(logits, deterministic=True) + action = action.cpu().numpy().reshape(vecenv.action_space.shape) + + if isinstance(logits, torch.distributions.Normal): + action = np.clip(action, vecenv.action_space.low, vecenv.action_space.high) + + ob, _, _, _, infos = vecenv.step(action) + + # Serial backend returns infos as single list (infos[0] is the env's info list) + if infos and infos[0]: + for env_idx, summary in enumerate(infos[0]): + env_map_name = summary["map_name"].split("/")[-1].split(".")[0] + summary["episode_id"] = batch_start + env_idx + summary["env_id"] = env_idx + summary["map_name"] = env_map_name + + for k, v in summary.items(): + if k not in global_infos: + global_infos[k] = [] + global_infos[k].append(v) + + if args["render"]: + # Loop through every environment to generate its specific HTML replay + for env_idx in range(num_envs_in_batch): + global_episode_id = batch_start + env_idx + # Ensure we don't render padding environments if num_scenarios isn't perfectly divisible by batch_size + if global_episode_id >= num_scenarios: + break + env_map_name = map_names[env_idx] + + pufferlib.viz.generate_interactive_replay( + current_scenarios[env_idx], + agent_histories[env_idx], + traffic_histories[env_idx], + trajectory_histories[env_idx], + all_agents_obs_histories[env_idx], + f"{gif_folder}/{env_map_name}_{global_episode_id:03d}.html", + head_north=True, + use_rear_axle=args["env"]["use_rear_axle"], + ) + + scenarios_processed += num_envs_in_batch + pbar.update(num_envs_in_batch) + + if args["render"]: + pufferlib.viz.build_gallery_index(gif_folder) + + avg_infos = _export_metrics(global_infos, eval_folder, num_scenarios, quiet, verify_coverage=False) + _log_eval_metrics(logger, avg_infos, args, metric_prefix, quiet) + + # Close vectorized environment to avoid file descriptor leaks + vecenv.close() + + +def dict_to_args(d, prefix=""): + args = [] + for k, v in d.items(): + key_str = f"{prefix}.{k}" if prefix else k + if isinstance(v, dict): + args.extend(dict_to_args(v, key_str)) + else: + args.append(f"--{key_str.replace('_', '-')}") + args.append(str(v)) + return args def sweep(args=None, env_name=None): args = args or load_config(env_name) - if not args["wandb"] and not args["neptune"]: - raise pufferlib.APIUsageError("Sweeps require either wandb or neptune") + if not args["wandb"] and not args["neptune"] and not args["tb"]: + raise pufferlib.APIUsageError("Sweeps require either wandb, neptune, or tb") method = args["sweep"].pop("method") try: @@ -1163,6 +2240,7 @@ def sweep(args=None, env_name=None): sweep = sweep_cls(args["sweep"]) points_per_run = args["sweep"]["downsample"] target_key = f"environment/{args['sweep']['metric']}" + for i in range(args["max_runs"]): seed = time.time_ns() & 0xFFFFFFFF random.seed(seed) @@ -1230,71 +2308,6 @@ def controlled_exp(env_name, args=None): print(f"\n✓ Completed all {len(combinations)} experiments") -def sanity(env_name, args=None): - args = args or load_config(env_name) - base_dir = Path(__file__).resolve().parent / "resources" / "drive" / "sanity" - json_dir = base_dir / "sanity_jsons" - binary_dir = base_dir / "sanity_binaries" - - available_maps = {p.stem: p for p in json_dir.glob("*.json")} - selected = args.get("sanity_maps") - if isinstance(selected, str): - selected = [selected] - - if selected: - missing = [name for name in selected if name not in available_maps] - if missing: - raise pufferlib.APIUsageError(f"Unknown sanity maps: {', '.join(sorted(missing))}") - chosen = [(name, available_maps[name]) for name in selected] - else: - chosen = sorted(available_maps.items()) - - if not chosen: - raise pufferlib.APIUsageError(f"No sanity maps found in {json_dir}") - - from pufferlib.ocean.drive.drive import load_map - - binary_dir.mkdir(parents=True, exist_ok=True) - binaries = [] - for idx, (name, json_path) in enumerate(chosen): - output_path = binary_dir / f"{name}.bin" - load_map(str(json_path), idx, str(output_path)) - binaries.append((name, output_path)) - - runs = [] - for name, binary in binaries: - map_zero = binary_dir / "map_000.bin" - shutil.copy2(binary, map_zero) - - run_args = { - **args, - "env": {**args["env"], "num_maps": 1, "map_dir": str(binary_dir)}, - "train": {**args["train"], "render_map": str(map_zero)}, - } - if run_args.get("wandb"): - run_args["wandb_name"] = name - - print(f"Running sanity map '{name}' from {binary.name}") - run_logs = train(env_name=env_name, args=run_args) - runs.append({"map": name, "logs": run_logs}) - - print("Sanity checklist:") - for entry in runs: - name = entry["map"] - logs = entry.get("logs") or [] - final = logs[-1] if logs else {} - score = final.get("environment/score") - if score is None: - status = "unknown (no score)" - elif score >= 0.95: - status = "✅ Solved" - else: - status = "❌ unsolved" - print(f" - {name}: {status} (score={score})") - - return runs - - def profile(args=None, env_name=None, vecenv=None, policy=None): args = load_config() vecenv = vecenv or load_env(env_name, args) @@ -1336,6 +2349,31 @@ def export(args=None, env_name=None, vecenv=None, policy=None, path=None, silent print(f"Saved {len(weights)} weights to {path}") +def ensure_drive_binary(): + """Delete existing visualize binary and rebuild it. This ensures the + binary is always up-to-date with the latest code changes. + """ + if os.path.exists("./visualize"): + print("Removing existing visualize binary...") + os.remove("./visualize") + + print("Building visualize binary...") + try: + result = subprocess.run( + ["bash", "scripts/build_ocean.sh", "visualize", "local"], capture_output=True, text=True, timeout=300 + ) + + if result.returncode == 0: + print("Successfully built visualize binary") + else: + print(f"Build failed: {result.stderr}") + raise RuntimeError("Failed to build visualize binary for rendering") + except subprocess.TimeoutExpired: + raise RuntimeError("Build timed out") + except Exception as e: + raise RuntimeError(f"Build error: {e}") + + def autotune(args=None, env_name=None, vecenv=None, policy=None): package = args["package"] module_name = "pufferlib.ocean" if package == "ocean" else f"pufferlib.environments.{package}" @@ -1359,6 +2397,8 @@ def load_policy(args, vecenv, env_name=""): env_module = importlib.import_module(module_name) device = args["train"]["device"] + if isinstance(device, int): + device = torch.device("cuda", device) if torch.cuda.is_available() else torch.device("cpu") policy_cls = getattr(env_module.torch, args["policy_name"]) policy = policy_cls(vecenv.driver_env, **args["policy"]) @@ -1409,8 +2449,21 @@ def load_config(env_name, config_dir=None): "--load-id", type=str, default=None, help="Kickstart/eval from from a finished Wandb/Neptune run" ) parser.add_argument( - "--render-mode", type=str, default="auto", choices=["auto", "human", "ansi", "rgb_array", "raylib", "None"] + "--render-mode", + type=str, + default="matplotlib", + choices=["auto", "human", "ansi", "rgb_array", "raylib", "matplotlib", "None"], + ) + parser.add_argument("--video-path", type=str, default="videos", help="Path to save videos") + parser.add_argument("--num_scenarios", type=int, default=3, help="Number of scenarios to eval") + parser.add_argument( + "--num_carla_maps", type=int, default=6, help="Number of CARLA maps to use in gigaflow mode (max 6)" + ) + parser.add_argument("--render", type=int, default=0, help="Rendering the evaluation") + parser.add_argument( + "--render_obs", type=int, default=0, help="Rendering the observation of first agent in evaluation" ) + parser.add_argument("--agent_index", nargs="*", type=int, default=None, help="Agent index to plot the observation") parser.add_argument("--save-frames", type=int, default=0) parser.add_argument("--gif-path", type=str, default="eval.gif") parser.add_argument("--fps", type=float, default=15) @@ -1421,9 +2474,12 @@ def load_config(env_name, config_dir=None): parser.add_argument("--neptune", action="store_true", help="Use neptune for logging") parser.add_argument("--neptune-name", type=str, default="pufferai") parser.add_argument("--neptune-project", type=str, default="ablations") + parser.add_argument("--tb", action="store_true", help="Use tensorboard for logging") parser.add_argument("--local-rank", type=int, default=0, help="Used by torchrun for DDP") parser.add_argument("--tag", type=str, default=None, help="Tag for experiment") - parser.add_argument("--sanity-maps", nargs="*", default=None, help="Optional list of sanity map base names to run") + parser.add_argument( + "--eval_simulation", type=str, default=None, help="Simulation mode for evaluation - gigaflow/replay" + ) args = parser.parse_known_args()[0] if config_dir is None: @@ -1475,11 +2531,20 @@ def puffer_type(value): prev[subkey] = value args["train"]["use_rnn"] = args["rnn_name"] is not None + + # Use World size to divide Num_Agents / minibatch size in DDP + if "LOCAL_RANK" in os.environ: + world_size = int(os.environ.get("WORLD_SIZE", 1)) + args["env"]["num_agents"] = args["env"]["num_agents"] // world_size + args["train"]["minibatch_size"] = args["train"]["minibatch_size"] // world_size + args["train"]["max_minibatch_size"] = args["train"]["max_minibatch_size"] // world_size + args["train"]["total_timesteps"] = args["train"]["total_timesteps"] // world_size + return args def main(): - err = "Usage: puffer [train, eval, sweep, controlled_exp, autotune, profile, export, sanity] [env_name] [optional args]. --help for more info" + err = "Usage: puffer [train, eval, sweep, controlled_exp, autotune, profile, export] [env_name] [optional args]. --help for more info" if len(sys.argv) < 3: raise pufferlib.APIUsageError(err) @@ -1489,6 +2554,11 @@ def main(): train(env_name=env_name) elif mode == "eval": eval(env_name=env_name) + elif mode == "eval_multi_scenarios": + eval_multi_scenarios(env_name=env_name) + elif mode == "eval_multi_scenarios_render": + eval_multi_scenarios_render(env_name=env_name) + print("") elif mode == "sweep": sweep(env_name=env_name) elif mode == "controlled_exp": @@ -1499,11 +2569,13 @@ def main(): profile(env_name=env_name) elif mode == "export": export(env_name=env_name) - elif mode == "sanity": - sanity(env_name=env_name) else: raise pufferlib.APIUsageError(err) if __name__ == "__main__": - main() + try: + main() + except Exception: + traceback.print_exc() + sys.exit(1) diff --git a/pufferlib/pytorch.py b/pufferlib/pytorch.py index 92d4af77a6..2ec10a5c5f 100644 --- a/pufferlib/pytorch.py +++ b/pufferlib/pytorch.py @@ -187,12 +187,14 @@ def entropy_probs(logits, probs): return -p_log_p.sum(-1) -def sample_logits(logits, action=None): +def sample_logits(logits, action=None, deterministic=False): is_discrete = isinstance(logits, torch.Tensor) if isinstance(logits, torch.distributions.Normal): batch = logits.loc.shape[0] if action is None: action = logits.sample().view(batch, -1) + if deterministic: + action = logits.loc.view(batch, -1) # TODO - DETERMINISTIC use mean action for eval log_probs = logits.log_prob(action.view(batch, -1)).sum(1) logits_entropy = logits.entropy().view(batch, -1).sum(1) @@ -210,9 +212,12 @@ def sample_logits(logits, action=None): probs = logits_to_probs(logits) if action is None: - probs = torch.nan_to_num(probs, 1e-8, 1e-8, 1e-8) - action = torch.multinomial(probs.reshape(-1, probs.shape[-1]), 1, replacement=True).int() - action = action.reshape(probs.shape[:-1]) + if deterministic: + action = torch.argmax(probs, -1) + else: + probs = torch.nan_to_num(probs, 1e-8, 1e-8, 1e-8) + action = torch.multinomial(probs.reshape(-1, probs.shape[-1]), 1, replacement=True).int() + action = action.reshape(probs.shape[:-1]) else: batch = logits[0].shape[0] action = action.view(batch, -1).T diff --git a/pufferlib/resources/drive/binaries/carla/opendrive__Town01.bin b/pufferlib/resources/drive/binaries/carla/opendrive__Town01.bin new file mode 100644 index 0000000000000000000000000000000000000000..7b05f6a185566005054808c780a01522f73fbe0b GIT binary patch literal 106288 zcmc${2Uyfh*Z&WTBGLsx?7e}MMQKu!%%~I-sp^usideb5177WF~58XlUF88UgAggN=$n z-Frr~`8x~RQnvwhuWv;?om)}!I1`$7#Ds1+Y(fVdGNFxQOsM*ICG#UhFQGJ8t`pz_ zXad!M?5p+yZma9O0d<^*x)f07QK&l_G&G7ahq}z6PIIW+95k4hS!a_<-h64WZ1dvs 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Skipping this evaluation: {e}") except Exception as e: print(f"Failed to run WOSAC evaluation: {type(e).__name__}: {e}") + + +def render_videos(config, vecenv, logger, epoch, global_step, bin_path): + """ + Generate and log training videos using C-based rendering. + + Args: + config: Configuration dictionary containing data_dir, env, and render settings + vecenv: Vectorized environment with driver_env attribute + logger: Logger object with run_id and optional wandb attribute + epoch: Current training epoch + global_step: Current global training step + bin_path: Path to the exported .bin model weights file + + Returns: + None. Prints error messages if rendering fails. + """ + if not os.path.exists(bin_path): + print(f"Binary weights file does not exist: {bin_path}") + return + + run_id = logger.run_id + model_dir = os.path.join(config["data_dir"], f"{config['env']}_{run_id}") + + # Now call the C rendering function + try: + # Create output directory for videos + video_output_dir = os.path.join(model_dir, "videos") + os.makedirs(video_output_dir, exist_ok=True) + + # Copy the binary weights to the expected location + expected_weights_path = "resources/drive/puffer_drive_weights.bin" + os.makedirs(os.path.dirname(expected_weights_path), exist_ok=True) + shutil.copy2(bin_path, expected_weights_path) + + # TODO: Fix memory leaks so that this is not needed + # Suppress AddressSanitizer exit code (temp) + env_vars = os.environ.copy() + env_vars["ASAN_OPTIONS"] = "exitcode=0" + + # Base command (without map/output paths) + base_cmd = ["xvfb-run", "-a", "-s", "-screen 0 1280x720x24", "./visualize"] + + # Render config flags + if config.get("show_grid", False): + base_cmd.append("--show-grid") + if config.get("obs_only", False): + base_cmd.append("--obs-only") + if config.get("show_lasers", False): + base_cmd.append("--lasers") + if config.get("show_human_logs", False): + base_cmd.append("--log-trajectories") + + env_cfg = getattr(vecenv, "driver_env", None) + if env_cfg is not None: + if getattr(env_cfg, "goal_radius", None) is not None: + base_cmd.extend(["--goal-radius", str(env_cfg.goal_radius)]) + if getattr(env_cfg, "init_steps", 0) > 0: + base_cmd.extend(["--init-steps", str(env_cfg.init_steps)]) + if getattr(env_cfg, "init_mode", None) is not None: + base_cmd.extend(["--init-mode", str(env_cfg.init_mode)]) + if getattr(env_cfg, "control_mode", None) is not None: + base_cmd.extend(["--control-mode", str(env_cfg.control_mode)]) + if getattr(env_cfg, "control_all_agents", False): + base_cmd.append("--pure-self-play") + if getattr(env_cfg, "deterministic_agent_selection", False): + base_cmd.append("--deterministic-selection") + + # Policy-controlled agents (prefer num_policy_controlled_agents, fallback to max_controlled_agents) + n_policy = getattr(env_cfg, "num_policy_controlled_agents", getattr(env_cfg, "max_controlled_agents", -1)) + try: + n_policy = int(n_policy) + except (TypeError, ValueError): + n_policy = -1 + if n_policy > 0: + base_cmd += ["--num-policy-controlled-agents", str(n_policy)] + + if getattr(env_cfg, "num_maps", False): + base_cmd.extend(["--num-maps", str(env_cfg.num_maps)]) + if getattr(env_cfg, "scenario_length", None): + base_cmd.extend(["--scenario-length", str(env_cfg.scenario_length)]) + + # Handle single or multiple map rendering + render_maps = config.get("render_map", None) + if render_maps is None: + render_maps = [None] + elif isinstance(render_maps, (str, os.PathLike)): + render_maps = [render_maps] + else: + # Ensure list-like + render_maps = list(render_maps) + + # Collect videos to log as lists so W&B shows all in the same step + videos_to_log_world = [] + videos_to_log_agent = [] + + for i, map_path in enumerate(render_maps): + cmd = list(base_cmd) # copy + if map_path is not None and os.path.exists(map_path): + cmd.extend(["--map-name", str(map_path)]) + + # Output paths (overwrite each iteration; then moved/renamed) + cmd.extend(["--output-topdown", "resources/drive/output_topdown.mp4"]) + cmd.extend(["--output-agent", "resources/drive/output_agent.mp4"]) + + result = subprocess.run(cmd, cwd=os.getcwd(), capture_output=True, text=True, timeout=120, env=env_vars) + + vids_exist = os.path.exists("resources/drive/output_topdown.mp4") and os.path.exists( + "resources/drive/output_agent.mp4" + ) + + if result.returncode == 0 or (result.returncode == 1 and vids_exist): + videos = [ + ( + "resources/drive/output_topdown.mp4", + f"epoch_{epoch:06d}_map{i:02d}_topdown.mp4" if map_path else f"epoch_{epoch:06d}_topdown.mp4", + ), + ( + "resources/drive/output_agent.mp4", + f"epoch_{epoch:06d}_map{i:02d}_agent.mp4" if map_path else f"epoch_{epoch:06d}_agent.mp4", + ), + ] + + for source_vid, target_filename in videos: + if os.path.exists(source_vid): + target_path = os.path.join(video_output_dir, target_filename) + shutil.move(source_vid, target_path) + # Accumulate for a single wandb.log call + if hasattr(logger, "wandb") and logger.wandb: + import wandb + + if "topdown" in target_filename: + videos_to_log_world.append(wandb.Video(target_path, format="mp4")) + else: + videos_to_log_agent.append(wandb.Video(target_path, format="mp4")) + else: + print(f"Video generation completed but {source_vid} not found") + else: + print(f"C rendering failed (map index {i}) with exit code {result.returncode}: {result.stdout}") + + # Log all videos at once so W&B keeps all of them under the same step + if hasattr(logger, "wandb") and logger.wandb and (videos_to_log_world or videos_to_log_agent): + payload = {} + if videos_to_log_world: + payload["render/world_state"] = videos_to_log_world + if videos_to_log_agent: + payload["render/agent_view"] = videos_to_log_agent + logger.wandb.log(payload, step=global_step) + + except subprocess.TimeoutExpired: + print("C rendering timed out") + except Exception as e: + print(f"Failed to generate GIF: {e}") + + finally: + # Clean up bin weights file + if os.path.exists(expected_weights_path): + os.remove(expected_weights_path) diff --git a/pufferlib/vector.py b/pufferlib/vector.py index bf5dc7460e..0df30f7ba3 100644 --- a/pufferlib/vector.py +++ b/pufferlib/vector.py @@ -5,7 +5,6 @@ import time import psutil -from pufferlib.emulation import GymnasiumPufferEnv, PettingZooPufferEnv from pufferlib import PufferEnv, set_buffers import pufferlib.spaces @@ -62,6 +61,7 @@ def num_envs(self): def __init__(self, env_creators, env_args, env_kwargs, num_envs, buf=None, seed=0, **kwargs): self.driver_env = env_creators[0](*env_args[0], **env_kwargs[0]) + self.driver_env.starting_map_counter = 0 # reset map counter for serial envs - only used in evaluation self.agents_per_batch = self.driver_env.num_agents * num_envs self.num_agents = self.agents_per_batch @@ -74,6 +74,7 @@ def __init__(self, env_creators, env_args, env_kwargs, num_envs, buf=None, seed= self.envs = [] ptr = 0 + child_seeds = np.random.SeedSequence(seed).spawn(len(env_creators)) if seed is not None else None for i in range(num_envs): end = ptr + self.driver_env.num_agents buf_i = dict( @@ -85,10 +86,15 @@ def __init__(self, env_creators, env_args, env_kwargs, num_envs, buf=None, seed= actions=self.actions[ptr:end], ) ptr = end - seed_i = seed + i if seed is not None else None + if seed is not None: + seed_i = int(child_seeds[i].generate_state(1)[0]) & 0x7FFFFFFF + else: + seed_i = None env = env_creators[i](*env_args[i], buf=buf_i, seed=seed_i, **env_kwargs[i]) self.envs.append(env) + # Close the initial driver_env used only for configuration (it's not in self.envs) + self.driver_env.close() self.driver_env = driver = self.envs[0] self.emulated = self.driver_env.emulated check_envs(self.envs, self.driver_env) @@ -119,11 +125,12 @@ def _avg_infos(self): def async_reset(self, seed=None): self.flag = RECV infos = [] + child_seeds = np.random.SeedSequence(seed).spawn(len(self.envs)) if seed is not None else None for i, env in enumerate(self.envs): if seed is None: ob, i = env.reset() else: - ob, i = env.reset(seed=seed + i) + ob, i = env.reset(seed=int(child_seeds[i].generate_state(1)[0]) & 0x7FFFFFFF) if isinstance(i, list): infos.extend(i) @@ -174,6 +181,9 @@ def recv(self): self.masks, ) + def get_state(self): + return [env.get_state() for env in self.envs][0] + def close(self): for env in self.envs: env.close() @@ -213,7 +223,7 @@ def _worker_process( if is_native and num_envs == 1: envs = env_creators[0](*env_args[0], **env_kwargs[0], buf=buf, seed=seed) else: - envs = Serial(env_creators, env_args, env_kwargs, num_envs, buf=buf, seed=seed * num_envs) + envs = Serial(env_creators, env_args, env_kwargs, num_envs, buf=buf, seed=seed) semaphores = np.ndarray(num_workers, dtype=np.uint8, buffer=shm["semaphores"]) notify = np.ndarray(num_workers, dtype=bool, buffer=shm["notify"]) @@ -369,11 +379,13 @@ def __init__( w_send_pipes, self.recv_pipes = zip(*[Pipe() for _ in range(num_workers)]) self.recv_pipe_dict = {p: i for i, p in enumerate(self.recv_pipes)} + worker_ss = np.random.SeedSequence(seed).spawn(num_workers) if seed is not None else [None] * num_workers + worker_seeds = [int(ss.generate_state(1)[0]) & 0x7FFFFFFF if ss is not None else None for ss in worker_ss] + self.processes = [] for i in range(num_workers): start = i * envs_per_worker end = start + envs_per_worker - seed_i = seed + i if seed is not None else None p = Process( target=_worker_process, args=( @@ -392,7 +404,7 @@ def __init__( w_recv_pipes[i], self.shm, is_native, - seed_i, + worker_seeds[i], ), ) p.start() @@ -525,10 +537,12 @@ def async_reset(self, seed=0): self.infos = [[] for _ in range(self.num_workers)] self.buf["semaphores"][:] = RESET + worker_ss = ( + np.random.SeedSequence(seed).spawn(self.num_workers) if seed is not None else [None] * self.num_workers + ) for i in range(self.num_workers): - start = i * self.envs_per_worker - end = (i + 1) * self.envs_per_worker - self.send_pipes[i].send(seed + i) + s = int(worker_ss[i].generate_state(1)[0]) & 0x7FFFFFFF if worker_ss[i] is not None else None + self.send_pipes[i].send(s) def notify(self): self.buf["notify"][:] = True @@ -775,15 +789,14 @@ def make_seeds(seed, num_envs): def check_envs(envs, driver): - valid = (PufferEnv, GymnasiumPufferEnv, PettingZooPufferEnv) - if not isinstance(driver, valid): - raise pufferlib.APIUsageError(f"env_creator must be {valid}") + if not isinstance(driver, PufferEnv): + raise pufferlib.APIUsageError("env_creator must be PufferEnv") driver_obs = driver.single_observation_space driver_atn = driver.single_action_space for env in envs: - if not isinstance(env, valid): - raise pufferlib.APIUsageError(f"env_creators must be {valid}") + if not isinstance(env, PufferEnv): + raise pufferlib.APIUsageError("env_creators must be PufferEnv") obs_space = env.single_observation_space if obs_space != driver_obs: raise pufferlib.APIUsageError(f"\n{obs_space}\n{driver_obs} obs space mismatch") diff --git a/pufferlib/viz.py b/pufferlib/viz.py new file mode 100644 index 0000000000..bcfe83c9d4 --- /dev/null +++ b/pufferlib/viz.py @@ -0,0 +1,2174 @@ +"""Bird's Eye View visualization for PufferDrive scenarios using Matplotlib.""" + +import dataclasses +import weakref +from typing import Optional, Tuple + +import math +import matplotlib.figure +import matplotlib.patches as mpatches +import matplotlib.pyplot as plt +import numpy as np +from matplotlib.collections import LineCollection, PatchCollection, PolyCollection +from matplotlib.patches import Circle, Polygon +import os +import json +import zlib +import base64 + +from pufferlib.ocean.drive import binding + + +COLORS = { + "pedestrian": "#2E8B57", + "cyclist": "#FF8C00", + "road_line": "#808080", + "road_edge": "#000000", + "lane": "#D3D3D3", + "crosswalk": "#E6C200", + "speed_bump": "#C90000", + "stop_sign": "#FF0000", + "inactive_agent": "#808080", + "background": "#F5F5F5", +} + +TRAFFIC_LIGHT_COLORS = { + 0: "#808080", + 1: "#FF0000", + 2: "#FFFF00", + 3: "#00FF00", + 4: "#FF0000", + 5: "#FFFF00", + 6: "#00FF00", + 7: "#FF6600", + 8: "#FFFF00", +} + +VEHICLE_COLORS = [ + "#681D00", + "#1F77B4", + "#FF7F0E", + "#2CA02C", + "#9467BD", + "#8C564B", + "#D47CBA", + "#BCBD22", + "#17BECF", + "#AEC7E8", + "#FFBB78", + "#98DF8A", + "#FF9896", + "#C5B0D5", + "#C49C94", + "#F7B6D2", + "#DBDB8D", + "#9EDAE5", +] + +_figure_cache: weakref.WeakValueDictionary = weakref.WeakValueDictionary() +_map_cache = {} + +MULTI_LANE_FULL_SCORE_TIME = binding.MULTI_LANE_FULL_SCORE_TIME +MULTI_LANE_HALF_SCORE_TIME = binding.MULTI_LANE_HALF_SCORE_TIME + +METRIC_LABELS = [ + "collision", + "offroad", + "red_light", + "stop_sign", + "reached_goal", + "lane_dist", + "lane_angle", + "comfort_violation", + "velocity_progress", + "speed_limit", + "ADE", + "progression", + "at_fault_collision", + "ttc", + "ttc_tfl", + "progress_ratio", + "multi_lane_time", + "multi_lane_score", +] + + +@dataclasses.dataclass +class VizConfig: + """Visualization config using radius and center for view bounds.""" + + center: Optional[Tuple[float, float]] = None + radius: Optional[float] = None + figsize: Tuple[float, float] = (20.0, 20.0) + dpi: int = 100 + show_agent_id: bool = True + show_routes: bool = False + show_goal: bool = True + show_sdc_paths: bool = False + show_trajectories: bool = False + goal_radius: float = 2.0 + follow_ego: bool = False + debug_metrics: bool = False + reuse_figure: bool = True + + def get_bounds(self, scenario) -> Tuple[float, float, float, float]: + map_corners = scenario.get("map_corners") + + if self.follow_ego: + ego_agent = scenario.get("agents")[-1] + cx, cy = ego_agent["sim_x"], ego_agent["sim_y"] + elif self.center is not None: + cx, cy = self.center + elif map_corners and len(map_corners) >= 4: + cx, cy = (map_corners[0] + map_corners[2]) / 2, (map_corners[1] + map_corners[3]) / 2 + else: + cx, cy = 0.0, 0.0 + + if self.radius is not None: + r = self.radius + elif map_corners and len(map_corners) >= 4: + r = max(map_corners[2] - map_corners[0], map_corners[3] - map_corners[1]) / 2 * 1.02 + else: + r = 100.0 + return (cx - r, cx + r, cy - r, cy + r) + + +def get_agent_color(agent_id, is_active=True): + return COLORS["inactive_agent"] if not is_active else VEHICLE_COLORS[agent_id % len(VEHICLE_COLORS)] + + +def _init_fig_ax(config: VizConfig, reuse_key: str = None, with_metrics: bool = False): + cache_key = f"{reuse_key}_{'metrics' if with_metrics else 'single'}" if reuse_key else None + + if config.reuse_figure and cache_key and cache_key in _figure_cache: + fig = _figure_cache[cache_key] + if fig and plt.fignum_exists(fig.number): + for ax in fig.axes: + ax.clear() + ax.set_facecolor(COLORS["background"]) + if with_metrics: + return fig, fig.axes[0], fig.axes[1] + return fig, fig.axes[0] + + if with_metrics: + fig, (ax_main, ax_metrics) = plt.subplots( + 1, 2, figsize=(config.figsize[0] * 1.5, config.figsize[1]), gridspec_kw={"width_ratios": [2, 1]} + ) + else: + fig, ax_main = plt.subplots() + fig.set_size_inches(config.figsize) + ax_metrics = None + + fig.set_dpi(config.dpi) + fig.set_facecolor(COLORS["background"]) + ax_main.set_facecolor(COLORS["background"]) + if ax_metrics: + ax_metrics.set_facecolor(COLORS["background"]) + + if config.reuse_figure and cache_key: + _figure_cache[cache_key] = fig + + if with_metrics: + return fig, ax_main, ax_metrics + return fig, ax_main + + +def _build_road_cache(road_elements): + lanes, lines, edges = [], [], [] + lane_dict = {} + for elem in road_elements or []: + if not isinstance(elem, dict): + continue + x, y, t = elem.get("x"), elem.get("y"), elem.get("type", 0) + if not x or not y: + continue + pts = np.column_stack((np.asarray(x), np.asarray(y))) + if 1 <= t <= 3: + lanes.append(pts) + lid = elem.get("id") + if lid is not None: + lane_dict[lid] = pts + elif 11 <= t <= 18: + lines.append(pts) + elif 21 <= t <= 23: + edges.append(pts) + return { + "lanes": lanes, + "lines": lines, + "edges": edges, + "lane_dict": lane_dict, + "collections": None, + } + + +def _render_roads(ax, road_cache): + if not road_cache: + return + collections = road_cache.get("collections") + if collections is None: + collections = [] + lanes = road_cache.get("lanes") or [] + lines = road_cache.get("lines") or [] + edges = road_cache.get("edges") or [] + if lanes: + collections.append(LineCollection(lanes, colors=COLORS["lane"], linewidths=0.8, alpha=0.7, zorder=1)) + if lines: + collections.append( + LineCollection( + lines, + colors=COLORS["road_line"], + linewidths=0.8, + alpha=0.6, + linestyles=(0, (5, 5)), + zorder=2, + ) + ) + if edges: + collections.append(LineCollection(edges, colors=COLORS["road_edge"], linewidths=0.8, alpha=0.8, zorder=2)) + road_cache["collections"] = collections + for collection in collections: + ax.add_collection(collection) + + +def _build_traffic_cache(traffic_elements): + traffic_lights = [] # (stop_line, states) + stop_signs = [] # stop_line endpoints + yield_signs = [] # stop_line endpoints + for elem in traffic_elements or []: + if not isinstance(elem, dict): + continue + t_type = elem.get("type", 1) + sl = elem.get("stop_line") + if sl is None or len(sl) < 4: + continue + if t_type == 1: + traffic_lights.append({"stop_line": sl, "states": elem.get("states", [])}) + elif t_type == 2: + stop_signs.append(sl) + elif t_type == 3: + yield_signs.append(sl) + return { + "traffic_lights": traffic_lights, + "stop_signs": stop_signs, + "yield_signs": yield_signs, + } + + +def _render_traffic(ax, traffic_cache, timestep): + if not traffic_cache: + return + # Traffic lights — colored by state + for light in traffic_cache.get("traffic_lights", []): + sl = light["stop_line"] + states = light["states"] + state = int(states[timestep]) if states and len(states) > timestep else 0 + color = TRAFFIC_LIGHT_COLORS.get(state, "#808080") + ax.plot([sl[0], sl[3]], [sl[1], sl[4]], color=color, linewidth=3, solid_capstyle="butt", alpha=0.9, zorder=15) + + # Stop signs — red/black striped + for sl in traffic_cache.get("stop_signs", []): + ax.plot([sl[0], sl[3]], [sl[1], sl[4]], color="black", linewidth=4, solid_capstyle="butt", alpha=0.9, zorder=15) + ax.plot( + [sl[0], sl[3]], + [sl[1], sl[4]], + color="#FF0000", + linewidth=2.5, + solid_capstyle="butt", + alpha=0.9, + zorder=15, + linestyle=(0, (3, 2)), + ) + + # Yield signs — yellow/black striped + for sl in traffic_cache.get("yield_signs", []): + ax.plot([sl[0], sl[3]], [sl[1], sl[4]], color="black", linewidth=4, solid_capstyle="butt", alpha=0.9, zorder=15) + ax.plot( + [sl[0], sl[3]], + [sl[1], sl[4]], + color="#FFD700", + linewidth=2.5, + solid_capstyle="butt", + alpha=0.9, + zorder=15, + linestyle=(0, (3, 2)), + ) + + +def _render_routes(ax, agents, lane_dict, active_indices): + if not agents or not lane_dict: + return + + active_set = set(active_indices or []) + segments_by_color = {} + for idx, agent in enumerate(agents): + if not isinstance(agent, dict) or idx not in active_set: + continue + route = agent.get("route", []) + if not route: + continue + color = get_agent_color(agent.get("id", idx)) + segs = segments_by_color.setdefault(color, []) + for lid in route: + if lid in lane_dict: + segs.append(lane_dict[lid]) + + for color, segs in segments_by_color.items(): + if segs: + ax.add_collection(LineCollection(segs, colors=color, linewidths=2.0, alpha=0.6, linestyles="--", zorder=5)) + + +def _render_agents(ax, agents, active_indices, static_indices, config, px_per_meter, use_rear_axle=False): + if not agents: + return + active_set, static_set = set(active_indices or []), set(static_indices or []) + vehicles = [] + vehicle_lengths = [] + vehicle_widths = [] + vehicle_headings = [] + vehicle_colors = [] + vehicle_edges = [] + text_items = [] + goal_points = [] + goal_colors = [] + ped_patches = [] + cyclist_patches = [] + font_size = max(12, int(px_per_meter / 5)) + + for idx, agent in enumerate(agents): + if idx not in active_set and idx not in static_set: + continue + if not agent.get("sim_valid"): + continue + x, y = agent.get("sim_x"), agent.get("sim_y") + if x is None or y is None: + continue + + agent_type = agent.get("type", 1) + agent_id = agent.get("id", idx) + is_active = idx in active_set + color = get_agent_color(agent_id, is_active) + edge = "black" if is_active else COLORS["inactive_agent"] + + if agent_type == 1: + if agent.get("stopped"): + color = "red" + length = agent.get("sim_length", 4) + width = agent.get("sim_width", 2) + heading = agent.get("sim_heading", 0) + + vehicles.append((x, y)) + vehicle_lengths.append(length) + vehicle_widths.append(width) + vehicle_headings.append(heading) + vehicle_colors.append(color) + vehicle_edges.append(edge) + + if config.show_agent_id: + text_items.append((x, y + width, str(agent_id))) + + if config.show_goal and is_active: + gx, gy = agent.get("goal_position_x"), agent.get("goal_position_y") + if gx is not None and gy is not None: + goal_points.append((gx, gy)) + goal_colors.append(color) + elif agent_type == 2: + ped_patches.append( + Circle( + (x, y), + radius=0.5, + facecolor=COLORS["pedestrian"], + edgecolor="black", + linewidth=0.7, + alpha=0.85, + zorder=10, + ) + ) + elif agent_type == 3: + cyclist_patches.append( + Circle( + (x, y), + radius=0.8, + facecolor=COLORS["cyclist"], + edgecolor="black", + linewidth=1.5, + alpha=0.85, + zorder=10, + ) + ) + + if vehicles: + centers = np.asarray(vehicles, dtype=float) + lengths = np.asarray(vehicle_lengths, dtype=float) + widths = np.asarray(vehicle_widths, dtype=float) + headings = np.asarray(vehicle_headings, dtype=float) + cos_h = np.cos(headings) + sin_h = np.sin(headings) + half_l = lengths / 2.0 + half_w = widths / 2.0 + base = np.stack( + ( + np.stack((half_l, half_w), axis=1), + np.stack((half_l, -half_w), axis=1), + np.stack((-half_l, -half_w), axis=1), + np.stack((-half_l, half_w), axis=1), + ), + axis=1, + ) + rot_x = base[:, :, 0] * cos_h[:, None] - base[:, :, 1] * sin_h[:, None] + rot_y = base[:, :, 0] * sin_h[:, None] + base[:, :, 1] * cos_h[:, None] + polys = np.stack((rot_x, rot_y), axis=2) + centers[:, None, :] + + ax.add_collection( + PolyCollection( + polys, + facecolors=vehicle_colors, + edgecolors=vehicle_edges, + linewidths=0.7, + alpha=0.8, + zorder=10, + ) + ) + + dx = lengths * 0.6 * cos_h + dy = lengths * 0.6 * sin_h + segments = np.stack((centers, centers + np.stack((dx, dy), axis=1)), axis=1) + ax.add_collection(LineCollection(segments, colors=vehicle_colors, linewidths=0.7, zorder=11)) + + head_len = widths * 0.25 + head_half_width = widths * 0.2 + tip = centers + np.stack((dx, dy), axis=1) + dir_vec = np.stack((cos_h, sin_h), axis=1) + perp_vec = np.stack((-sin_h, cos_h), axis=1) + base_center = tip - dir_vec * head_len[:, None] + left = base_center + perp_vec * head_half_width[:, None] + right = base_center - perp_vec * head_half_width[:, None] + arrows = np.stack((tip, left, right), axis=1) + ax.add_collection( + PolyCollection( + arrows, + facecolors=vehicle_colors, + edgecolors="black", + linewidths=0.3, + zorder=12, + ) + ) + + if text_items: + for x, y, text in text_items: + ax.text( + x, + y, + text, + fontsize=font_size, + color="black", + ha="center", + va="bottom", + fontweight="bold", + zorder=12, + ) + + if goal_points: + gx, gy = zip(*goal_points) + ax.scatter(gx, gy, s=20, c=goal_colors, marker="o", zorder=13) + goal_patches = [Circle((x, y), radius=config.goal_radius) for x, y in goal_points] + ax.add_collection( + PatchCollection( + goal_patches, + facecolors="none", + edgecolors=goal_colors, + linewidths=1.0, + linestyles="--", + zorder=13, + ) + ) + + if ped_patches: + ax.add_collection(PatchCollection(ped_patches, match_original=True)) + if cyclist_patches: + ax.add_collection(PatchCollection(cyclist_patches, match_original=True)) + + +def _render_paths(ax, scenario): + """Render SDC planned paths.""" + for idx in range(scenario["active_agent_count"]): + x = np.array([item["x"] for item in scenario["sdc_paths"][idx]["waypoints"]]) + y = np.array([item["y"] for item in scenario["sdc_paths"][idx]["waypoints"]]) + init_idx = scenario["agents"][scenario["active_agent_indices"][idx]]["closest_path_idx_wp"] + end_idx = min(init_idx + 20, scenario["sdc_paths"][idx]["num_waypoints"] - 1) + agent_id = scenario["agents"][scenario["active_agent_indices"][idx]]["id"] + color = get_agent_color(agent_id, is_active=True) + ax.scatter(x[init_idx:end_idx], y[init_idx:end_idx], color=color, s=20) + + +def _render_trajectories(ax, scenario): + for idx in range(scenario["active_agent_count"]): + wps = scenario["trajectory_waypoints_global"][idx]["waypoints"] + x = np.array([item["x"] for item in wps]) + y = np.array([item["y"] for item in wps]) + heading = np.array([item["heading"] for item in wps]) + ax.scatter(x, y, color=np.array([0, 100, 0]) / 255.0, s=20) + ax.quiver( + x, + y, + np.cos(heading), + np.sin(heading), + color=np.array([0, 100, 0]) / 255.0, + scale_units="xy", # Use data coordinates for scaling + scale=1.0, # A scale of 1.0 means arrows of length (U,V) are plotted as such + width=0.005, + ) + + +def _render_debug_metrics_table(ax, agents, active_agent_indices, px_per_meter=10.0): + """Render a table of per-agent metrics for debugging.""" + font_size = max(10, int(px_per_meter / 5)) + + if not agents or not active_agent_indices: + ax.text(0.5, 0.5, "No active agents", ha="center", va="center", fontsize=font_size) + ax.axis("off") + return + + active_set = set(active_agent_indices) + + # Gather metrics for active agents + metrics_data = [] + for idx, agent in enumerate(agents): + if idx not in active_set: + continue + agent_id = agent["id"] + vx, vy = agent.get("sim_vx", 0), agent.get("sim_vy", 0) + speed = np.sqrt(vx**2 + vy**2) + current_lane_id = agent.get("current_lane_index", -1) + metrics = agent.get("metrics_array", [0.0] * len(METRIC_LABELS)) + metrics_data.append( + { + "id": agent_id, + "current_lane": current_lane_id, + "speed": speed, + "lane_dist": metrics[5], + "lane_head": metrics[6], + "offroad": metrics[1], + "collision": metrics[0], + "comfort": metrics[7], + "red_light": metrics[2], + "at_fault": metrics[12], + "ttc": metrics[13], + "ttc_tfl": metrics[14], + "progress": metrics[15], + "ml_time": metrics[16] if len(metrics) > 16 else 0.0, + "color": get_agent_color(agent_id, is_active=True), + } + ) + + if not metrics_data: + ax.text(0.5, 0.5, "No active agents", ha="center", va="center", fontsize=font_size) + ax.axis("off") + return + + ax.axis("off") + ax.set_xlim(0, 1) + ax.set_ylim(0, 1) + + # Remove margins + ax.margins(0) + + # Table headers + headers = [ + "ID", + "Lane", + "LDist", + "LHead", + "Spd", + "Cmft", + "Off", + "Col", + "Red", + "AF", + "TTC", + "TTC_TFL", + "Prog", + "MLt", + ] + num_agents = len(metrics_data) + y_start, y_end = 0.95, 0.05 + row_height = min(0.06, (y_start - y_end) / (num_agents + 2)) + x_positions = np.linspace(0.02, 0.96, len(headers)) + for i, header in enumerate(headers): + ax.text(x_positions[i], y_start, header, fontsize=font_size + 2, fontweight="bold", va="top") + + for row_idx, data in enumerate(metrics_data): + y_pos = y_start - (row_idx + 1) * row_height + ax.text( + x_positions[0], y_pos, str(data["id"]), fontsize=font_size, color=data["color"], fontweight="bold", va="top" + ) + ax.text(x_positions[1], y_pos, f"{data['current_lane']:.0f}", fontsize=font_size, va="top") + ax.text(x_positions[2], y_pos, f"{data['lane_dist']:.2f}", fontsize=font_size, va="top") + ax.text(x_positions[3], y_pos, f"{data['lane_head']:.2f}", fontsize=font_size, va="top") + ax.text(x_positions[4], y_pos, f"{data['speed']:.1f}", fontsize=font_size, va="top") + ax.text( + x_positions[5], + y_pos, + f"{data['comfort']:.1f}", + fontsize=font_size, + color="red" if data["comfort"] > 0 else "green", + va="top", + ) + ax.text( + x_positions[6], + y_pos, + "+" if data["offroad"] else "-", + fontsize=font_size, + color="red" if data["offroad"] else "green", + va="top", + ) + ax.text( + x_positions[7], + y_pos, + "+" if data["collision"] else "-", + fontsize=font_size, + color="red" if data["collision"] else "green", + va="top", + ) + ax.text( + x_positions[8], + y_pos, + "+" if data["red_light"] else "-", + fontsize=font_size, + color="red" if data["red_light"] else "green", + va="top", + ) + ax.text( + x_positions[9], + y_pos, + "+" if data["at_fault"] else "-", + fontsize=font_size, + color="red" if data["at_fault"] else "green", + va="top", + ) + ax.text( + x_positions[10], + y_pos, + f"{data['ttc']:.2f}", + fontsize=font_size, + color="red" if data["ttc"] < 0.95 else "green", + va="top", + ) + ax.text( + x_positions[11], + y_pos, + f"{data['ttc_tfl']:.2f}", + fontsize=font_size, + color="red" if data["ttc_tfl"] < 0.95 else "green", + va="top", + ) + ax.text( + x_positions[12], + y_pos, + f"{data['progress']:.2f}", + fontsize=font_size, + color="green" if data["progress"] > 0.2 else "red", + va="top", + ) + ax.text( + x_positions[13], + y_pos, + f"{data['ml_time']:.1f}", + fontsize=font_size, + color="red" if data["ml_time"] > MULTI_LANE_FULL_SCORE_TIME else "green", + va="top", + ) + + ax.set_title("Active Agent Metrics + V-Max", fontsize=font_size + 4, fontweight="bold", pad=10) + + +def _get_cache_key(reuse_key): + return reuse_key + + +def _get_or_build_map_cache(cache_key, scenario): + if cache_key: + cache = _map_cache.get(cache_key) + map_name = scenario.get("map_name") + if cache and cache.get("map_name") == map_name: + return cache + road_cache = _build_road_cache(scenario.get("road_elements", [])) + traffic_cache = _build_traffic_cache(scenario.get("traffic_elements", [])) + cache = { + "map_name": map_name, + "road": road_cache, + "traffic": traffic_cache, + } + _map_cache[cache_key] = cache + return cache + + return { + "map_name": scenario.get("map_name"), + "road": _build_road_cache(scenario.get("road_elements", [])), + "traffic": _build_traffic_cache(scenario.get("traffic_elements", [])), + } + + +def plot_simulator_state( + scenario, + timestep: int = 0, + show_trajectories: bool = False, + simulation_mode: str = None, + reuse_key: str = None, + use_rear_axle: bool = False, +) -> np.ndarray: + """Render simulator state to RGB image array.""" + vis_radius = None if simulation_mode == "gigaflow" or simulation_mode is None else 75.0 + vis_config = VizConfig(radius=vis_radius, show_trajectories=show_trajectories) + + cache_key = _get_cache_key(reuse_key) + map_cache = _get_or_build_map_cache(cache_key, scenario) + + bounds = vis_config.get_bounds(scenario) + x_min, x_max, y_min, y_max = bounds + + px_per_meter = min( + vis_config.figsize[0] * vis_config.dpi / (x_max - x_min), + vis_config.figsize[1] * vis_config.dpi / (y_max - y_min), + ) + + if vis_config.debug_metrics: + fig, ax, ax_metrics = _init_fig_ax(vis_config, cache_key, with_metrics=True) + else: + fig, ax = _init_fig_ax(vis_config, cache_key, with_metrics=False) + ax_metrics = None + + ax.set_aspect("equal") + ax.set_title( + f"PufferDrive | {scenario.get('dataset_name', '')} | {scenario.get('scenario_id', '')} | t={timestep}", + fontsize=max(14, int(px_per_meter / 8)), + fontweight="bold", + ) + + _render_roads(ax, map_cache.get("road")) + _render_traffic(ax, map_cache.get("traffic"), timestep) + if vis_config.show_routes: + _render_routes( + ax, + scenario.get("agents", []), + map_cache.get("road", {}).get("lane_dict"), + scenario.get("active_agent_indices", []), + ) + if vis_config.show_sdc_paths: + _render_paths(ax, scenario) + if vis_config.show_trajectories and timestep > 0: + _render_trajectories(ax, scenario) + + _render_agents( + ax, + scenario.get("agents", []), + scenario.get("active_agent_indices", []), + scenario.get("static_agent_indices", []), + vis_config, + px_per_meter, + use_rear_axle, + ) + + ax.set_xlim(x_min, x_max) + ax.set_ylim(y_min, y_max) + + if vis_config.debug_metrics and ax_metrics: + _render_debug_metrics_table( + ax_metrics, + scenario.get("agents", []), + scenario.get("active_agent_indices", []), + px_per_meter=px_per_meter, + ) + + close_fig = not (vis_config.reuse_figure and cache_key) + return _img_from_fig(fig, close=close_fig) + + +def _img_from_fig(fig: matplotlib.figure.Figure, close: bool = True) -> np.ndarray: + fig.subplots_adjust(left=0.01, bottom=0.02, right=1.00, top=0.96) + fig.canvas.draw() + data = np.frombuffer(fig.canvas.tostring_argb(), dtype=np.uint8) + img = data.reshape(fig.canvas.get_width_height()[::-1] + (4,))[:, :, 1:] + if close: + plt.close(fig) + return img + + +def close_figure(reuse_key: str): + if not reuse_key: + return + for suffix in ("single", "metrics"): + cache_key = f"{reuse_key}_{suffix}" + fig = _figure_cache.pop(cache_key, None) + if fig and plt.fignum_exists(fig.number): + plt.close(fig) + _map_cache.pop(reuse_key, None) + + +def unpack_obs( + obs_flat, + dynamics_model: int = 0, + target_type: str = "static", + reward_conditioning: bool = False, + num_target_waypoints: int = 5, + max_partners: int = 16, + max_lane_segments: int = 16, + max_boundary_segments: int = 16, + max_traffic_lights: int = 16, + max_stop_signs: int = 10, + agent_idx: int = 0, +): + """ + Unpack the flattened observation into the ego state and visible state. + Args: + obs_flat: flattened observation tensor of shape (batch_size, obs_dim) + dynamics_model: 0 for CLASSIC, 1 for JERK + target_type: 0 for goal only, 1 for waypoints only, 2 for both + Return: + ego_state, partners_obs, lane_obs, boundary_obs, traffic_obs, gps_obs, include_goal, include_waypoints + """ + ego_dim = binding.EGO_FEATURES_JERK if dynamics_model == "jerk" else binding.EGO_FEATURES_CLASSIC + + # Partner obs + partner_feature_size = binding.PARTNER_FEATURES + # Road obs + road_feature_size = binding.ROAD_FEATURES + # Traffic light obs + traffic_feature_size = binding.TRAFFIC_LIGHT_FEATURES + # Stop sign obs + stop_sign_feature_size = binding.STOP_SIGN_FEATURES + + # Target obs + target_features = binding.STATIC_TARGET_FEATURES if target_type == "static" else binding.DYNAMIC_TARGET_FEATURES + target_dim = num_target_waypoints * target_features + + if max_stop_signs > 0: + ego_dim += 1 + + # Extract ego state + ego_state = obs_flat[:, :ego_dim] + + target_start = ego_dim + if reward_conditioning: + target_start += binding.NUM_REWARD_COEFS + + target_end = target_start + target_dim + target_obs = obs_flat[:, target_start:target_end] + target_obs = target_obs.reshape(-1, num_target_waypoints, target_features) + + # Extract partners + partners_start = target_end + partners_end = partners_start + max_partners * partner_feature_size + partners_obs = obs_flat[:, partners_start:partners_end] + partners_obs = partners_obs.reshape(-1, max_partners, partner_feature_size) + + # Extract lane elements + lane_start = partners_end + lane_end = lane_start + max_lane_segments * road_feature_size + lane_obs = obs_flat[:, lane_start:lane_end] + lane_obs = lane_obs.reshape(-1, max_lane_segments, road_feature_size) + + # Extract boundary elements + boundary_start = lane_end + boundary_end = boundary_start + max_boundary_segments * road_feature_size + boundary_obs = obs_flat[:, boundary_start:boundary_end] + boundary_obs = boundary_obs.reshape(-1, max_boundary_segments, road_feature_size) + + # Extract traffic lights + traffic_start = boundary_end + traffic_end = traffic_start + max_traffic_lights * traffic_feature_size + if max_traffic_lights > 0: + traffic_obs = obs_flat[:, traffic_start:traffic_end] + traffic_obs = traffic_obs.reshape(-1, max_traffic_lights, traffic_feature_size) + else: + traffic_obs = np.zeros((obs_flat.shape[0], 0, traffic_feature_size)) + + # Extract stop signs + stop_sign_start = traffic_end + stop_sign_end = stop_sign_start + max_stop_signs * stop_sign_feature_size + if max_stop_signs > 0: + stop_sign_obs = obs_flat[:, stop_sign_start:stop_sign_end] + stop_sign_obs = stop_sign_obs.reshape(-1, max_stop_signs, stop_sign_feature_size) + else: + stop_sign_obs = np.zeros((obs_flat.shape[0], 0, stop_sign_feature_size)) + + return ( + ego_state[agent_idx], + target_obs[agent_idx], + partners_obs[agent_idx], + lane_obs[agent_idx], + boundary_obs[agent_idx], + traffic_obs[agent_idx], + stop_sign_obs[agent_idx], + ) + + +def plot_observation( + obs, + dynamics_model="classic", + target_type="static", + reward_conditioning=False, + num_target_waypoints=10, + max_partners=16, + max_lane_segments=32, + max_boundary_segments=32, + max_traffic_lights=4, + max_stop_signs=4, + agent_idx=0, + use_rear_axle=False, +) -> np.ndarray: + """Plot observation in ego-centric frame. + + Args: + obs: flattened observation tensor + dynamics_model: 0 for CLASSIC, 1 for JERK + target_type: 0 for goal only, 1 for waypoints only, 2 for both + """ + fig, ax = plt.subplots(figsize=(20, 20)) + + ego_state, target_obs, partners_obs, lane_obs, boundary_obs, traffic_obs, stop_sign_obs = unpack_obs( + obs, + dynamics_model, + target_type, + reward_conditioning, + num_target_waypoints, + max_partners, + max_lane_segments, + max_boundary_segments, + max_traffic_lights, + max_stop_signs, + agent_idx, + ) + + if dynamics_model == "jerk": + ego_speed, ego_width, ego_length, steering_angle, a_long, a_lat, lcenter, lalign, speed_limit = ego_state + else: + ego_speed, ego_width, ego_length, lcenter, lalign, speed_limit = ego_state + + # Ego vehicle at origin + ax.add_patch( + mpatches.Rectangle( + (-ego_length / 2, -ego_width / 2), + ego_length, + ego_width, + facecolor="blue", + edgecolor="black", + linewidth=2, + alpha=0.7, + zorder=10, + ) + ) + + # Draw target waypoints + for i in range(target_obs.shape[0]): + if np.all(target_obs[i] == 0): + continue + wp_x, wp_y = target_obs[i][0], target_obs[i][1] + if target_type == "static": + color = "red" if i == 0 else "orange" + marker = "*" if i == 0 else "o" + s = 200 if i == 0 else 80 + else: + color = "magenta" + marker = "o" + s = 100 + ax.scatter(wp_x, wp_y, color=color, marker=marker, s=s, zorder=15) + + # Add dynamics info text for JERK model + ego_info = f"Speed: {ego_speed:.2f}\nLane Centering: {lcenter:.2f}\nLane Align: {lalign:.2f}\nSpeed Limit: {speed_limit:.2f}" + + if dynamics_model == "jerk": + ego_info += f"\nSteering: {steering_angle:.3f}\na_long: {a_long:.2f}\na_lat: {a_lat:.2f}" + + ax.text( + 0.02, + 0.98, + ego_info, + transform=ax.transAxes, + fontsize=10, + verticalalignment="top", + bbox=dict(boxstyle="round", facecolor="wheat", alpha=0.8), + ) + + # Partner agents + for i in range(partners_obs.shape[0]): + if np.all(partners_obs[i] == 0): + continue + rel_x, rel_y, width, length, heading_cos, heading_sin, speed = partners_obs[i] + heading = np.arctan2(heading_sin, heading_cos) + + rect = mpatches.Rectangle( + (-length / 2, -width / 2), + length, + width, + facecolor="gray", + edgecolor="black", + linewidth=1, + alpha=0.6, + zorder=9, + ) + rect.set_transform(plt.matplotlib.transforms.Affine2D().rotate(heading).translate(rel_x, rel_y) + ax.transData) + ax.add_patch(rect) + + # Road elements + count_lane = 0 + for i in range(lane_obs.shape[0]): + if np.all(lane_obs[i] == 0): + continue + count_lane += 1 + rel_x, rel_y, length, width, dir_cos, dir_sin = lane_obs[i] + color = "lightgrey" + ax.scatter(rel_x, rel_y, color=color, s=10, zorder=1) + ax.plot( + [rel_x + dir_cos * length / 2, rel_x - dir_cos * length / 2], + [rel_y + dir_sin * length / 2, rel_y - dir_sin * length / 2], + color=color, + linewidth=1, + zorder=1, + ) + + count_boundary = 0 + for i in range(boundary_obs.shape[0]): + if np.all(boundary_obs[i] == 0): + continue + count_boundary += 1 + rel_x, rel_y, length, width, dir_cos, dir_sin = boundary_obs[i] + color = "black" + ax.scatter(rel_x, rel_y, color=color, s=10, zorder=1) + ax.plot( + [rel_x + dir_cos * length / 2, rel_x - dir_cos * length / 2], + [rel_y + dir_sin * length / 2, rel_y - dir_sin * length / 2], + color=color, + linewidth=1, + zorder=1, + ) + + ax.text( + 0.12, + 0.95, + f"Lanes: {count_lane}\nBoundaries: {count_boundary}", + transform=ax.transAxes, + fontsize=10, + verticalalignment="top", + bbox=dict(boxstyle="round", facecolor="wheat", alpha=0.8), + ) + + # Traffic lights (stop lines) + for i in range(traffic_obs.shape[0]): + if np.all(traffic_obs[i] == 0): + continue + rel_x1, rel_y1, rel_x2, rel_y2, state_normalized = traffic_obs[i] + + if state_normalized == 0: + state = 4 + elif state_normalized == 1: + state = 2 + elif state_normalized == 2: + state = 6 + else: + state = 0 + + ax.plot( + [rel_x1, rel_x2], + [rel_y1, rel_y2], + color=TRAFFIC_LIGHT_COLORS[state], + linewidth=2.5, + solid_capstyle="round", + alpha=0.9, + zorder=12, + ) + + # Stop signs + for i in range(stop_sign_obs.shape[0]): + if np.all(stop_sign_obs[i] == 0): + continue + rel_x, rel_y, _ = stop_sign_obs[i] + + radius = 0.02 + angles = np.linspace(0, 2 * np.pi, 9) + octagon_x = rel_x + radius * np.cos(angles + np.pi / 8) + octagon_y = rel_y + radius * np.sin(angles + np.pi / 8) + octagon_points = np.column_stack((octagon_x, octagon_y)) + ax.add_patch( + plt.Polygon( + xy=octagon_points, + alpha=0.9, + facecolor=COLORS.get("stop_sign", "#808080"), + edgecolor="red", + linewidth=1, + zorder=12, + ) + ) + + ax.axis((-1, 1, -1, 1)) + ax.set_aspect("equal", adjustable="box") + ax.set_xlabel("X (ego frame)", fontsize=16) + ax.set_ylabel("Y (ego frame)", fontsize=16) + ax.set_title("Observation (Ego-Centric View)", fontsize=18, fontweight="bold") + # ax.grid(True, alpha=0.3) + return _img_from_fig(fig) + + +# HTML INTERACTIVE REPLAY +def fill_agents_state(scenario, use_trajectory=False): + current_agents_data = [] + active_indices = scenario.get("active_agent_indices", []) + + # Actions + if use_trajectory: + raw_actions = scenario.get("ctrl_trajectory_actions", []) + else: + raw_actions = scenario.get("actions", []) + action_map = {} + if raw_actions and len(raw_actions) == len(active_indices): + for i, agent_idx in enumerate(active_indices): + action_map[agent_idx] = raw_actions[i] + + for idx, agent in enumerate(scenario.get("agents", [])): + if not agent.get("sim_valid"): + continue + + agent_id = agent.get("id", idx) + is_active = idx in active_indices + + # Couleur + if agent.get("stopped", False): + color = "red" + else: + color = get_agent_color(agent_id, is_active) + req_acc = float(action_map[idx][0]) if idx in action_map else 0.0 + req_str = float(action_map[idx][1]) if idx in action_map else 0.0 + + # On arrondit tout pour alléger le JSON final + current_agents_data.append( + { + "id": int(agent_id), + "x": round(float(agent["sim_x"]), 2), + "y": round(float(agent["sim_y"]), 2), + "h": round(float(agent["sim_heading"]), 3), + "l": round(float(agent["sim_length"]), 2), + "w": round(float(agent["sim_width"]), 2), + "s": round(float(agent.get("sim_speed", 0)), 2), + "st": round(float(agent.get("sim_steering", 0)), 3), + "c": color, + # Commandes + "ra": round(req_acc, 2), + "rs": round(req_str, 2), + # Compact metrics array (M1..M18) + "m": [round(float(m), 2) for m in agent.get("metrics_array")], + } + ) + + return current_agents_data + + +def fill_traffics_state(scenario, timestep): + current_traffic_data = [] + traffic_elements = scenario.get("traffic_elements", []) + for elem in traffic_elements or []: + if not isinstance(elem, dict): + continue + + t_type = elem.get("type", 1) + sl = elem.get("stop_line") + if sl is None or len(sl) < 4: + continue + + if t_type == 1: + states = elem.get("states", []) + state = int(states[timestep]) if states and len(states) > timestep else 0 + color = TRAFFIC_LIGHT_COLORS.get(state, "#808080") + current_traffic_data.append({"type": "light", "stop_line": sl, "c": color}) + elif t_type == 2: + current_traffic_data.append({"type": "stop", "stop_line": sl, "c": "#FF0000", "c2": "#000000"}) + elif t_type == 3: + current_traffic_data.append({"type": "yield", "stop_line": sl, "c": "#FFD700", "c2": "#000000"}) + + return current_traffic_data + + +def fill_trajectories(scenario, timestep): + current_trajectories = [] + if timestep > 0: + traj_data = scenario.get("trajectory_waypoints_global", []) + active_count = scenario.get("active_agent_count", 0) + + # On itère seulement sur les agents actifs qui ont des trajectoires + for idx in range(min(len(traj_data), active_count)): + waypoints = traj_data[idx].get("waypoints", []) + pts = [] + for wp in waypoints: + pts.append([float(wp["x"]), float(wp["y"]), float(wp["heading"])]) + + current_trajectories.append(pts) + return current_trajectories + + +def extract_obs_frame(obs, scenario, args, timestep, obs_index=0, agent_idx=0, head_north=False): + ego_state, target_obs, partners_obs, lane_obs, boundary_obs, traffic_obs, stop_sign_obs = unpack_obs( + obs, + dynamics_model=args["env"]["dynamics_model"], + target_type=args["env"]["target_type"], + reward_conditioning=args["env"]["reward_conditioning"], + num_target_waypoints=args["env"]["num_target_waypoints"], + max_partners=args["env"]["max_partner_observations"], + max_lane_segments=args["env"]["max_lane_segment_observations"], + max_boundary_segments=args["env"]["max_boundary_segment_observations"], + max_traffic_lights=args["env"]["max_traffic_light_observations"], + max_stop_signs=args["env"]["max_stop_sign_observations"], + agent_idx=obs_index, + ) + + # --- Rotation Helper --- + def _rot(x, y): + """Rotates coordinates 90 degrees CCW if head_north is True.""" + return (-y, x) if head_north else (x, y) + + # --- Parse Ego --- + if args["env"]["dynamics_model"] == "jerk": + ego_speed, ego_width, ego_length, steering_angle, a_long, a_lat = ego_state[:6] + else: + ego_speed, ego_width, ego_length = ego_state[:3] + steering_angle, a_long, a_lat = 0.0, 0.0, 0.0 + + ego_data = { + "s": round(float(ego_speed), 3), + "w": round(float(ego_width), 3), + "l": round(float(ego_length), 3), + "st": round(float(steering_angle), 3), + "al": round(float(a_long), 3), + "alat": round(float(a_lat), 3), + } + + # --- Parse Road Segments --- + def parse_roads(roads): + res = [] + for r in roads: + if np.all(r == 0): + continue + x, y = r[0], r[1] + length, width = r[2], r[3] + cos_a, sin_a = r[4], r[5] + if head_north: + x_rot, y_rot = _rot(x, y) + cos_rot, sin_rot = _rot(cos_a, sin_a) + else: + x_rot, y_rot = x, y + cos_rot, sin_rot = cos_a, sin_a + res.append( + [ + round(float(x_rot), 4), + round(float(y_rot), 4), + round(float(length), 4), + round(float(width), 4), + round(float(cos_rot), 4), + round(float(sin_rot), 4), + ] + ) + return res + + # --- Parse Partners --- + parsed_partners = [] + for p in partners_obs: + if np.all(p == 0): + continue + + px, py = _rot(p[0], p[1]) + h = math.atan2(p[5], p[4]) + + if head_north: + h += math.pi / 2 + h = (h + math.pi) % (2 * math.pi) - math.pi + + parsed_partners.append( + { + "x": round(float(px), 3), + "y": round(float(py), 3), + "w": round(float(p[2]), 3), + "l": round(float(p[3]), 3), + "h": round(float(h), 3), + "s": round(float(p[6]), 3), + } + ) + + # --- Parse Traffic Lights --- + parsed_lights = [] + for t in traffic_obs: + if np.all(t == 0): + continue + lx, ly = _rot(t[0], t[1]) + parsed_lights.append({"x": round(float(lx), 3), "y": round(float(ly), 3), "state": int(t[-1])}) + + # --- Parse Stop Signs --- + parsed_stops = [] + for s in stop_sign_obs: + if np.all(s == 0): + continue + sx, sy = _rot(s[0], s[1]) + parsed_stops.append({"x": round(float(sx), 3), "y": round(float(sy), 3)}) + + # --- Parse Trajectory & GPS --- + traj_data = [] + if ("trajectory" in args["env"]["action_type"] or args.get("show_trajectories")) and timestep > 0: + wps = scenario["trajectory_waypoints_local"][agent_idx]["waypoints"] + for wp in wps: + wx, wy = _rot(float(wp["x"]) / 70.0, float(wp["y"]) / 70.0) + traj_data.append({"x": round(wx, 4), "y": round(wy, 4)}) + + gps_data = [] + for g in target_obs: + if np.all(g == 0): + continue + gx, gy = _rot(g[0], g[1]) + gps_data.append([round(float(gx), 3), round(float(gy), 3)]) + + return { + "ego": ego_data, + "partners": parsed_partners, + "lanes": parse_roads(lane_obs), + "bounds": parse_roads(boundary_obs), + "lights": parsed_lights, + "stops": parsed_stops, + "traj": traj_data, + "gps": gps_data, + } + + +def generate_interactive_replay( + scenario, + agent_history, + traffic_history, + trajectory_history, + all_agents_obs_history, + filename="replay.html", + head_north=False, + use_rear_axle=False, +): + # --- 0. COMPRESSION HELPER --- + def pack_and_compress_data(data, decimals=3): + # Recursively round all floats to save string space + def round_floats(o): + if isinstance(o, float): + return round(o, decimals) + if isinstance(o, dict): + return {k: round_floats(v) for k, v in o.items()} + if isinstance(o, (list, tuple)): + return [round_floats(v) for v in o] + return o + + # Dump without whitespace + compact_json = json.dumps(round_floats(data), separators=(",", ":")) + + # Compress using zlib (deflate) + compressed_bytes = zlib.compress(compact_json.encode("utf-8")) + + # Return as Base64 string for safe HTML embedding + return base64.b64encode(compressed_bytes).decode("ascii") + + # --- 1. METADATA --- + raw_dyn = scenario.get("dynamics_model", 0) + dyn_str = "Jerk" if raw_dyn == 1 else "Classic" + + metadata = { + "map_name": scenario.get("map_name", "Unknown"), + "scenario_id": scenario.get("scenario_id", "Unknown"), + "dynamics_model": dyn_str, + "target_type": scenario.get("target_type", "static"), + "active_indices": str(scenario.get("active_agent_indices", [])), + } + + # --- 2. MAP DATA --- + map_data = {"lanes": [], "lines": [], "edges": []} + for elem in scenario.get("road_elements", []): + if not isinstance(elem, dict): + continue + t = elem.get("type", 0) + if "x" in elem and "y" in elem: + pts = [[float(x), float(y)] for x, y in zip(elem["x"], elem["y"])] + if 1 <= t <= 3: + map_data["lanes"].append(pts) + elif 11 <= t <= 18: + map_data["lines"].append(pts) + elif 21 <= t <= 23: + map_data["edges"].append(pts) + + # --- 3. TEMPLATE HTML --- + html_template = """ + + + + PufferDrive Replay XXL + + + +

      Unpacking Replay Data...
      +
      +
      SPACE: Play | ARROWS: Step | ESC: Free | CLICK: Follow | ENTER: Search
      + +
      +
      +

      Scenario Info

      +
      Map
      -
      +
      ID
      -
      +
      +
      Step
      0
      +
      Camera Mode
      +
      Free Roam
      + +
      + +
      +
      ⚠ COLLISION ⚠
      +

      ☰ DRAG | Agent ?

      + +
      +
      +
      Speed
      +
      0.0 km/h
      +
      +
      +
      Req Acc/Str
      +
      0.0 / 0.0
      +
      +
      + + + +
      Metrics Table
      +
      + +
      Position (X/Y)
      +
      0 , 0
      +
      + +
      +
      ☰ DRAG TO MOVE | EGO-CENTRIC NN OBS
      + +
      + + + +
      + + + +
      +
      + + + + + + + """ + + # --- 4. PACKAGE, COMPRESS, AND INJECT --- + master_payload = { + "map": map_data, + "agents": agent_history, + "traffic": traffic_history, + "traj": trajectory_history, + "meta": metadata, + "obs": all_agents_obs_history, + "head_north": head_north, + "use_rear_axle": use_rear_axle, + } + + print("Compressing replay data, this might take a second...") + compressed_payload = pack_and_compress_data(master_payload, decimals=3) + + try: + final_html = html_template.replace("__COMPRESSED_PAYLOAD__", compressed_payload) + with open(filename, "w") as f: + f.write(final_html) + print(f"Success! Saved optimized replay to {filename}") + except Exception as e: + print(f"Error: {e}") + + +def build_gallery_index(folder_path="."): + # Assuming files still start with "map_" based on your example + files = [f for f in os.listdir(folder_path) if f.startswith("map_") and f.endswith(".html")] + + if not files: + print("No matching .html files found in this directory.") + return + + def sort_key(filename): + # 1. Strip the '.html' extension + name_no_ext = filename[:-5] + + # 2. Split from the right exactly once + # e.g., "map_000_000" -> ["map_000", "000"] + parts = name_no_ext.rsplit("_", 1) + + env_map_name = parts[0] + global_episode_id = int(parts[1]) + + # 3. Sort first by episode ID, then by map name + return (global_episode_id, env_map_name) + + files.sort(key=sort_key) + + # 3. Build the HTML template + html_content = """ + + + + + PufferDrive Replay Gallery + + + +
      +
      PUFFERDRIVE GALLERY
      + + + +
      + + + + + + + """ + + # 4. Inject the options into the dropdown + options_html = "\n".join( + [f'' for f in files] + ) + + final_html = html_content.replace("__OPTIONS__", options_html).replace("__FIRST__", files[0]) + + # 5. Save the file + index_path = os.path.join(folder_path, "index.html") + with open(index_path, "w") as f: + f.write(final_html) diff --git a/scripts/build_ocean.sh b/scripts/build_ocean.sh index f090291120..ea9c2322f2 100755 --- a/scripts/build_ocean.sh +++ b/scripts/build_ocean.sh @@ -1,6 +1,6 @@ #!/bin/bash -# Usage: ./build_env.sh pong [local|fast|web] +# Usage: ./build_ocean.sh visualize [local|fast] ENV=$1 MODE=${2:-local} @@ -11,72 +11,13 @@ if [ "$ENV" = "visualize" ]; then else SRC_DIR="pufferlib/ocean/$ENV" fi -WEB_OUTPUT_DIR="build_web/$ENV" + RAYLIB_NAME='raylib-5.5_macos' -BOX2D_NAME='box2d-macos-arm64' if [ "$PLATFORM" = "Linux" ]; then RAYLIB_NAME='raylib-5.5_linux_amd64' - BOX2D_NAME='box2d-linux-amd64' -fi -if [ "$MODE" = "web" ]; then - RAYLIB_NAME='raylib-5.5_webassembly' - BOX2D_NAME='box2d-web' -fi - -LINK_ARCHIVES="./$RAYLIB_NAME/lib/libraylib.a" -if [ "$ENV" = "impulse_wars" ]; then - LINK_ARCHIVES="$LINK_ARCHIVES ./$BOX2D_NAME/libbox2d.a" fi -# Create build output directory -mkdir -p "$WEB_OUTPUT_DIR" - -if [ "$MODE" = "web" ]; then - echo "Building $ENV for web deployment..." - - # Add MAX_AGENTS override for drive environment - EXTRA_FLAGS="" - if [ "$ENV" = "drive" ]; then - EXTRA_FLAGS="-DMAX_AGENTS=10" - echo "Setting MAX_AGENTS=10 for drive web build" - fi - - emcc \ - -o "$WEB_OUTPUT_DIR/game.html" \ - "$SRC_DIR/$ENV.c" \ - -O3 \ - -Wall \ - $LINK_ARCHIVES \ - -I./$RAYLIB_NAME/include \ - -I./$BOX2D_NAME/include \ - -I./$BOX2D_NAME/src \ - -I./pufferlib/extensions \ - -I./pufferlib \ - -L. \ - -L./$RAYLIB_NAME/lib \ - -sASSERTIONS=2 \ - -gsource-map \ - -s USE_GLFW=3 \ - -s USE_WEBGL2=1 \ - -s ASYNCIFY \ - -sFILESYSTEM \ - -s FORCE_FILESYSTEM=1 \ - --shell-file ./scripts/minshell.html \ - -sINITIAL_MEMORY=512MB \ - -sALLOW_MEMORY_GROWTH \ - -sSTACK_SIZE=512KB \ - -DNDEBUG \ - -DPLATFORM_WEB \ - -DGRAPHICS_API_OPENGL_ES3 \ - $EXTRA_FLAGS \ - --preload-file pufferlib/resources/$1@resources/$1 \ - #--preload-file pufferlib/resources/shared@resources/shared - echo "Web build completed: $WEB_OUTPUT_DIR/game.html" - echo "Preloaded files:" - echo " pufferlib/resources/$1@resources$1" - echo " pufferlib/resources/shared@resources/shared" - exit 0 -fi +LINK_ARCHIVES="./extern/$RAYLIB_NAME/lib/libraylib.a" # Detect available compiler and set compiler-specific flags if command -v clang >/dev/null 2>&1; then @@ -91,13 +32,11 @@ fi FLAGS=( -Wall - -I./$RAYLIB_NAME/include - -I./$BOX2D_NAME/include - -I./$BOX2D_NAME/src + -I./extern/$RAYLIB_NAME/include -I./pufferlib/extensions - -I./inih-r62 + -I./extern/inih-r62 "$SRC_DIR/$ENV.c" -o "$ENV" - ./inih-r62/ini.c + ./extern/inih-r62/ini.c $LINK_ARCHIVES -lm -lpthread @@ -105,7 +44,6 @@ FLAGS=( -DPLATFORM_DESKTOP ) - if [ "$PLATFORM" = "Darwin" ]; then FLAGS+=( -framework Cocoa @@ -119,18 +57,21 @@ echo ${FLAGS[@]} if [ "$MODE" = "local" ]; then echo "Building $ENV for local testing..." if [ "$PLATFORM" = "Linux" ]; then - # These important debug flags don't work on macos FLAGS+=( -fsanitize=address,undefined,bounds,pointer-overflow,leak -fno-omit-frame-pointer ) fi $COMPILER -g -O0 ${FLAGS[@]} +elif [ "$MODE" = "profile-debug" ]; then + echo "Building profiling-enabled $ENV (no inlining, perf-friendly)..." + $COMPILER -g -O1 -fno-inline -fno-omit-frame-pointer -DNDEBUG ${FLAGS[@]} + echo "Built to: $ENV" elif [ "$MODE" = "fast" ]; then echo "Building optimized $ENV for local testing..." - $COMPILER -pg -O2 -DNDEBUG ${FLAGS[@]} + $COMPILER -O2 -DNDEBUG ${FLAGS[@]} echo "Built to: $ENV" else - echo "Invalid mode specified: local|fast|web" + echo "Invalid mode: $MODE (use local|fast|profile-debug)" exit 1 fi diff --git a/scripts/eval/benchmark_comparison.py b/scripts/eval/benchmark_comparison.py new file mode 100644 index 0000000000..0928b10dfe --- /dev/null +++ b/scripts/eval/benchmark_comparison.py @@ -0,0 +1,57 @@ +import pandas as pd +from pathlib import Path + +BENCHMARK_DIR = Path("benchmark") +SUMMARY_FILE = "evaluation_summary.csv" + +DROP_METRICS = {"episode_id", "dnf_rate", "episode_return"} +META_COLS = [ + "model_name", + "xp", + "eval_mode", + "score", + "collision_rate", + "offroad_rate", + "red_light_violation_rate", + # "stop_sign_violation_rate", + "num_goals_reached", + "avg_speed_per_agent", +] + +rows = [] + +for model_dir in BENCHMARK_DIR.iterdir(): + if not model_dir.is_dir(): + continue + + for summary_path in model_dir.rglob(SUMMARY_FILE): + # model/xp/eval_mode/evaluation_summary.csv + try: + xp = summary_path.parents[1].name + eval_mode = summary_path.parent.name + except IndexError: + continue + + df = pd.read_csv(summary_path) # Metric | Average + + metrics = {metric: value for metric, value in zip(df["Metric"], df["Average"]) if metric not in DROP_METRICS} + + row = { + "model_name": model_dir.name, + "xp": xp, + "eval_mode": eval_mode, + **metrics, + } + rows.append(row) + +# Build dataframe +out_df = pd.DataFrame(rows) +out_df = out_df.round(3) + +# Enforce column order +metric_cols = [c for c in out_df.columns if c not in META_COLS] +out_df = out_df[META_COLS + metric_cols] + +out_df.to_csv("benchmark/benchmark_summary.csv", index=False) + +print("✅ Saved benchmark_all_summaries.csv") diff --git a/scripts/eval/run_all_eval.sh b/scripts/eval/run_all_eval.sh new file mode 100755 index 0000000000..b2e425a84e --- /dev/null +++ b/scripts/eval/run_all_eval.sh @@ -0,0 +1,72 @@ +#!/bin/bash + +BASE_DIR="experiments" +COMMAND_PREFIX="puffer eval_multi_scenarios puffer_drive" + +# Defaults +NUM_SCENARIOS=50 +RENDER=0 +RENDER_OBS=0 +EVAL_SIMULATION="replay" + +# Parse command-line arguments +while [[ $# -gt 0 ]]; do + case $1 in + --num_scenarios) + NUM_SCENARIOS="$2" + shift 2 + ;; + --eval_simulation) + EVAL_SIMULATION="$2" + shift 2 + ;; + --render) + RENDER="$2" + shift 2 + ;; + --render_obs) + RENDER_OBS="$2" + shift 2 + ;; + *) + echo "❌ Unknown argument: $1" + exit 1 + ;; + esac +done + +COMMAND_SUFFIX="--num_scenarios ${NUM_SCENARIOS} --render ${RENDER} --render_obs ${RENDER_OBS} --eval_simulation ${EVAL_SIMULATION}" + +if [ ! -d "$BASE_DIR" ]; then + echo "Error: Directory '$BASE_DIR' not found." + echo "Please run this script from the directory containing '$BASE_DIR/'" + exit 1 +fi + +echo "🚀 Starting evaluation for all experiments in '$BASE_DIR'..." +echo " num_scenarios = ${NUM_SCENARIOS}" +echo " eval_simulation = ${EVAL_SIMULATION}" +echo "---" + +for exp_path in ${BASE_DIR}/*/; do + MODELS_DIR="${exp_path}/models/" + echo "Processing experiment: ${exp_path}" + + LATEST_MODEL=$(ls -1 ${MODELS_DIR}*.pt 2>/dev/null | grep -v "trainer_state.pt" | sort -V | tail -n 1) + + if [ -z "${LATEST_MODEL}" ]; then + echo " [SKIP] No '.pt' model files found in ${MODELS_DIR}." + echo "---" + continue + fi + + echo " ✅ Found latest model: ${LATEST_MODEL}" + FULL_COMMAND="${COMMAND_PREFIX} --load-model-path ${LATEST_MODEL} ${COMMAND_SUFFIX}" + echo " ▶️ Executing:" + echo " ${FULL_COMMAND}" + + ${FULL_COMMAND} + echo "---" +done + +echo "🎉 All experiments processed." diff --git a/scripts/eval/run_all_latest_eval.py b/scripts/eval/run_all_latest_eval.py new file mode 100644 index 0000000000..b130183c3f --- /dev/null +++ b/scripts/eval/run_all_latest_eval.py @@ -0,0 +1,113 @@ +#!/usr/bin/env python3 + +import argparse +import shlex +import shutil +import subprocess +from pathlib import Path + + +def parse_args(): + parser = argparse.ArgumentParser( + description="Run `puffer eval puffer_drive` on the latest model from each run folder." + ) + parser.add_argument("--runs-dir", default="runs", help="Directory containing run folders") + parser.add_argument("--num_scenarios", type=int, default=20, help="Number of scenarios for eval") + parser.add_argument( + "--command-prefix", + default="puffer eval_multi_scenarios_render puffer_drive --eval_simulation gigaflow --render 1 --render_obs 1 --num_carla_maps 2", + help="Full base command to run before --load-model-path and --num_scenarios are appended", + ) + parser.add_argument("--dry-run", action="store_true", help="Print commands without executing") + parser.add_argument( + "--stop-on-error", + action="store_true", + help="Stop execution when one evaluation fails", + ) + return parser.parse_args() + + +def latest_model_in_run(run_dir): + models_dir = run_dir / "models" + if not models_dir.is_dir(): + return None + + candidates = [path for path in models_dir.glob("*.pt") if path.is_file() and "trainer_state" not in path.name] + if not candidates: + return None + + return max(candidates, key=lambda path: path.stat().st_mtime) + + +def list_run_dirs(runs_dir): + return sorted([path for path in runs_dir.iterdir() if path.is_dir()]) + + +def build_command(prefix, model_path, num_scenarios): + return [*shlex.split(prefix), "--load-model-path", str(model_path), "--num_scenarios", str(num_scenarios)] + + +def move_videos_to_run(run_dir, workspace_dir): + source = workspace_dir / "videos" + if not source.is_dir(): + print(" -> no videos directory found to move") + return + + target = run_dir / "videos" + if target.exists(): + shutil.rmtree(target) + + shutil.move(str(source), str(target)) + print(f" -> moved videos to {target}") + + +def main(): + args = parse_args() + runs_dir = Path(args.runs_dir) + + if not runs_dir.is_dir(): + raise SystemExit(f"Error: runs directory not found: {runs_dir}") + + run_dirs = list_run_dirs(runs_dir) + if not run_dirs: + raise SystemExit(f"Error: no run directories found in {runs_dir}") + + selected = [(run_dir, latest_model_in_run(run_dir)) for run_dir in run_dirs] + + selected = [(run_dir, model_path) for run_dir, model_path in selected if model_path is not None] + + if not selected: + raise SystemExit(f"Error: no model files found under {runs_dir}/*/models") + + print(f"Found {len(selected)} runs with models in {runs_dir}\n") + + failures = [] + for run_dir, model_path in selected: + cmd = build_command(args.command_prefix, model_path, args.num_scenarios) + cmd_str = " ".join(shlex.quote(part) for part in cmd) + print(f"[{run_dir.name}] {cmd_str}") + + if args.dry_run: + continue + + result = subprocess.run(cmd, cwd=Path.cwd()) + if result.returncode != 0: + failures.append((run_dir.name, model_path, result.returncode)) + print(f" -> failed with exit code {result.returncode}\n") + if args.stop_on_error: + break + else: + move_videos_to_run(run_dir, Path.cwd()) + print(" -> done\n") + + if failures: + print("Failures:") + for run_name, model_path, code in failures: + print(f"- {run_name}: {model_path} (exit={code})") + raise SystemExit(1) + + print("All evaluations completed.") + + +if __name__ == "__main__": + main() diff --git a/scripts/eval/run_failure_scenarios.py b/scripts/eval/run_failure_scenarios.py new file mode 100644 index 0000000000..dab3296016 --- /dev/null +++ b/scripts/eval/run_failure_scenarios.py @@ -0,0 +1,101 @@ +import pandas as pd +import subprocess + +# --- Configuration --- +# Set the path to your CSV file +CSV_FILE_PATH = "benchmark/puffer_drive_0jb42gn1/model_puffer_drive_000700/episode_metrics.csv" + +# Base command components +BASE_COMMAND_PREFIX = "puffer eval_multi_scenarios puffer_drive --load-model-path experiments/puffer_drive_0jb42gn1/best_models/model_puffer_drive_000700.pt --num_scenarios 500 --render 1 --render_obs 1" +BASE_COMMAND_SUFFIX = "" # Can be used for extra args if needed + + +def get_failed_scenario_indices(csv_path): + """ + Reads the episode metrics CSV and returns a list of scenario indices + (episode_id) where a failure (collision, offroad, or traffic light violation) occurred. + """ + try: + # 1. Read the CSV file + df = pd.read_csv(csv_path) + except FileNotFoundError: + print(f"Error: File not found at {csv_path}") + return None + except Exception as e: + print(f"Error reading CSV: {e}") + return None + + # Ensure required columns exist + required_columns = ["episode_id", "offroad_rate", "collision_rate", "red_light_violation_rate"] + + # Check if all necessary columns are present + if not all(col in df.columns for col in required_columns if col != "traffic_light_rate"): + print( + f"Error: CSV is missing one or more required columns: {', '.join(col for col in ['episode_id', 'offroad_rate', 'collision_rate', 'traffic_light_rate'] if col not in df.columns)}" + ) + return [] + + # 2. Define the failure condition + # Check if any of the specified rates is greater than 0 + failure_condition = (df["collision_rate"] > 0) | (df["offroad_rate"] > 0) + + if "traffic_light_rate" in df.columns: + failure_condition = failure_condition | (df["traffic_light_rate"] > 0) + + # 3. Filter and extract the episode_id (which acts as the map index) + failed_indices = df[failure_condition]["episode_id"].tolist() + + # The map indices for the command should be an integer list (or a list of strings of integers) + # The 'episode_id' column in the screenshot is an integer index starting from 0. + return [str(int(i)) for i in failed_indices] + + +def execute_puffer_command(indices): + """ + Constructs and executes the puffer eval command. + """ + if not indices: + print("\n✅ No failed scenarios found. Nothing to render.") + return + + # Join the indices with a space + indices_str = " ".join(indices) + + # Construct the final command string + final_command = f"{BASE_COMMAND_PREFIX} --map_indices {indices_str}" + + print("\n--- 🔨 Command to be executed ---") + print(final_command) + print("---------------------------------") + + # Execute the command + try: + print("\n🚀 Executing command via subprocess...") + # Use shell=True for convenience, but it's generally safer to pass a list + # For simplicity with a complex string command, we use shell=True here. + process = subprocess.run(final_command, shell=True, check=True, text=True, capture_output=True) + + print("\n✅ Command execution finished.") + print("--- Subprocess Output (Stdout) ---") + print(process.stdout) + + if process.stderr: + print("--- Subprocess Errors (Stderr) ---") + print(process.stderr) + + except subprocess.CalledProcessError as e: + print(f"\n❌ ERROR: Command failed with exit code {e.returncode}") + print("--- Subprocess Errors (Stderr) ---") + print(e.stderr) + except FileNotFoundError: + print("\n❌ ERROR: 'puffer' or related command not found. Ensure your environment is correctly set up.") + except Exception as e: + print(f"\n❌ An unexpected error occurred during command execution: {e}") + + +if __name__ == "__main__": + failed_scenario_indices = get_failed_scenario_indices(CSV_FILE_PATH) + + if failed_scenario_indices is not None: + print(f"\nFound {len(failed_scenario_indices)} failed scenarios: {', '.join(failed_scenario_indices)}") + execute_puffer_command(failed_scenario_indices) diff --git a/scripts/submit_cluster.py b/scripts/submit_cluster.py new file mode 100644 index 0000000000..7dc1977d24 --- /dev/null +++ b/scripts/submit_cluster.py @@ -0,0 +1,450 @@ +""" +Submitit script for launching PufferDrive training jobs on SLURM clusters. + +Example usage: + # Single job with config file + python scripts/submit_cluster.py \ + --save_dir /path/to/experiments \ + --compute_config scripts/cluster_configs/nyu_greene.yaml \ + --program_config scripts/cluster_configs/train_base.yaml + + # Sweep over learning rates + python scripts/submit_cluster.py \ + --save_dir /path/to/experiments \ + --compute_config scripts/cluster_configs/nyu_greene.yaml \ + --args learning_rate=1e-4:3e-4:1e-3 + + # Override compute settings + python scripts/submit_cluster.py \ + --save_dir /path/to/experiments \ + --compute_config scripts/cluster_configs/nyu_greene.yaml \ + --gpus 4 --time 120 + + # Dry run (preview commands without submitting) + python scripts/submit_cluster.py \ + --save_dir /path/to/experiments \ + --compute_config scripts/cluster_configs/nyu_greene.yaml \ + --dry + + # Run inside Singularity container (for glibc compatibility) + python scripts/submit_cluster.py \ + --save_dir /path/to/experiments \ + --compute_config scripts/cluster_configs/nyu_greene.yaml \ + --program_config scripts/cluster_configs/train_base.yaml \ + --container +""" + +import argparse +import hashlib +import json +import os +import pprint +import time +from typing import List, Optional, Tuple, Dict + +import yaml +import submitit + + +def parse_args(): + parser = argparse.ArgumentParser(description="Submit PufferDrive training jobs to SLURM cluster") + + # Job management + parser.add_argument("--save_dir", type=str, required=True, help="Base directory for experiment outputs") + parser.add_argument("--prefix", type=str, default=None, help="Prefix for job names and wandb run name") + parser.add_argument("--wandb-name", type=str, default=None, help="Wandb run name (defaults to --prefix)") + parser.add_argument("--wandb-group", type=str, default=None, help="Wandb group name (overrides program config)") + parser.add_argument("--wandb-project", type=str, default=None, help="Wandb project name (overrides program config)") + parser.add_argument("--dry", action="store_true", help="Dry run (don't submit, just print commands)") + + # Config files + parser.add_argument("--compute_config", type=str, default=None, help="YAML file with SLURM settings") + parser.add_argument("--program_config", type=str, default=None, help="YAML file with training args") + + # SLURM settings (override compute_config) + parser.add_argument("--account", type=str, help="SLURM account") + parser.add_argument("--partition", type=str, help="SLURM partition") + parser.add_argument("--cpus", type=int, default=None, help="CPUs per task") + parser.add_argument("--gpus", type=int, default=None, help="GPUs per node") + parser.add_argument("--nodes", type=int, default=None, help="Number of nodes") + parser.add_argument("--gpu_type", type=str, default=None, help="GPU type (a100/v100/etc)") + parser.add_argument("--nodelist", type=str, default=None, help="Specific nodes to use") + parser.add_argument("--mem", type=str, default=None, help="Memory per node (e.g., 32gb)") + parser.add_argument("--exclude", type=str, default="", help="Nodes to exclude") + parser.add_argument("--time", type=int, default=None, help="Time limit in minutes") + parser.add_argument("--task_per_node", type=int, default=1, help="Tasks per node") + parser.add_argument("--max_pjob", type=int, default=None, help="Max parallel jobs") + + # Program settings + parser.add_argument( + "--main", type=str, default="-m pufferlib.pufferl train puffer_drive", help="Main command to run" + ) + parser.add_argument( + "--args", type=str, nargs="+", default=None, help="Args to override/sweep (e.g., learning_rate=1e-4:3e-4)" + ) + + # Container settings + parser.add_argument("--container", action="store_true", help="Run inside Singularity container") + parser.add_argument( + "--container_image", + type=str, + default="/share/apps/images/cuda12.8.1-cudnn9.8.0-ubuntu24.04.2.sif", + help="Singularity image path", + ) + parser.add_argument( + "--container_overlay", + type=str, + default="/scratch/ev2237/containers/pufferdrive/overlay.ext3", + help="Singularity overlay path", + ) + + args = parser.parse_args() + return args + + +def process_main_args(main_args: Optional[List[str]], program_config: Optional[str]) -> Tuple[List[Dict], List[str]]: + """Process arguments and expand sweep syntax (colon-separated values).""" + from_config = {} + if program_config is not None: + from_config = yaml.safe_load(open(program_config, "r")) + print("Loaded base config:") + pprint.pprint(from_config) + + full_args = [from_config] + if main_args is None: + return full_args, [] + + override_keys = [] + for arg in main_args: + new_full_args = [] + if "=" not in arg: + raise ValueError(f"Invalid argument format: '{arg}'. Expected 'key=value' or 'key=val1:val2'") + key, vals = arg.split("=", 1) + override_keys.append(key) + vals = vals.split(":") + for val in vals: + for args in full_args: + new_args = args.copy() + new_args[key] = val + new_full_args.append(new_args) + full_args = new_full_args + + return full_args, override_keys + + +def generate_dict_hash(params_dict: Dict, hash_len: int = 7) -> str: + """Generate a short hash of the params dict for unique job naming.""" + hash_obj = hashlib.sha1(json.dumps(params_dict, sort_keys=True).encode()) + return hash_obj.hexdigest()[:hash_len] + + +def get_all_commands(args) -> Dict[str, Tuple[List[str], str]]: + """Generate all commands to run (expanding sweeps).""" + all_main_args, overrides = process_main_args(args.args, args.program_config) + name2commands = {} + + # Keys to exclude from auto-generated job name (paths, wandb config, common overrides) + name_skip_keys = { + "config", + "config_path", + "map_dir", + "env.map_dir", + "init_mode", + "env.init_mode", + "total_timesteps", + "train.total_timesteps", + "wandb_project", + "wandb_group", + "wandb_name", + } + # Boolean flags that don't take values (store_true) + boolean_flags = {"wandb", "neptune"} + + for main_args in all_main_args: + cmd = [] + name_entries = [] + + if args.program_config is not None: + name_entries.append(args.program_config.split("/")[-1].rsplit(".", 1)[0]) + + for key, val in main_args.items(): + # Convert underscores to dashes for CLI compatibility + cli_key = key.replace("_", "-") + + # Handle boolean flags that don't take values + if key in boolean_flags: + if val in (True, "True", "true", "1"): + cmd.append(f"--{cli_key}") + # Skip if False - don't add the flag at all + else: + cmd.append(f"--{cli_key}") + cmd.append(str(val)) + + if key in overrides and key not in name_skip_keys: + display_key = key.split(".")[-1] if "." in key else key + name_entries.append(f"{display_key}{val}") + + job_name = "_".join(name_entries) if name_entries else "pufferdrive" + # Sanitize job name + job_name = ( + job_name[:128] + .replace("{", "") + .replace("}", "") + .replace("'", "") + .replace('"', "") + .replace(":", "") + .replace("/", "") + ) + job_name += "_" + generate_dict_hash(main_args) + + if args.prefix is not None: + job_name = f"{args.prefix}_{job_name}" + + # Wandb overrides: explicit flags take priority, then prefix for name + wandb_name = args.wandb_name or args.prefix + if wandb_name is not None: + cmd.extend(["--wandb-name", wandb_name]) + if args.wandb_group is not None: + cmd.extend(["--wandb-group", args.wandb_group]) + if args.wandb_project is not None: + cmd.extend(["--wandb-project", args.wandb_project]) + + save_dir = os.path.join(args.save_dir, job_name) + name2commands[job_name] = (cmd, save_dir) + + return name2commands + + +def submit(args, job_name: str, command: List[str], save_dir: str, dry: bool): + """Submit a single job to SLURM via submitit.""" + # Load compute config + from_config = {} + if args.compute_config is not None: + from_config = yaml.safe_load(open(args.compute_config, "r")) + from_config = {k: v for k, v in from_config.items() if v is not None} + + # Override with CLI args + for key in ["account", "partition", "cpus", "gpus", "gpu_type", "mem", "nodes", "time", "nodelist", "exclude"]: + if vars(args)[key] is not None: + from_config[key] = vars(args)[key] + + print(">>> Compute config:") + pprint.pprint(from_config) + + # Set up executor + executor = submitit.AutoExecutor(folder=os.path.join(save_dir, "submitit")) + + # Build GRES string for GPUs + if from_config.get("gpu_type") is not None: + gres = f"gpu:{from_config['gpu_type']}:{from_config['gpus']}" + elif from_config.get("gpus") is not None: + gres = f"gpu:{from_config['gpus']}" + else: + gres = None + + if not os.path.exists(save_dir): + os.makedirs(save_dir) + + additional_parameters = {} + if from_config.get("nodelist") is not None: + additional_parameters["nodelist"] = from_config["nodelist"] + + executor.update_parameters( + slurm_account=from_config.get("account"), + slurm_partition=from_config.get("partition"), + cpus_per_task=from_config.get("cpus", 8) // args.task_per_node, + tasks_per_node=args.task_per_node, + nodes=from_config.get("nodes", 1), + slurm_gres=gres, + slurm_exclude=from_config.get("exclude") or None, + slurm_mem=from_config.get("mem"), + slurm_time=from_config.get("time", 60), + slurm_job_name=job_name, + slurm_additional_parameters=additional_parameters, + ) + + def launch_training(args, from_config, cmd, save_dir, project_root, container_config=None): + """Runs inside the SLURM allocation.""" + import glob + import os + import shutil + import subprocess + import sys + import submitit + + # Code isolation: symlink top-level entries, hard copy pufferlib/ source + # (symlink resources/ to avoid copying 3.7GB of maps/models). + isolated_root = os.path.join(save_dir, "code") + if os.path.exists(isolated_root): + version = 1 + while os.path.exists(f"{isolated_root}_v{version}"): + version += 1 + isolated_root = f"{isolated_root}_v{version}" + os.makedirs(isolated_root, exist_ok=True) + # Symlink each top-level entry (instant, avoids deep-copying data/) + for entry in os.listdir(project_root): + src = os.path.join(project_root, entry) + dst = os.path.join(isolated_root, entry) + if os.path.exists(dst) or os.path.islink(dst): + if os.path.isdir(dst) and not os.path.islink(dst): + shutil.rmtree(dst) + else: + os.remove(dst) + os.symlink(src, dst) + # Hard copy pufferlib/ so branch switches don't break running jobs. + # Previously used `cp -rs` (symlinks) which meant switching branches + # after submission would silently change the code running jobs use. + # We symlink resources/ (3.7GB of maps/models) to avoid slow copies, + # but hard copy everything else (source code, .so files). + pufferlib_dst = os.path.join(isolated_root, "pufferlib") + if os.path.islink(pufferlib_dst): + os.remove(pufferlib_dst) + elif os.path.isdir(pufferlib_dst): + shutil.rmtree(pufferlib_dst) + pufferlib_src = os.path.join(project_root, "pufferlib") + shutil.copytree( + pufferlib_src, + pufferlib_dst, + symlinks=False, + ignore=shutil.ignore_patterns("resources"), + ) + # Symlink resources/ (large static data, safe to share) + resources_src = os.path.join(pufferlib_src, "resources") + resources_dst = os.path.join(pufferlib_dst, "resources") + if os.path.isdir(resources_src): + os.symlink(resources_src, resources_dst) + project_root = isolated_root + + # Change to project directory and set up environment + os.chdir(project_root) + os.environ["PYTHONPATH"] = project_root + ":" + os.environ.get("PYTHONPATH", "") + + nodes = from_config.get("nodes", 1) + gpus = from_config.get("gpus", 1) + + # Parse the main command + main_parts = args.main.split() + + if nodes == 1: + base_cmd = [ + "torchrun", + "--standalone", + "--nproc_per_node", + str(gpus), + ] + main_parts + else: + env = submitit.JobEnvironment() + master_addr = env.hostnames[0] + os.environ["MASTER_ADDR"] = master_addr + os.environ["MASTER_PORT"] = "29500" + + base_cmd = [ + "torchrun", + "--nnodes", + str(nodes), + "--nproc_per_node", + str(gpus), + "--rdzv-backend", + "c10d", + "--rdzv-id", + str(env.job_id), + "--rdzv-endpoint", + f"{master_addr}:29500", + ] + main_parts + + # Add save_dir to command + full_cmd = base_cmd + cmd + ["--train.data-dir", save_dir] + + # Wrap with singularity if container mode is enabled + if container_config is not None: + env_setup = "source /ext3/env.sh" + # Redirect caches to scratch to avoid home quota issues + scratch_dir = os.environ.get("SCRATCH_DIR", "/scratch/" + os.environ.get("USER", "")) + cache_exports = ( + f"export XDG_CACHE_HOME={scratch_dir}/cache && " + f"export WANDB_CACHE_DIR={scratch_dir}/wandb_cache && " + f"export WANDB_CONFIG_DIR={scratch_dir}/wandb_config && " + f"export WANDB_DATA_DIR={scratch_dir}/wandb_data && " + f"export WANDB_DIR={scratch_dir}/wandb_data && " + f"mkdir -p {scratch_dir}/cache" + ) + inner_cmd = f"{env_setup} && {cache_exports} && cd {project_root} && " + " ".join(full_cmd) + full_cmd = [ + "singularity", + "exec", + "--nv", + "--overlay", + container_config["overlay"] + ":ro", # Read-only overlay for running + ] + # Bind mount SSL certificates for TLS verification (wandb, etc.) + for cert_path in ["/etc/ssl/certs", "/etc/pki"]: + if os.path.exists(cert_path): + full_cmd.extend(["--bind", f"{cert_path}:{cert_path}:ro"]) + full_cmd.extend( + [ + container_config["image"], + "bash", + "-c", + inner_cmd, + ] + ) + + print(f">>> Job: {job_name}") + print(f">>> Working directory: {project_root}") + print(f">>> Container: {container_config is not None}") + print(f">>> Command: {' '.join(full_cmd)}") + subprocess.run(full_cmd, check=True) + + # Get project root (directory containing this script's parent) + project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) + + # Build container config if enabled + container_config = None + if args.container: + container_config = { + "image": args.container_image, + "overlay": args.container_overlay, + } + print(f">>> Container mode enabled: {container_config['image']}") + + if not dry: + job = executor.submit(launch_training, args, from_config, command, save_dir, project_root, container_config) + print(f"Submitted job {job.job_id}: {job_name}") + return job + else: + print(f"[DRY RUN] Would submit: {job_name}") + return None + + +def wait_if_full(jobs: List, max_pjob: Optional[int]): + """Wait if we've hit the max parallel job limit.""" + + def remove_done_jobs(jobs): + for i in range(len(jobs) - 1, -1, -1): + if jobs[i].done(): + jobs.pop(i) + return len(jobs) + + if max_pjob is None: + return + + while remove_done_jobs(jobs) >= max_pjob and len(jobs) > 0: + print(f"Reached max jobs ({len(jobs)}), waiting...") + time.sleep(120) + + print(f"{len(jobs)} jobs remaining, launching new job") + + +if __name__ == "__main__": + args = parse_args() + name2commands = get_all_commands(args) + + print(f">>> Will submit {len(name2commands)} job(s)") + jobs = [] + for name, (cmd, save_dir) in name2commands.items(): + job = submit(args, name, cmd, save_dir, args.dry) + if job is not None: + jobs.append(job) + wait_if_full(jobs, args.max_pjob) + + print("All jobs launched!") diff --git a/setup.py b/setup.py index ea1692af66..6dc45cd240 100644 --- a/setup.py +++ b/setup.py @@ -1,114 +1,77 @@ # Debug command: # DEBUG=1 python setup.py build_ext --inplace --force -# CUDA_VISIBLE_DEVICES=None LD_PRELOAD=$(gcc -print-file-name=libasan.so) python3.12 -m pufferlib.clean_pufferl eval --train.device cpu from setuptools import find_packages, find_namespace_packages, setup, Extension import numpy import os -import glob import urllib.request import zipfile import tarfile import platform -import shutil import sys from setuptools.command.build_ext import build_ext from torch.utils import cpp_extension -from torch.utils.cpp_extension import ( - CppExtension, - CUDAExtension, -) +from torch.utils.cpp_extension import CppExtension, CUDAExtension, CUDA_HOME, ROCM_HOME + +# build cuda extension if torch can find CUDA or HIP/ROCM in the system +# may require `uv pip install --no-build-isolation` or `python setup.py build_ext --inplace` +BUILD_CUDA_EXT = bool(CUDA_HOME or ROCM_HOME) # Build with DEBUG=1 to enable debug symbols DEBUG = os.getenv("DEBUG", "0") == "1" NO_OCEAN = os.getenv("NO_OCEAN", "0") == "1" NO_TRAIN = os.getenv("NO_TRAIN", "0") == "1" -# Build raylib for your platform +EXTERNAL_LIB_DIR = "extern" +os.makedirs(EXTERNAL_LIB_DIR, exist_ok=True) + RAYLIB_URL = "https://github.com/raysan5/raylib/releases/download/5.5/" RAYLIB_NAME = "raylib-5.5_macos" if platform.system() == "Darwin" else "raylib-5.5_linux_amd64" RLIGHTS_URL = "https://raw.githubusercontent.com/raysan5/raylib/refs/heads/master/examples/shaders/rlights.h" - -# Fetch inih library INIH_URL = "https://github.com/benhoyt/inih/archive/refs/tags/{tag}.{ext}" -def download_raylib(platform, ext): - if not os.path.exists(platform): - print(f"Downloading Raylib {platform}") - urllib.request.urlretrieve(RAYLIB_URL + platform + ext, platform + ext) - if ext == ".zip": - with zipfile.ZipFile(platform + ext, "r") as zip_ref: - zip_ref.extractall() - else: - with tarfile.open(platform + ext, "r") as tar_ref: - if sys.version_info >= (3, 12): # Use secure call when python version >= 3.12 - tar_ref.extractall(filter="data") - else: - tar_ref.extractall() - - os.remove(platform + ext) - urllib.request.urlretrieve(RLIGHTS_URL, platform + "/include/rlights.h") - - -def download_library(url: str, name: str, tag: str, ext: str = "tar.gz", files_to_extract: list = None): - library_folder = name + "-" + tag - archive_file = library_folder + "." + ext - if not os.path.exists(library_folder): - filled_url = url.format(tag=tag, ext=ext) - print(f"Downloading {name}-{tag}") - urllib.request.urlretrieve(filled_url, archive_file) - if ext == "zip": - with zipfile.ZipFile(archive_file, "r") as zip_ref: - if files_to_extract: - members = [ - member_info.filename - for member_info in zip_ref.infolist() - if os.path.basename(member_info.filename) in files_to_extract - ] - zip_ref.extractall(members=members) - else: - zip_ref.extractall() - else: - with tarfile.open(archive_file, "r") as tar_ref: - kwargs = {} - if sys.version_info >= (3, 12): - kwargs["filter"] = "data" - if files_to_extract: - kwargs["members"] = [ - member for member in tar_ref.getmembers() if os.path.basename(member.name) in files_to_extract - ] - tar_ref.extractall(**kwargs) - os.remove(archive_file) +def download_raylib(name, ext): + dest = os.path.join(EXTERNAL_LIB_DIR, name) + if os.path.exists(dest): + return + print(f"Downloading Raylib {name}") + archive = name + ext + urllib.request.urlretrieve(RAYLIB_URL + archive, archive) + if ext == ".zip": + with zipfile.ZipFile(archive, "r") as zf: + zf.extractall(EXTERNAL_LIB_DIR) + else: + with tarfile.open(archive, "r") as tf: + tf.extractall(EXTERNAL_LIB_DIR, filter="data") if sys.version_info >= (3, 12) else tf.extractall( + EXTERNAL_LIB_DIR + ) + os.remove(archive) + urllib.request.urlretrieve(RLIGHTS_URL, os.path.join(dest, "include", "rlights.h")) + + +def download_inih(): + dest = os.path.join(EXTERNAL_LIB_DIR, "inih-r62") + if os.path.exists(dest): + return + print("Downloading inih") + url = INIH_URL.format(tag="r62", ext="tar.gz") + archive = "inih-r62.tar.gz" + urllib.request.urlretrieve(url, archive) + with tarfile.open(archive, "r") as tf: + members = [m for m in tf.getmembers() if os.path.basename(m.name) in ["ini.c", "ini.h"]] + tf.extractall(EXTERNAL_LIB_DIR, members=members, filter="data") if sys.version_info >= ( + 3, + 12, + ) else tf.extractall(EXTERNAL_LIB_DIR, members=members) + os.remove(archive) if not NO_OCEAN: - download_library(INIH_URL, "inih", "r62", files_to_extract=["ini.c", "ini.h"]) - download_raylib("raylib-5.5_webassembly", ".zip") - -BOX2D_URL = "https://github.com/capnspacehook/box2d/releases/latest/download/" -BOX2D_NAME = "box2d-macos-arm64" if platform.system() == "Darwin" else "box2d-linux-amd64" - - -def download_box2d(platform): - if not os.path.exists(platform): - ext = ".tar.gz" - - print(f"Downloading Box2D {platform}") - urllib.request.urlretrieve(BOX2D_URL + platform + ext, platform + ext) - with tarfile.open(platform + ext, "r") as tar_ref: - if sys.version_info >= (3, 12): - tar_ref.extractall(filter="data") - else: - tar_ref.extractall() - - os.remove(platform + ext) + download_inih() -if not NO_OCEAN: - download_box2d("box2d-web") - # Shared compile args for all platforms extra_compile_args = [ "-DNPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION", @@ -165,7 +128,7 @@ def download_box2d(platform): "-Bsymbolic-functions", ] if not NO_OCEAN: - download_raylib("raylib-5.5_linux_amd64", ".tar.gz") + download_raylib(RAYLIB_NAME, ".tar.gz") elif system == "Darwin": extra_compile_args += [ "-Wno-error=int-conversion", @@ -181,25 +144,11 @@ def download_box2d(platform): "IOKit", ] if not NO_OCEAN: - download_raylib("raylib-5.5_macos", ".tar.gz") + download_raylib(RAYLIB_NAME, ".tar.gz") else: raise ValueError(f"Unsupported system: {system}") -if not NO_OCEAN: - download_box2d(BOX2D_NAME) - -# Default Gym/Gymnasium/PettingZoo versions -# Gym: -# - 0.26 still has deprecation warnings and is the last version of the package -# - 0.25 adds a breaking API change to reset, step, and render_modes -# - 0.24 is broken -# - 0.22-0.23 triggers deprecation warnings by calling its own functions -# - 0.21 is the most stable version -# - <= 0.20 is missing dict methods for gym.spaces.Dict -# - 0.18-0.21 require setuptools<=65.5.0 - -# Extensions class BuildExt(build_ext): def run(self): # Propagate any build_ext options (e.g., --inplace, --force) to subcommands @@ -226,47 +175,29 @@ def run(self): super().run() -RAYLIB_A = f"{RAYLIB_NAME}/lib/libraylib.a" -INCLUDE = [numpy.get_include(), "raylib/include", f"{BOX2D_NAME}/include", f"{BOX2D_NAME}/src"] -extension_kwargs = dict( - include_dirs=INCLUDE, - extra_compile_args=extra_compile_args, - extra_link_args=extra_link_args, - extra_objects=[RAYLIB_A], -) +RAYLIB_DIR = os.path.join(EXTERNAL_LIB_DIR, RAYLIB_NAME) +RAYLIB_A = os.path.join(RAYLIB_DIR, "lib", "libraylib.a") +INIH_DIR = os.path.join(EXTERNAL_LIB_DIR, "inih-r62") -# Find C extensions c_extensions = [] +c_extension_paths = [] if not NO_OCEAN: - c_extension_paths = glob.glob("pufferlib/ocean/**/binding.c", recursive=True) + c_extension_paths = ["pufferlib/ocean/drive"] c_extensions = [ Extension( - path.rstrip(".c").replace("/", "."), - sources=[path], - **extension_kwargs, + "pufferlib.ocean.drive.binding", + sources=["pufferlib/ocean/drive/binding.c", os.path.join(INIH_DIR, "ini.c")], + include_dirs=[numpy.get_include(), os.path.join(RAYLIB_DIR, "include")], + extra_compile_args=extra_compile_args + + [ + '-DINI_START_COMMENT_PREFIXES="#"', + '-DINI_INLINE_COMMENT_PREFIXES="#"', + ], + extra_link_args=extra_link_args, + extra_objects=[RAYLIB_A], ) - for path in c_extension_paths - if "matsci" not in path ] - c_extension_paths = [os.path.join(*path.split("/")[:-1]) for path in c_extension_paths] - - for c_ext in c_extensions: - if "drive" in c_ext.name: - c_ext.sources.append("inih-r62/ini.c") - c_ext.extra_compile_args.extend( - [ - '-DINI_START_COMMENT_PREFIXES="#"', - '-DINI_INLINE_COMMENT_PREFIXES="#"', - ] - ) - - if "impulse_wars" in c_ext.name: - print(f"Adding {c_ext.name} to extra objects") - c_ext.extra_objects.append(f"{BOX2D_NAME}/libbox2d.a") - if "matsci" in c_ext.name: - c_ext.include_dirs.append("/usr/local/include") - c_ext.extra_link_args.extend(["-L/usr/local/lib", "-llammps"]) # Check if CUDA compiler is available. You need cuda dev, not just runtime. torch_extensions = [] @@ -274,7 +205,7 @@ def run(self): torch_sources = [ "pufferlib/extensions/pufferlib.cpp", ] - if shutil.which("nvcc"): + if BUILD_CUDA_EXT: extension = CUDAExtension torch_sources.append("pufferlib/extensions/cuda/pufferlib.cu") else: @@ -292,7 +223,7 @@ def run(self): ] # Prevent Conda from injecting garbage compile flags -from distutils.sysconfig import get_config_vars +from distutils.sysconfig import get_config_vars # noqa: E402 cfg_vars = get_config_vars() for key in ("CC", "CXX", "LDSHARED"): @@ -306,34 +237,34 @@ def run(self): cfg_vars[key] = value.replace("-fno-strict-overflow", "") install_requires = [ - "setuptools", - "numpy<2.0", - "shimmy[gym-v21]", - "gym==0.23", + "setuptools<81", + "numpy", "gymnasium==0.29.1", - "pettingzoo==1.24.1", + "pyyaml", ] if not NO_TRAIN: install_requires += [ "torch", "psutil", - "nvidia-ml-py", "rich", "rich_argparse", + "pandas", + "tqdm", + "matplotlib==3.8.4", "imageio", "pyro-ppl", - "heavyball<2.0.0", + "mediapy", + "heavyball", "neptune", "wandb", - "matplotlib", - "tqdm", + "tensorboard", + "google-cloud-aiplatform", ] setup( version="3.0.0", packages=find_namespace_packages() + find_packages() + c_extension_paths + ["pufferlib/extensions"], - package_data={"pufferlib": [RAYLIB_NAME + "/lib/libraylib.a"]}, include_package_data=True, install_requires=install_requires, ext_modules=c_extensions + torch_extensions, @@ -342,5 +273,5 @@ def run(self): "build_torch": TorchBuildExt, "build_c": CBuildExt, }, - include_dirs=[numpy.get_include(), RAYLIB_NAME + "/include"], + include_dirs=[numpy.get_include(), os.path.join(RAYLIB_DIR, "include")], ) diff --git a/tests/test_drive_config.py b/tests/test_drive_config.py index dfb9a8e3df..43b30916f8 100644 --- a/tests/test_drive_config.py +++ b/tests/test_drive_config.py @@ -77,21 +77,22 @@ def test_drive_ini_config(self): # --- Tunable hyperparameters (tested at high strictness) --- if ASSERTION_LEVEL >= 3: - self.assertEqual(args["train"]["total_timesteps"], 2_000_000_000) - self.assertEqual(args["train"]["batch_size"], 524288) - self.assertEqual(args["train"]["rollout_horizon"], 32) - self.assertEqual(args["train"]["minibatch_size"], 32768) - self.assertEqual(args["train"]["learning_rate"], 0.003) + self.assertEqual(args["train"]["total_timesteps"], 3_000_000_000) + self.assertEqual(args["train"]["batch_size"], "auto") + self.assertEqual(args["train"]["bptt_horizon"], 91) + self.assertEqual(args["train"]["minibatch_size"], 11648) + self.assertEqual(args["train"]["learning_rate"], 0.001) self.assertEqual(args["train"]["gamma"], 0.98) self.assertEqual(args["train"]["gae_lambda"], 0.95) - self.assertEqual(args["train"]["ent_coef"], 0.005) + self.assertEqual(args["train"]["ent_coef"], 0.001) self.assertEqual(args["env"]["reward_vehicle_collision"], -0.5) - self.assertEqual(args["env"]["reward_offroad_collision"], -0.5) - self.assertEqual(args["env"]["num_maps"], 10000) + self.assertEqual(args["env"]["reward_offroad_collision"], -0.2) + self.assertEqual(args["env"]["num_maps"], 1) @patch("sys.argv", ["pufferl.py", "--train.learning-rate=0.5"]) def test_cli_override(self): """Test that command-line arguments override INI file values.""" + # learning_rate is 0.001 in drive.ini, but we override it to 0.5 here args = load_config("puffer_drive") self.assertEqual(args["train"]["learning_rate"], 0.5) diff --git a/tests/test_drive_render.py b/tests/test_drive_render.py new file mode 100644 index 0000000000..75ea9c3746 --- /dev/null +++ b/tests/test_drive_render.py @@ -0,0 +1,116 @@ +#!/usr/bin/env python3 +""" +Test script for PufferDrive raylib rendering functionality. +""" + +import os +import subprocess +import sys +import numpy as np + + +def test_drive_render(): + """Test that PufferDrive renderer runs successfully (exit code 0).""" + print("Testing PufferDrive rendering...") + + # Check if drive binary exists + if not os.path.exists("./visualize"): + print("Drive binary not found, attempting to build...") + try: + result = subprocess.run( + ["bash", "scripts/build_ocean.sh", "visualize", "local"], capture_output=True, text=True, timeout=600 + ) + if result.returncode != 0 or not os.path.exists("./visualize"): + print(f"Build failed: {result.stderr}") + return False + except Exception as e: + print(f"Build error: {e}") + return False + + # Backup existing weights file if it exists + weights_path = "resources/drive/puffer_drive_weights.bin" + backup_path = "resources/drive/puffer_drive_weights.bin.backup" + weights_existed = False + + if os.path.exists(weights_path): + weights_existed = True + os.rename(weights_path, backup_path) + + # Create dummy weights file + os.makedirs("resources/drive", exist_ok=True) + dummy_weights = np.random.randn(700000).astype(np.float32) + dummy_weights.tofile(weights_path) + + try: + # Set up environment to suppress AddressSanitizer exit code (needed due to current memory leaks) + env = os.environ.copy() + env["ASAN_OPTIONS"] = "exitcode=0" + + # Run the renderer with xvfb and frame skip for faster testing + print("Running renderer.") + result = subprocess.run( + [ + "xvfb-run", + "-a", + "-s", + "-screen 0 1280x720x24", + "./visualize", + "--frame-skip", + "10", + "--map-name", + "resources/drive/binaries/map_000.bin", + "--output-topdown", + "resources/drive/output_topdown.mp4", + "--output-agent", + "resources/drive/output_agent.mp4", + "--view", + "topdown", + ], + capture_output=True, + text=True, + timeout=600, + env=env, + ) + + print(f"Renderer exit code: {result.returncode}") + + # Show output for debugging if needed + if result.stdout: + print(f"stdout: {result.stdout}") + if result.stderr: + print(f"stderr: {result.stderr}") + + if result.returncode == 0: + print("Renderer completed successfully!") + return True + else: + print(f"Renderer failed with exit code {result.returncode}") + return False + + except subprocess.TimeoutExpired: + print("Renderer timed out") + return False + except Exception as e: + print(f"Render test failed: {e}") + return False + finally: + # Cleanup: remove test outputs and restore original weights if they existed + if os.path.exists(weights_path): + os.remove(weights_path) + + if weights_existed and os.path.exists(backup_path): + os.rename(backup_path, weights_path) + + # Clean up generated outputs + for output_file in ["resources/drive/output_topdown.mp4", "resources/drive/output_agent.mp4"]: + if os.path.exists(output_file): + os.remove(output_file) + + +if __name__ == "__main__": + if test_drive_render(): + print("Render test passed!") + sys.exit(0) + else: + print("Render test failed") + sys.exit(1) diff --git a/tests/test_drive_scenario_length.py b/tests/test_drive_scenario_length.py index 3e2949fdc2..e6ff3ff39b 100644 --- a/tests/test_drive_scenario_length.py +++ b/tests/test_drive_scenario_length.py @@ -4,14 +4,14 @@ from pufferlib.ocean.drive.drive import Drive -def test_drive_emits_log_after_custom_episode_length(): +def test_drive_emits_log_after_custom_scenario_length(): """Ensure the engine produces an episode log once the custom horizon elapses.""" try: env = Drive( num_agents=32, num_maps=1, - episode_length=5, + scenario_length=5, resample_frequency=0, report_interval=1, ) @@ -28,5 +28,5 @@ def test_drive_emits_log_after_custom_episode_length(): env.close() - assert logs, "Drive never flushed its log; episode_length may be ignored" + assert logs, "Drive never flushed its log; scenario_length may be ignored" assert pytest.approx(logs[-1]["episode_length"], rel=0.0, abs=1e-6) == 5 diff --git a/tests/test_drive_train.py b/tests/test_drive_train.py index 8271fb8be8..7027299674 100644 --- a/tests/test_drive_train.py +++ b/tests/test_drive_train.py @@ -27,7 +27,7 @@ def test_drive_training(): "compile": False, "total_timesteps": 100000, "batch_size": 128, - "rollout_horizon": 8, + "bptt_horizon": 8, "minibatch_size": 128, "max_minibatch_size": 128, "update_epochs": 1, @@ -51,8 +51,6 @@ def test_drive_training(): "action_type": "discrete", "num_maps": 1, "map_dir": "resources/drive/binaries", - "init_mode": "create_all_valid", - "control_mode": "control_agents", } ) @@ -73,14 +71,10 @@ def test_drive_training(): args["neptune"] = False args["eval"] = { "eval_interval": 10000, - "self_play_eval": False, + "num_episodes": 4, "wosac_realism_eval": False, - "human_replay_eval": False, - "render_self_play_eval": False, - "render_human_replay_eval": False, - "num_eval_agents": 8, - "map_dir": "resources/drive/binaries", - "render_select_mode": "fixed", + "human_replay_eval": True, + "human_replay_num_agents": 8, } # Load components @@ -90,7 +84,7 @@ def test_drive_training(): # Initialize training train_config = dict(**args["train"], env=env_name, eval=args.get("eval", {})) - pufferl = PuffeRL(train_config, vecenv, policy, logger=None, full_args=args) + pufferl = PuffeRL(train_config, vecenv, policy, logger=None) # Train until reaching 50K steps target_steps = 50000 diff --git a/tests/test_simulator_perf.py b/tests/test_simulator_perf.py index 36fe5ab2f7..18d761014d 100644 --- a/tests/test_simulator_perf.py +++ b/tests/test_simulator_perf.py @@ -14,7 +14,7 @@ def test_simulator_raw(): num_agents = 32 # ---- Run simulation ---- - env = Drive(num_agents=num_agents, num_maps=1, episode_length=91, map_dir="resources/drive/binaries") + env = Drive(num_agents=num_agents, num_maps=1, scenario_length=91, map_dir="resources/drive/binaries") obs, _ = env.reset() tick = 0 From 99d6d47a4344c0df0b0c2c625b9e6bfb543681b0 Mon Sep 17 00:00:00 2001 From: Eugene Vinitsky Date: Mon, 30 Mar 2026 21:07:35 -0400 Subject: [PATCH 02/24] Add cluster configs from 3.0, update map_dir for turbostream Co-Authored-By: Claude Opus 4.6 (1M context) --- scripts/cluster_configs/nyu_greene.yaml | 10 ++++++++++ scripts/cluster_configs/train_base.yaml | 23 +++++++++++++++++++++++ 2 files changed, 33 insertions(+) create mode 100644 scripts/cluster_configs/nyu_greene.yaml create mode 100644 scripts/cluster_configs/train_base.yaml diff --git a/scripts/cluster_configs/nyu_greene.yaml b/scripts/cluster_configs/nyu_greene.yaml new file mode 100644 index 0000000000..fc537c5c3d --- /dev/null +++ b/scripts/cluster_configs/nyu_greene.yaml @@ -0,0 +1,10 @@ +# NYU Greene cluster compute configuration +account: torch_pr_355_general # Set your account/allocation +nodes: 1 +gpus: 1 +cpus: 16 +mem: 32gb +time: 360 # minutes +gpu_type: null # rtx8000, a100, v100 (optional, uses partition default) +exclude: null +nodelist: null diff --git a/scripts/cluster_configs/train_base.yaml b/scripts/cluster_configs/train_base.yaml new file mode 100644 index 0000000000..921beeb681 --- /dev/null +++ b/scripts/cluster_configs/train_base.yaml @@ -0,0 +1,23 @@ +# Base training configuration for PufferDrive cluster jobs +# These args are passed to `puffer train puffer_drive` + +# Vectorization +vec.num_workers: 16 +vec.num_envs: 16 + +# Environment +env.num_agents: 1024 +env.map_dir: "pufferlib/resources/drive/binaries/carla" +env.num_maps: 8 + +# Training +train.total_timesteps: 2_000_000_000 +train.checkpoint_interval: 1000 + +# Rendering +train.render: True + +# W&B logging +wandb: True +wandb_project: pufferdrive +wandb_group: cluster From e03c8483887efb6aae12abadb592a0f44fba492b Mon Sep 17 00:00:00 2001 From: Eugene Vinitsky Date: Mon, 30 Mar 2026 21:17:32 -0400 Subject: [PATCH 03/24] Add MPLCONFIGDIR to scratch cache exports in submit_cluster.py Co-Authored-By: Claude Opus 4.6 (1M context) --- scripts/submit_cluster.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/scripts/submit_cluster.py b/scripts/submit_cluster.py index 7dc1977d24..377f0b7b2d 100644 --- a/scripts/submit_cluster.py +++ b/scripts/submit_cluster.py @@ -362,11 +362,12 @@ def launch_training(args, from_config, cmd, save_dir, project_root, container_co scratch_dir = os.environ.get("SCRATCH_DIR", "/scratch/" + os.environ.get("USER", "")) cache_exports = ( f"export XDG_CACHE_HOME={scratch_dir}/cache && " + f"export MPLCONFIGDIR={scratch_dir}/cache/matplotlib && " f"export WANDB_CACHE_DIR={scratch_dir}/wandb_cache && " f"export WANDB_CONFIG_DIR={scratch_dir}/wandb_config && " f"export WANDB_DATA_DIR={scratch_dir}/wandb_data && " f"export WANDB_DIR={scratch_dir}/wandb_data && " - f"mkdir -p {scratch_dir}/cache" + f"mkdir -p {scratch_dir}/cache/matplotlib" ) inner_cmd = f"{env_setup} && {cache_exports} && cd {project_root} && " + " ".join(full_cmd) full_cmd = [ From b0d62943c2d1a9948d4da9256ff50654a66e6d09 Mon Sep 17 00:00:00 2001 From: Eugene Vinitsky Date: Mon, 30 Mar 2026 21:20:21 -0400 Subject: [PATCH 04/24] Add --wandb-name argument support to pufferl.py Co-Authored-By: Claude Opus 4.6 (1M context) --- pufferlib/pufferl.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/pufferlib/pufferl.py b/pufferlib/pufferl.py index 2f78a42d79..fb70aee88b 100644 --- a/pufferlib/pufferl.py +++ b/pufferlib/pufferl.py @@ -1230,6 +1230,7 @@ def __init__(self, args, load_id=None, resume="allow"): id=load_id or wandb.util.generate_id(), project=args["wandb_project"], group=args["wandb_group"], + name=args.get("wandb_name"), allow_val_change=True, save_code=False, resume=resume, @@ -2471,6 +2472,7 @@ def load_config(env_name, config_dir=None): parser.add_argument("--wandb", action="store_true", help="Use wandb for logging") parser.add_argument("--wandb-project", type=str, default="pufferlib") parser.add_argument("--wandb-group", type=str, default="debug") + parser.add_argument("--wandb-name", type=str, default=None, help="Wandb run name") parser.add_argument("--neptune", action="store_true", help="Use neptune for logging") parser.add_argument("--neptune-name", type=str, default="pufferai") parser.add_argument("--neptune-project", type=str, default="ablations") From db849396e97af241e2cf61dcd4120d83fce840ba Mon Sep 17 00:00:00 2001 From: Eugene Vinitsky Date: Mon, 30 Mar 2026 21:27:44 -0400 Subject: [PATCH 05/24] Add visualize.c from turbostream (was missed in initial merge) Co-Authored-By: Claude Opus 4.6 (1M context) --- pufferlib/ocean/drive/visualize.c | 532 ++++++++++++++++++++++++++++++ 1 file changed, 532 insertions(+) create mode 100644 pufferlib/ocean/drive/visualize.c diff --git a/pufferlib/ocean/drive/visualize.c b/pufferlib/ocean/drive/visualize.c new file mode 100644 index 0000000000..53adddced3 --- /dev/null +++ b/pufferlib/ocean/drive/visualize.c @@ -0,0 +1,532 @@ +#include +#include +#include +#include +#include +#include +#include "rlgl.h" +#include +#include +#include +#include +#include "error.h" +#include "drive.h" +#include "drivenet.h" +#include "libgen.h" +#include "../env_config.h" +#define TRAJECTORY_LENGTH_DEFAULT 91 + +typedef struct { + int pipefd[2]; + pid_t pid; +} VideoRecorder; + +bool OpenVideo(VideoRecorder *recorder, const char *output_filename, int width, int height) { + if (pipe(recorder->pipefd) == -1) { + fprintf(stderr, "Failed to create pipe\n"); + return false; + } + + recorder->pid = fork(); + if (recorder->pid == -1) { + fprintf(stderr, "Failed to fork\n"); + return false; + } + + char size_str[64]; + snprintf(size_str, sizeof(size_str), "%dx%d", width, height); + + if (recorder->pid == 0) { // Child process: run ffmpeg + close(recorder->pipefd[1]); + dup2(recorder->pipefd[0], STDIN_FILENO); + close(recorder->pipefd[0]); + // Close all other file descriptors to prevent leaks + for (int fd = 3; fd < 256; fd++) { + close(fd); + } + execlp("ffmpeg", "ffmpeg", "-y", "-f", "rawvideo", "-pix_fmt", "rgba", "-s", size_str, "-r", "30", "-i", "-", + "-c:v", "libx264", "-pix_fmt", "yuv420p", "-preset", "ultrafast", "-crf", "23", "-loglevel", "error", + output_filename, NULL); + TraceLog(LOG_ERROR, "Failed to launch ffmpeg"); + return false; + } + + close(recorder->pipefd[0]); // Close read end in parent + return true; +} + +void WriteFrame(VideoRecorder *recorder, int width, int height) { + unsigned char *screen_data = rlReadScreenPixels(width, height); + write(recorder->pipefd[1], screen_data, width * height * 4 * sizeof(*screen_data)); + RL_FREE(screen_data); +} + +void CloseVideo(VideoRecorder *recorder) { + close(recorder->pipefd[1]); + waitpid(recorder->pid, NULL, 0); +} + +void renderTopDownView(Drive *env, Client *client, int map_height, int obs, int lasers, int trajectories, + int frame_count, float *path, int log_trajectories, int show_grid, int img_width, + int img_height) { + + BeginDrawing(); + + // Top-down orthographic camera + Camera3D camera = {0}; + camera.position = (Vector3){0.0f, 0.0f, 500.0f}; // above the scene + camera.target = (Vector3){0.0f, 0.0f, 0.0f}; // look at origin + camera.up = (Vector3){0.0f, -1.0f, 0.0f}; + camera.fovy = map_height; + camera.projection = CAMERA_ORTHOGRAPHIC; + + client->width = img_width; + client->height = img_height; + + Color road = (Color){35, 35, 37, 255}; + ClearBackground(road); + BeginMode3D(camera); + rlEnableDepthTest(); + + // Draw human replay trajectories if enabled + if (log_trajectories) { + for (int i = 0; i < env->active_agent_count; i++) { + int idx = env->active_agent_indices[i]; + Vector3 prev_point = {0}; + bool has_prev = false; + + for (int j = 0; j < env->agents[idx].trajectory_length; j++) { + float x = env->agents[idx].log_trajectory_x[j]; + float y = env->agents[idx].log_trajectory_y[j]; + float valid = env->agents[idx].log_valid[j]; + + if (!valid) { + has_prev = false; + continue; + } + + Vector3 curr_point = {x, y, 0.5f}; + + if (has_prev) { + DrawLine3D(prev_point, curr_point, Fade(LIGHTGREEN, 0.6f)); + } + + prev_point = curr_point; + has_prev = true; + } + } + } + + // Draw agent trajs + if (trajectories) { + for (int i = 0; i < frame_count; i++) { + DrawSphere((Vector3){path[i * 2], path[i * 2 + 1], 0.8f}, 0.5f, YELLOW); + } + } + + // Draw scene + draw_scene(env, client, 1, obs, lasers, show_grid); + EndMode3D(); + EndDrawing(); +} + +void renderAgentView(Drive *env, Client *client, int map_height, int obs_only, int lasers, int show_grid) { + // Agent perspective camera following the selected agent + int agent_idx = env->active_agent_indices[env->human_agent_idx]; + Agent *agent = &env->agents[agent_idx]; + + BeginDrawing(); + + Camera3D camera = {0}; + // Position camera behind and above the agent + camera.position = (Vector3){agent->sim_x - (25.0f * cosf(agent->sim_heading)), + agent->sim_y - (25.0f * sinf(agent->sim_heading)), 15.0f}; + camera.target = (Vector3){agent->sim_x + 40.0f * cosf(agent->sim_heading), + agent->sim_y + 40.0f * sinf(agent->sim_heading), 1.0f}; + camera.up = (Vector3){0.0f, 0.0f, 1.0f}; + camera.fovy = 45.0f; + camera.projection = CAMERA_PERSPECTIVE; + + Color road = (Color){35, 35, 37, 255}; + + ClearBackground(road); + BeginMode3D(camera); + rlEnableDepthTest(); + draw_scene(env, client, 0, obs_only, lasers, show_grid); // mode=0 for agent view + EndMode3D(); + EndDrawing(); +} + +static int run_cmd(const char *cmd) { + int rc = system(cmd); + if (rc != 0) { + fprintf(stderr, "[ffmpeg] command failed (%d): %s\n", rc, cmd); + } + return rc; +} + +// Make a high-quality GIF from numbered PNG frames like frame_000.png +static int make_gif_from_frames(const char *pattern, int fps, const char *palette_path, const char *out_gif) { + char cmd[1024]; + + // 1) Generate palette (no quotes needed for simple filter) + // NOTE: if your frames start at 000, you don't need -start_number. + snprintf(cmd, sizeof(cmd), "ffmpeg -y -framerate %d -i %s -vf palettegen %s", fps, pattern, palette_path); + if (run_cmd(cmd) != 0) + return -1; + + // 2) Use palette to encode the GIF + snprintf(cmd, sizeof(cmd), "ffmpeg -y -framerate %d -i %s -i %s -lavfi paletteuse -loop 0 %s", fps, pattern, + palette_path, out_gif); + if (run_cmd(cmd) != 0) + return -1; + + return 0; +} + +int eval_gif(const char *map_name, const char *policy_name, int show_grid, int obs_only, int lasers, + int log_trajectories, int frame_skip, float goal_radius, int init_steps, int max_controlled_agents, + const char *view_mode, const char *output_topdown, const char *output_agent, int num_maps, + int scenario_length_override, int init_mode, int control_mode, int goal_behavior) { + + // Parse configuration from INI file + env_init_config conf = {0}; // Initialize to zero + const char *ini_file = "pufferlib/config/ocean/drive.ini"; + if (load_env_config(ini_file, &conf) < 0) { + fprintf(stderr, "Error: Could not load %s. Cannot determine environment configuration.\n", ini_file); + return -1; + } + + char map_buffer[100]; + if (map_name == NULL) { + srand(time(NULL)); + int random_map = rand() % num_maps; + sprintf(map_buffer, "%s/map_%03d.bin", conf.map_dir, random_map); // random map file + map_name = map_buffer; + } + + if (frame_skip <= 0) { + frame_skip = 1; // Default: render every frame + } + + // Check if map file exists + FILE *map_file = fopen(map_name, "rb"); + if (map_file == NULL) { + RAISE_FILE_ERROR(map_name); + } + fclose(map_file); + + FILE *policy_file = fopen(policy_name, "rb"); + if (policy_file == NULL) { + RAISE_FILE_ERROR(policy_name); + } + fclose(policy_file); + + Drive env = { + .dynamics_model = conf.dynamics_model, + .reward_vehicle_collision = conf.reward_vehicle_collision, + .reward_offroad_collision = conf.reward_offroad_collision, + .reward_ade = conf.reward_ade, + .goal_radius = conf.goal_radius, + .dt = conf.dt, + .map_name = (char *)map_name, + .init_steps = init_steps, + .max_controlled_agents = max_controlled_agents, + .collision_behavior = conf.collision_behavior, + .offroad_behavior = conf.offroad_behavior, + .compute_eval_metrics = conf.compute_eval_metrics, + .goal_behavior = goal_behavior, + .init_mode = init_mode, + .control_mode = control_mode, + }; + + env.scenario_length = (scenario_length_override > 0) ? scenario_length_override + : (conf.scenario_length > 0) ? conf.scenario_length + : TRAJECTORY_LENGTH_DEFAULT; + allocate(&env); + + // Set which vehicle to focus on for obs mode + env.human_agent_idx = 0; + + c_reset(&env); + + // Make client for rendering + Client *client = (Client *)calloc(1, sizeof(Client)); + env.client = client; + + SetConfigFlags(FLAG_WINDOW_HIDDEN); + SetTargetFPS(6000); + + float map_width = env.grid_map->bottom_right_x - env.grid_map->top_left_x; + float map_height = env.grid_map->top_left_y - env.grid_map->bottom_right_y; + + printf("Map size: %.1fx%.1f\n", map_width, map_height); + float scale = 6.0f; // Can be used to increase the video quality + + // Calculate video width and height; round to nearest even number + int img_width = (int)roundf(map_width * scale / 2.0f) * 2; + int img_height = (int)roundf(map_height * scale / 2.0f) * 2; + + InitWindow(img_width, img_height, "Puffer Drive"); + SetConfigFlags(FLAG_MSAA_4X_HINT); + + // Load the textures and models + client->puffers = LoadTexture("resources/puffers_128.png"); + client->cars[0] = LoadModel("resources/drive/RedCar.glb"); + client->cars[1] = LoadModel("resources/drive/WhiteCar.glb"); + client->cars[2] = LoadModel("resources/drive/BlueCar.glb"); + client->cars[3] = LoadModel("resources/drive/YellowCar.glb"); + client->cars[4] = LoadModel("resources/drive/GreenCar.glb"); + client->cars[5] = LoadModel("resources/drive/GreyCar.glb"); + client->cyclist = LoadModel("resources/drive/cyclist.glb"); + client->pedestrian = LoadModel("resources/drive/pedestrian.glb"); + + Weights *weights = load_weights(policy_name); + printf("Active agents in map: %d\n", env.active_agent_count); + DriveNet *net = init_drivenet(weights, env.active_agent_count, env.dynamics_model); + + int frame_count = env.scenario_length > 0 ? env.scenario_length : TRAJECTORY_LENGTH_DEFAULT; + int log_trajectory = log_trajectories; + char filename_topdown[256]; + char filename_agent[256]; + + if (output_topdown != NULL && output_agent != NULL) { + strcpy(filename_topdown, output_topdown); + strcpy(filename_agent, output_agent); + } else { + char policy_base[256]; + strcpy(policy_base, policy_name); + *strrchr(policy_base, '.') = '\0'; + + char map[256]; + strcpy(map, basename((char *)map_name)); + *strrchr(map, '.') = '\0'; + + // Create video directory if it doesn't exist + char video_dir[256]; + sprintf(video_dir, "%s/video", policy_base); + char mkdir_cmd[512]; + snprintf(mkdir_cmd, sizeof(mkdir_cmd), "mkdir -p \"%s\"", video_dir); + system(mkdir_cmd); + + sprintf(filename_topdown, "%s/video/%s_topdown.mp4", policy_base, map); + sprintf(filename_agent, "%s/video/%s_agent.mp4", policy_base, map); + } + + bool render_topdown = (strcmp(view_mode, "both") == 0 || strcmp(view_mode, "topdown") == 0); + bool render_agent = (strcmp(view_mode, "both") == 0 || strcmp(view_mode, "agent") == 0); + + printf("Rendering: %s\n", view_mode); + + int rendered_frames = 0; + double startTime = GetTime(); + + VideoRecorder topdown_recorder, agent_recorder; + + if (render_topdown) { + if (!OpenVideo(&topdown_recorder, filename_topdown, img_width, img_height)) { + CloseWindow(); + return -1; + } + } + + if (render_agent) { + if (!OpenVideo(&agent_recorder, filename_agent, img_width, img_height)) { + if (render_topdown) + CloseVideo(&topdown_recorder); + CloseWindow(); + return -1; + } + } + + if (render_topdown) { + printf("Recording topdown view...\n"); + for (int i = 0; i < frame_count; i++) { + if (i % frame_skip == 0) { + renderTopDownView(&env, client, map_height, 0, 0, 0, frame_count, NULL, log_trajectories, show_grid, + img_width, img_height); + WriteFrame(&topdown_recorder, img_width, img_height); + rendered_frames++; + } + int (*actions)[2] = (int (*)[2])env.actions; + forward(net, env.observations, (int *)env.actions); + c_step(&env); + } + } + + if (render_agent) { + c_reset(&env); + printf("Recording agent view...\n"); + for (int i = 0; i < frame_count; i++) { + // Check if selected agent has reached the first goal and stop recording + int human_idx = env.active_agent_indices[env.human_agent_idx]; + if (env.agents[human_idx].reached_goal_this_episode) { + break; + } + if (i % frame_skip == 0) { + renderAgentView(&env, client, map_height, obs_only, lasers, show_grid); + WriteFrame(&agent_recorder, img_width, img_height); + rendered_frames++; + } + int (*actions)[2] = (int (*)[2])env.actions; + forward(net, env.observations, (int *)env.actions); + c_step(&env); + } + } + + double endTime = GetTime(); + double elapsedTime = endTime - startTime; + double writeFPS = (elapsedTime > 0) ? rendered_frames / elapsedTime : 0; + + printf("Wrote %d frames in %.2f seconds (%.2f FPS) to %s \n", rendered_frames, elapsedTime, writeFPS, + filename_topdown); + + if (render_topdown) { + CloseVideo(&topdown_recorder); + } + if (render_agent) { + CloseVideo(&agent_recorder); + } + CloseWindow(); + + // Clean up resources + free(client); + free_allocated(&env); + free_drivenet(net); + free(weights); + return 0; +} + +int main(int argc, char *argv[]) { + int show_grid = 0; + int obs_only = 0; + int lasers = 0; + int log_trajectories = 1; + int frame_skip = 1; + float goal_radius = 2.0f; + int init_steps = 0; + const char *map_name = NULL; + const char *policy_name = "resources/drive/puffer_drive_weights.bin"; + int max_controlled_agents = -1; + int num_maps = 1; + int scenario_length_cli = -1; + int init_mode = 0; + int control_mode = 0; + int goal_behavior = 0; + + const char *view_mode = "both"; // "both", "topdown", "agent" + const char *output_topdown = NULL; + const char *output_agent = NULL; + + // Parse command line arguments + for (int i = 1; i < argc; i++) { + if (strcmp(argv[i], "--show-grid") == 0) { + show_grid = 1; + } else if (strcmp(argv[i], "--obs-only") == 0) { + obs_only = 1; + } else if (strcmp(argv[i], "--lasers") == 0) { + lasers = 1; + } else if (strcmp(argv[i], "--log-trajectories") == 0) { + log_trajectories = 1; + } else if (strcmp(argv[i], "--frame-skip") == 0) { + if (i + 1 < argc) { + frame_skip = atoi(argv[i + 1]); + i++; // Skip the next argument since we consumed it + if (frame_skip <= 0) { + frame_skip = 1; // Ensure valid value + } + } + } else if (strcmp(argv[i], "--goal-radius") == 0) { + if (i + 1 < argc) { + goal_radius = atof(argv[i + 1]); + i++; + if (goal_radius <= 0) { + goal_radius = 2.0f; // Ensure valid value + } + } + } else if (strcmp(argv[i], "--map-name") == 0) { + // Check if there's a next argument for the map path + if (i + 1 < argc) { + map_name = argv[i + 1]; + i++; // Skip the next argument since we used it as map path + } else { + fprintf(stderr, "Error: --map-name option requires a map file path\n"); + return 1; + } + } else if (strcmp(argv[i], "--policy-name") == 0) { + if (i + 1 < argc) { + policy_name = argv[i + 1]; + i++; + } else { + fprintf(stderr, "Error: --policy-name option requires a policy file path\n"); + return 1; + } + } else if (strcmp(argv[i], "--view") == 0) { + if (i + 1 < argc) { + view_mode = argv[i + 1]; + i++; + if (strcmp(view_mode, "both") != 0 && strcmp(view_mode, "topdown") != 0 && + strcmp(view_mode, "agent") != 0) { + fprintf(stderr, "Error: --view must be 'both', 'topdown', or 'agent'\n"); + return 1; + } + } else { + fprintf(stderr, "Error: --view option requires a value (both/topdown/agent)\n"); + return 1; + } + } else if (strcmp(argv[i], "--output-topdown") == 0) { + if (i + 1 < argc) { + output_topdown = argv[i + 1]; + i++; + } + } else if (strcmp(argv[i], "--output-agent") == 0) { + if (i + 1 < argc) { + output_agent = argv[i + 1]; + i++; + } + } else if (strcmp(argv[i], "--init-steps") == 0) { + if (i + 1 < argc) { + init_steps = atoi(argv[i + 1]); + i++; + if (init_steps < 0) { + init_steps = 0; + } + } + } else if (strcmp(argv[i], "--init-mode") == 0) { + if (i + 1 < argc) { + init_mode = atoi(argv[i + 1]); + i++; + } + } else if (strcmp(argv[i], "--control-mode") == 0) { + if (i + 1 < argc) { + control_mode = atoi(argv[i + 1]); + i++; + } + } else if (strcmp(argv[i], "--max-controlled-agents") == 0) { + if (i + 1 < argc) { + max_controlled_agents = atoi(argv[i + 1]); + i++; + } + } else if (strcmp(argv[i], "--num-maps") == 0) { + if (i + 1 < argc) { + num_maps = atoi(argv[i + 1]); + i++; + } + } else if (strcmp(argv[i], "--scenario-length") == 0) { + if (i + 1 < argc) { + scenario_length_cli = atoi(argv[i + 1]); + i++; + } + } else if (strcmp(argv[i], "--goal-behavior") == 0) { + if (i + 1 < argc) { + goal_behavior = atoi(argv[i + 1]); + i++; + } + } + } + + eval_gif(map_name, policy_name, show_grid, obs_only, lasers, log_trajectories, frame_skip, goal_radius, init_steps, + max_controlled_agents, view_mode, output_topdown, output_agent, num_maps, scenario_length_cli, init_mode, + control_mode, goal_behavior); + return 0; +} From bbd8d77206863d4d4764298134af329375f72fad Mon Sep 17 00:00:00 2001 From: Eugene Vinitsky Date: Mon, 30 Mar 2026 21:28:41 -0400 Subject: [PATCH 06/24] Fix visualize.c struct fields to match turbostream drive.h Co-Authored-By: Claude Opus 4.6 (1M context) --- pufferlib/ocean/drive/visualize.c | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/pufferlib/ocean/drive/visualize.c b/pufferlib/ocean/drive/visualize.c index 53adddced3..d49924fc2c 100644 --- a/pufferlib/ocean/drive/visualize.c +++ b/pufferlib/ocean/drive/visualize.c @@ -231,11 +231,10 @@ int eval_gif(const char *map_name, const char *policy_name, int show_grid, int o .dt = conf.dt, .map_name = (char *)map_name, .init_steps = init_steps, - .max_controlled_agents = max_controlled_agents, + .num_controllable_agents = max_controlled_agents, .collision_behavior = conf.collision_behavior, .offroad_behavior = conf.offroad_behavior, .compute_eval_metrics = conf.compute_eval_metrics, - .goal_behavior = goal_behavior, .init_mode = init_mode, .control_mode = control_mode, }; From fc54f7c80dcc135f60e24a4ec3754d255f2c5444 Mon Sep 17 00:00:00 2001 From: Eugene Vinitsky Date: Mon, 30 Mar 2026 22:49:35 -0400 Subject: [PATCH 07/24] Fix distance_since_spawn: accumulate sim_speed * dt each step Was always 0, making avg_distance_per_infraction always 0. Co-Authored-By: Claude Opus 4.6 (1M context) --- pufferlib/ocean/drive/drive.h | 1 + 1 file changed, 1 insertion(+) diff --git a/pufferlib/ocean/drive/drive.h b/pufferlib/ocean/drive/drive.h index 3ac55a5905..567df62ecd 100644 --- a/pufferlib/ocean/drive/drive.h +++ b/pufferlib/ocean/drive/drive.h @@ -3801,6 +3801,7 @@ static void compute_rewards(Drive *env, int i) { env->rewards[i] += speed_reward; env->logs[i].avg_speed_per_agent += agent->sim_speed; + agent->distance_since_spawn += agent->sim_speed * env->dt; env->logs[i].episode_return += speed_reward; // ADE reward (CUSTOM) From 6d4cd710942767994c91855b26b072c0cfdb84b4 Mon Sep 17 00:00:00 2001 From: Eugene Vinitsky Date: Mon, 30 Mar 2026 23:11:55 -0400 Subject: [PATCH 08/24] Add missing import pufferlib.utils in pufferl.py Fixes render_videos call failing with 'module has no attribute utils'. Co-Authored-By: Claude Opus 4.6 (1M context) --- pufferlib/pufferl.py | 1 + 1 file changed, 1 insertion(+) diff --git a/pufferlib/pufferl.py b/pufferlib/pufferl.py index fb70aee88b..df9a352b2b 100644 --- a/pufferlib/pufferl.py +++ b/pufferlib/pufferl.py @@ -40,6 +40,7 @@ import pufferlib.sweep import pufferlib.vector import pufferlib.pytorch +import pufferlib.utils import pufferlib.viz import mediapy From 4806ad58832d4e6b69508554297258fedc004acd Mon Sep 17 00:00:00 2001 From: Eugene Vinitsky Date: Mon, 30 Mar 2026 23:48:19 -0400 Subject: [PATCH 09/24] Auto-discover map files for rendering instead of using map_%03d.bin format When render_map is not set, discovers .bin files from the env's map_dir. Co-Authored-By: Claude Opus 4.6 (1M context) --- pufferlib/utils.py | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/pufferlib/utils.py b/pufferlib/utils.py index ab00e502ac..d15c26da23 100644 --- a/pufferlib/utils.py +++ b/pufferlib/utils.py @@ -247,8 +247,13 @@ def render_videos(config, vecenv, logger, epoch, global_step, bin_path): # Handle single or multiple map rendering render_maps = config.get("render_map", None) - if render_maps is None: - render_maps = [None] + if render_maps is None or render_maps == "none": + # Auto-discover maps from the env's map_dir + map_dir = config.get("env", {}).get("map_dir", None) if isinstance(config.get("env"), dict) else getattr(env_cfg, "map_dir", None) + if map_dir and os.path.isdir(map_dir): + render_maps = sorted(os.path.join(map_dir, f) for f in os.listdir(map_dir) if f.endswith(".bin")) + if not render_maps: + render_maps = [None] elif isinstance(render_maps, (str, os.PathLike)): render_maps = [render_maps] else: From 91aa2c18c2ab1c0805241df2cf53ef093b81a6da Mon Sep 17 00:00:00 2001 From: Eugene Vinitsky Date: Mon, 30 Mar 2026 23:54:45 -0400 Subject: [PATCH 10/24] Point render_map at actual Carla Town01 binary Co-Authored-By: Claude Opus 4.6 (1M context) --- pufferlib/config/ocean/drive.ini | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pufferlib/config/ocean/drive.ini b/pufferlib/config/ocean/drive.ini index bf69f486b7..3b99087be4 100644 --- a/pufferlib/config/ocean/drive.ini +++ b/pufferlib/config/ocean/drive.ini @@ -149,7 +149,7 @@ show_lasers = False ; Display human xy logs in the background show_human_logs = True ; Options: List[str to path], str to path (e.g., "resources/drive/training/binaries/map_001.bin"), None -render_map = none +render_map = pufferlib/resources/drive/binaries/carla/opendrive__Town01.bin [eval] ; Set to True to enable periodic multi-scenario evaluation during training From 41cba0fdd18bdc490462230e66e7f4d36d2ae582 Mon Sep 17 00:00:00 2001 From: Eugene Vinitsky Date: Tue, 31 Mar 2026 00:19:00 -0400 Subject: [PATCH 11/24] Fix formatting (ruff) and relax matplotlib pin Co-Authored-By: Claude Opus 4.6 (1M context) --- pufferlib/utils.py | 6 +++++- setup.py | 2 +- 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/pufferlib/utils.py b/pufferlib/utils.py index d15c26da23..2fcdd2be7b 100644 --- a/pufferlib/utils.py +++ b/pufferlib/utils.py @@ -249,7 +249,11 @@ def render_videos(config, vecenv, logger, epoch, global_step, bin_path): render_maps = config.get("render_map", None) if render_maps is None or render_maps == "none": # Auto-discover maps from the env's map_dir - map_dir = config.get("env", {}).get("map_dir", None) if isinstance(config.get("env"), dict) else getattr(env_cfg, "map_dir", None) + map_dir = ( + config.get("env", {}).get("map_dir", None) + if isinstance(config.get("env"), dict) + else getattr(env_cfg, "map_dir", None) + ) if map_dir and os.path.isdir(map_dir): render_maps = sorted(os.path.join(map_dir, f) for f in os.listdir(map_dir) if f.endswith(".bin")) if not render_maps: diff --git a/setup.py b/setup.py index 6dc45cd240..c63db150cc 100644 --- a/setup.py +++ b/setup.py @@ -251,7 +251,7 @@ def run(self): "rich_argparse", "pandas", "tqdm", - "matplotlib==3.8.4", + "matplotlib>=3.8", "imageio", "pyro-ppl", "mediapy", From b1e529d835d3e59499765dd94c56da67bb5ac0e3 Mon Sep 17 00:00:00 2001 From: Eugene Vinitsky Date: Tue, 31 Mar 2026 00:20:22 -0400 Subject: [PATCH 12/24] Restrict clang-format hook to C/C++ files only Co-Authored-By: Claude Opus 4.6 (1M context) --- .pre-commit-config.yaml | 1 + 1 file changed, 1 insertion(+) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index d286db0404..dd233d3ae7 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -26,3 +26,4 @@ repos: rev: 'v21.1.5' hooks: - id: clang-format + types_or: [c, c++] From baa3fffd74731738fbc7c23cd2f7f7e8cdb56f32 Mon Sep 17 00:00:00 2001 From: Eugene Vinitsky Date: Tue, 31 Mar 2026 00:36:57 -0400 Subject: [PATCH 13/24] Fix render pipeline: pass weights via --policy-name, use absolute output paths - Pass bin_path directly to visualize via --policy-name instead of copying to a relative symlinked path that doesn't resolve in code isolation - Use absolute paths for output mp4s in the video output dir - Print stderr on render failure for debugging Co-Authored-By: Claude Opus 4.6 (1M context) --- pufferlib/utils.py | 28 ++++++++++++---------------- 1 file changed, 12 insertions(+), 16 deletions(-) diff --git a/pufferlib/utils.py b/pufferlib/utils.py index 2fcdd2be7b..aee6af818d 100644 --- a/pufferlib/utils.py +++ b/pufferlib/utils.py @@ -193,18 +193,14 @@ def render_videos(config, vecenv, logger, epoch, global_step, bin_path): video_output_dir = os.path.join(model_dir, "videos") os.makedirs(video_output_dir, exist_ok=True) - # Copy the binary weights to the expected location - expected_weights_path = "resources/drive/puffer_drive_weights.bin" - os.makedirs(os.path.dirname(expected_weights_path), exist_ok=True) - shutil.copy2(bin_path, expected_weights_path) - # TODO: Fix memory leaks so that this is not needed # Suppress AddressSanitizer exit code (temp) env_vars = os.environ.copy() env_vars["ASAN_OPTIONS"] = "exitcode=0" - # Base command (without map/output paths) - base_cmd = ["xvfb-run", "-a", "-s", "-screen 0 1280x720x24", "./visualize"] + # Base command — pass policy weights directly via --policy-name + base_cmd = ["xvfb-run", "-a", "-s", "-screen 0 1280x720x24", "./visualize", + "--policy-name", bin_path] # Render config flags if config.get("show_grid", False): @@ -273,24 +269,24 @@ def render_videos(config, vecenv, logger, epoch, global_step, bin_path): if map_path is not None and os.path.exists(map_path): cmd.extend(["--map-name", str(map_path)]) - # Output paths (overwrite each iteration; then moved/renamed) - cmd.extend(["--output-topdown", "resources/drive/output_topdown.mp4"]) - cmd.extend(["--output-agent", "resources/drive/output_agent.mp4"]) + # Output paths — use absolute paths in the video output dir + topdown_tmp = os.path.join(video_output_dir, "output_topdown.mp4") + agent_tmp = os.path.join(video_output_dir, "output_agent.mp4") + cmd.extend(["--output-topdown", topdown_tmp]) + cmd.extend(["--output-agent", agent_tmp]) result = subprocess.run(cmd, cwd=os.getcwd(), capture_output=True, text=True, timeout=120, env=env_vars) - vids_exist = os.path.exists("resources/drive/output_topdown.mp4") and os.path.exists( - "resources/drive/output_agent.mp4" - ) + vids_exist = os.path.exists(topdown_tmp) and os.path.exists(agent_tmp) if result.returncode == 0 or (result.returncode == 1 and vids_exist): videos = [ ( - "resources/drive/output_topdown.mp4", + topdown_tmp, f"epoch_{epoch:06d}_map{i:02d}_topdown.mp4" if map_path else f"epoch_{epoch:06d}_topdown.mp4", ), ( - "resources/drive/output_agent.mp4", + agent_tmp, f"epoch_{epoch:06d}_map{i:02d}_agent.mp4" if map_path else f"epoch_{epoch:06d}_agent.mp4", ), ] @@ -310,7 +306,7 @@ def render_videos(config, vecenv, logger, epoch, global_step, bin_path): else: print(f"Video generation completed but {source_vid} not found") else: - print(f"C rendering failed (map index {i}) with exit code {result.returncode}: {result.stdout}") + print(f"C rendering failed (map index {i}) with exit code {result.returncode}: {result.stdout}\nstderr: {result.stderr}") # Log all videos at once so W&B keeps all of them under the same step if hasattr(logger, "wandb") and logger.wandb and (videos_to_log_world or videos_to_log_agent): From dbb7c43fa5acd3a18b05d6933fa34fd43bff4e2d Mon Sep 17 00:00:00 2001 From: Eugene Vinitsky Date: Tue, 31 Mar 2026 01:16:06 -0400 Subject: [PATCH 14/24] Fix stale expected_weights_path ref, add visualize stderr logging Co-Authored-By: Claude Opus 4.6 (1M context) --- pufferlib/utils.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/pufferlib/utils.py b/pufferlib/utils.py index aee6af818d..fc09cf7703 100644 --- a/pufferlib/utils.py +++ b/pufferlib/utils.py @@ -279,6 +279,11 @@ def render_videos(config, vecenv, logger, epoch, global_step, bin_path): vids_exist = os.path.exists(topdown_tmp) and os.path.exists(agent_tmp) + if not vids_exist and result.stderr: + print(f"Visualize stderr: {result.stderr[:500]}") + if not vids_exist and result.stdout: + print(f"Visualize stdout: {result.stdout[:500]}") + if result.returncode == 0 or (result.returncode == 1 and vids_exist): videos = [ ( @@ -323,6 +328,4 @@ def render_videos(config, vecenv, logger, epoch, global_step, bin_path): print(f"Failed to generate GIF: {e}") finally: - # Clean up bin weights file - if os.path.exists(expected_weights_path): - os.remove(expected_weights_path) + pass From 51f9722d2404e978add9005875f4110eeb567e28 Mon Sep 17 00:00:00 2001 From: Eugene Vinitsky Date: Tue, 31 Mar 2026 01:32:57 -0400 Subject: [PATCH 15/24] Fix visualize build: add include guards to drive.h, add drive.h include to drivenet.h Co-Authored-By: Claude Opus 4.6 (1M context) --- pufferlib/ocean/drive/drive.h | 5 +++++ pufferlib/ocean/drive/drivenet.h | 1 + 2 files changed, 6 insertions(+) diff --git a/pufferlib/ocean/drive/drive.h b/pufferlib/ocean/drive/drive.h index 567df62ecd..d2aeb9c4b6 100644 --- a/pufferlib/ocean/drive/drive.h +++ b/pufferlib/ocean/drive/drive.h @@ -1,3 +1,6 @@ +#ifndef DRIVE_H +#define DRIVE_H + #include #include #include @@ -4671,3 +4674,5 @@ void c_step(Drive *env) { } #include "render.h" + +#endif // DRIVE_H diff --git a/pufferlib/ocean/drive/drivenet.h b/pufferlib/ocean/drive/drivenet.h index f364db25cb..e2ee50cc4b 100644 --- a/pufferlib/ocean/drive/drivenet.h +++ b/pufferlib/ocean/drive/drivenet.h @@ -1,4 +1,5 @@ #include +#include "drive.h" #include "puffernet.h" #include #include From 7fb53ad311bbb4bfce4d3c10bd4e0ceb06642f89 Mon Sep 17 00:00:00 2001 From: Eugene Vinitsky Date: Tue, 31 Mar 2026 01:40:07 -0400 Subject: [PATCH 16/24] Fix visualize: default to 32 agents, pass missing config fields - Default num_controllable_agents to 32 instead of -1 (caused calloc with huge size, producing 0 agents) - Pass simulation_mode, num_target_waypoints, min/max_waypoint_spacing, and traffic_light_behavior from config to Drive struct Co-Authored-By: Claude Opus 4.6 (1M context) --- pufferlib/ocean/drive/visualize.c | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/pufferlib/ocean/drive/visualize.c b/pufferlib/ocean/drive/visualize.c index d49924fc2c..739f1a6a88 100644 --- a/pufferlib/ocean/drive/visualize.c +++ b/pufferlib/ocean/drive/visualize.c @@ -234,9 +234,14 @@ int eval_gif(const char *map_name, const char *policy_name, int show_grid, int o .num_controllable_agents = max_controlled_agents, .collision_behavior = conf.collision_behavior, .offroad_behavior = conf.offroad_behavior, + .traffic_light_behavior = conf.traffic_light_behavior, .compute_eval_metrics = conf.compute_eval_metrics, .init_mode = init_mode, .control_mode = control_mode, + .simulation_mode = conf.simulation_mode, + .num_target_waypoints = conf.num_target_waypoints, + .min_waypoint_spacing = conf.min_waypoint_spacing, + .max_waypoint_spacing = conf.max_waypoint_spacing, }; env.scenario_length = (scenario_length_override > 0) ? scenario_length_override @@ -406,7 +411,7 @@ int main(int argc, char *argv[]) { int init_steps = 0; const char *map_name = NULL; const char *policy_name = "resources/drive/puffer_drive_weights.bin"; - int max_controlled_agents = -1; + int max_controlled_agents = 32; int num_maps = 1; int scenario_length_cli = -1; int init_mode = 0; From 6d99aa83dfae5fe7d89648d5612ae933dc725011 Mon Sep 17 00:00:00 2001 From: Eugene Vinitsky Date: Tue, 31 Mar 2026 22:58:22 -0400 Subject: [PATCH 17/24] Make symlog reward transform optional, off by default MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Symlog compresses large penalties (e.g. collision -3.5 → -1.5), weakening the learning signal for collision avoidance. Co-Authored-By: Claude Opus 4.6 (1M context) --- pufferlib/config/default.ini | 1 + pufferlib/pufferl.py | 14 ++++++++++++-- 2 files changed, 13 insertions(+), 2 deletions(-) diff --git a/pufferlib/config/default.ini b/pufferlib/config/default.ini index 96ce1d06cf..1b5dc26d64 100644 --- a/pufferlib/config/default.ini +++ b/pufferlib/config/default.ini @@ -63,6 +63,7 @@ prio_beta0 = 0.2 ppo_granularity = auto adv_filter_ewma_beta = 0.25 adv_filter_threshold_scale = 0.01 +symlog_rewards = False [sweep] method = Protein diff --git a/pufferlib/pufferl.py b/pufferlib/pufferl.py index df9a352b2b..5f9c687c8f 100644 --- a/pufferlib/pufferl.py +++ b/pufferlib/pufferl.py @@ -322,7 +322,8 @@ def evaluate(self): logits, value = self.policy.forward_eval(o_device, state) action, logprob, _ = pufferlib.pytorch.sample_logits(logits) - r = torch.sign(r) * torch.log1p(torch.abs(r)) + if config.get("symlog_rewards", False): + r = torch.sign(r) * torch.log1p(torch.abs(r)) profile("eval_copy", epoch) with torch.no_grad(): @@ -1624,7 +1625,16 @@ def eval(env_name, args=None, vecenv=None, policy=None): if isinstance(logits, torch.distributions.Normal): action = np.clip(action, vecenv.action_space.low, vecenv.action_space.high) - ob = vecenv.step(action)[0] + step_result = vecenv.step(action) + ob = step_result[0] + step_info = step_result[4] if len(step_result) > 4 else [] + for info_item in step_info: + if isinstance(info_item, dict): + print("\n=== Episode Metrics ===") + for k, v in sorted(info_item.items()): + if isinstance(v, (int, float)): + print(f" {k}: {v:.4f}") + print("=======================\n") if len(frames) > 0 and len(frames) == args["save_frames"]: import imageio From 59672607db5264ee22bbaf49634a326d1be8f7ef Mon Sep 17 00:00:00 2001 From: Eugene Vinitsky Date: Tue, 31 Mar 2026 23:21:53 -0400 Subject: [PATCH 18/24] Rewrite drivenet.h to match turbostream architecture, fix rendering - New drivenet.h matching PyTorch gigaflow encoder architecture (6 encoders + 4-layer backbone, no LSTM) - Fix double-rotation bug in mode-0 wireframe (heading applied twice) - Add traffic light and road edge rendering to 3D view - Color-code collision types (orange=vehicle, red=offroad, yellow=red light) - Store render_corners on Agent struct from compute_metrics - Add debug config printing to visualize.c - Add render_maps.py script for static map visualization Co-Authored-By: Claude Opus 4.6 (1M context) --- pufferlib/ocean/drive/datatypes.h | 1 + pufferlib/ocean/drive/drive.h | 5 + pufferlib/ocean/drive/drivenet.h | 515 +++++++++++++++++++----------- pufferlib/ocean/drive/render.h | 120 +++++-- pufferlib/ocean/drive/visualize.c | 48 ++- scripts/render_maps.py | 105 ++++++ 6 files changed, 566 insertions(+), 228 deletions(-) create mode 100644 scripts/render_maps.py diff --git a/pufferlib/ocean/drive/datatypes.h b/pufferlib/ocean/drive/datatypes.h index fc2361cd74..1904c4d258 100644 --- a/pufferlib/ocean/drive/datatypes.h +++ b/pufferlib/ocean/drive/datatypes.h @@ -210,6 +210,7 @@ struct Agent { float sim_width; float sim_height; int sim_valid; + float render_corners[4][2]; // Bounding box corners computed by simulation // Route information int route_length; diff --git a/pufferlib/ocean/drive/drive.h b/pufferlib/ocean/drive/drive.h index d2aeb9c4b6..8a7a579530 100644 --- a/pufferlib/ocean/drive/drive.h +++ b/pufferlib/ocean/drive/drive.h @@ -3503,6 +3503,8 @@ static void compute_metrics(Drive *env, int agent_idx) { corners[i][1] = agent->sim_y + (offsets[i][0] * half_length * sin_heading + offsets[i][1] * half_width * cos_heading); } + // Store for rendering + memcpy(agent->render_corners, corners, sizeof(corners)); GridMapEntity entity_list[MAX_ENTITIES_PER_CELL * 25]; // Array big enough for all neighboring cells int list_size = get_neighbors_entities(env, agent->sim_x, agent->sim_y, entity_list, MAX_ENTITIES_PER_CELL * 25, @@ -3539,6 +3541,9 @@ static void compute_metrics(Drive *env, int agent_idx) { int next = (k + 1) % 4; if (check_line_intersection(corners[k], corners[next], start, end)) { is_offroad = true; + printf("[OFFROAD] agent %d at (%.1f,%.1f,%.1f) hit edge elem %d geom %d at z=%.1f (dz=%.1f)\n", + agent_idx, agent->sim_x, agent->sim_y, agent->sim_z, + entity_idx, geometry_idx, element->z[geometry_idx], abs_dz); break; } } diff --git a/pufferlib/ocean/drive/drivenet.h b/pufferlib/ocean/drive/drivenet.h index e2ee50cc4b..fdad05c585 100644 --- a/pufferlib/ocean/drive/drivenet.h +++ b/pufferlib/ocean/drive/drivenet.h @@ -8,239 +8,386 @@ #include #include +// Tanh activation (not in puffernet.h) +static inline void tanh_activation(float *data, int size) { + for (int i = 0; i < size; i++) { + data[i] = tanhf(data[i]); + } +} + +// Gigaflow-style encoder: Linear → LayerNorm → Tanh → Linear +// Weights order matches PyTorch: encoder.0 (Linear), encoder.1 (LayerNorm), encoder.4 (Linear) +typedef struct { + Linear *linear1; + LayerNorm *layernorm; + Linear *linear2; + float *ln_output; + int batch_size; + int input_size; +} Encoder; + +Encoder *make_encoder(Weights *weights, int batch_size, int in_features, int input_size) { + Encoder *enc = calloc(1, sizeof(Encoder)); + enc->batch_size = batch_size; + enc->input_size = input_size; + enc->linear1 = make_linear(weights, batch_size, in_features, input_size); + enc->layernorm = make_layernorm(weights, batch_size, input_size); + enc->ln_output = calloc(batch_size * input_size, sizeof(float)); + enc->linear2 = make_linear(weights, batch_size, input_size, input_size); + return enc; +} + +// Apply encoder to flat input (batch_size x in_features) +void encoder_forward(Encoder *enc, float *input) { + linear(enc->linear1, input); + layernorm(enc->layernorm, enc->linear1->output); + // Tanh after layernorm + memcpy(enc->ln_output, enc->layernorm->output, enc->batch_size * enc->input_size * sizeof(float)); + tanh_activation(enc->ln_output, enc->batch_size * enc->input_size); + // Second linear + linear(enc->linear2, enc->ln_output); +} + +// Apply encoder per-object: input is [batch_size * num_objects * features] +// Output is max-pooled: [batch_size * input_size] +void encoder_forward_objects(Encoder *enc, float *input, float *output, + int batch_size, int num_objects, int in_features, int input_size) { + float *obj_encoded = calloc(batch_size * num_objects * input_size, sizeof(float)); + + for (int b = 0; b < batch_size; b++) { + for (int obj = 0; obj < num_objects; obj++) { + float *obj_in = &input[b * num_objects * in_features + obj * in_features]; + float *lin1_out = &obj_encoded[b * num_objects * input_size + obj * input_size]; + + // Linear1 + _linear(obj_in, enc->linear1->weights, enc->linear1->bias, lin1_out, 1, in_features, input_size); + + // LayerNorm + float ln_out[input_size]; + _layernorm(lin1_out, enc->layernorm->weights, enc->layernorm->bias, ln_out, 1, input_size); + + // Tanh + tanh_activation(ln_out, input_size); + + // Linear2 + float *final_out = &obj_encoded[b * num_objects * input_size + obj * input_size]; + _linear(ln_out, enc->linear2->weights, enc->linear2->bias, final_out, 1, input_size, input_size); + } + } + + // Max pool over objects dimension + for (int b = 0; b < batch_size; b++) { + for (int f = 0; f < input_size; f++) { + float max_val = -1e30f; + for (int obj = 0; obj < num_objects; obj++) { + float val = obj_encoded[b * num_objects * input_size + obj * input_size + f]; + if (val > max_val) max_val = val; + } + output[b * input_size + f] = max_val; + } + } + + free(obj_encoded); +} + +void free_encoder(Encoder *enc) { + free(enc->linear1); + free(enc->layernorm); + free(enc->linear2); + free(enc->ln_output); + free(enc); +} + typedef struct DriveNet DriveNet; struct DriveNet { int num_agents; int ego_dim; - float *obs_self; - float *obs_partner; - float *obs_road; - float *partner_linear_output; - float *road_linear_output; - float *partner_layernorm_output; - float *road_layernorm_output; - float *partner_linear_output_two; - float *road_linear_output_two; - Linear *ego_encoder; - Linear *road_encoder; - Linear *partner_encoder; - LayerNorm *ego_layernorm; - LayerNorm *road_layernorm; - LayerNorm *partner_layernorm; - Linear *ego_encoder_two; - Linear *road_encoder_two; - Linear *partner_encoder_two; - MaxDim1 *partner_max; - MaxDim1 *road_max; - CatDim1 *cat1; - CatDim1 *cat2; - GELU *gelu; - Linear *shared_embedding; - ReLU *relu; - LSTM *lstm; + int input_size; + int backbone_hidden_size; + + // Observation dimensions + int max_partner_obs; + int partner_features; + int max_lane_obs; + int max_boundary_obs; + int road_features; + int max_traffic_light_obs; + int traffic_light_features_raw; + int traffic_light_features_onehot; + int conditioning_dim; + + // Encoders + Encoder *ego_encoder; + Encoder *lane_encoder; + Encoder *boundary_encoder; + Encoder *partner_encoder; + Encoder *traffic_light_encoder; + Encoder *conditioning_encoder; + + // Temp buffers for per-object encoding + max pooling + float *lane_pooled; + float *boundary_pooled; + float *partner_pooled; + float *traffic_light_pooled; + float *traffic_light_onehot_buf; + + // Backbone layers: GELU → Linear → GELU → Linear → ... → GELU + int num_backbone_layers; + GELU **backbone_gelus; + Linear **backbone_linears; + + // Heads Linear *actor; Linear *value_fn; + + // Final GELU before heads + GELU *final_gelu; + + // Action selection Multidiscrete *multidiscrete; + + // Concatenation buffer + float *concat_buf; }; DriveNet *init_drivenet(Weights *weights, int num_agents, int dynamics_model) { DriveNet *net = calloc(1, sizeof(DriveNet)); - int hidden_size = 256; + + // Architecture config (must match torch.py / drive.ini) int input_size = 64; + int backbone_hidden_size = 512; + int backbone_num_layers = 4; + + int ego_dim = (dynamics_model == JERK) ? EGO_FEATURES_JERK : EGO_FEATURES_CLASSIC; + int partner_features = PARTNER_FEATURES; // 8 + int road_features = ROAD_FEATURES; // 7 + int traffic_light_features_raw = TRAFFIC_LIGHT_FEATURES; // 6 + int num_tl_states = 4; // NUM_TRAFFIC_LIGHT_STATES from datatypes.h + int traffic_light_features_onehot = traffic_light_features_raw - 1 + num_tl_states; // 5+4=9 - int ego_dim = (dynamics_model == JERK) ? 10 : 7; + // Observation counts (from drive.ini defaults) + int max_partner_obs = 20; + int max_lane_obs = 64; + int max_boundary_obs = 32; + int max_traffic_light_obs = 4; - // Determine action space size based on dynamics model - int action_size, logit_sizes[2]; - int action_dim; + // Conditioning: target waypoints (static: 3 waypoints * 3 features = 9) + int conditioning_dim = 9; + + // Action space + int action_size, logit_sizes[1]; if (dynamics_model == CLASSIC) { - action_size = 7 * 13; // Joint action space - logit_sizes[0] = 7 * 13; - action_dim = 1; - } else { // JERK - action_size = 4 * 3; // Joint action space (4 longitudinal × 3 lateral = 12) - logit_sizes[0] = 4 * 3; - action_dim = 1; + action_size = 7 * 9; // 7 accel * 9 steer + logit_sizes[0] = action_size; + } else { // JERK + action_size = 4 * 3; // 4 longitudinal * 3 lateral = 12 + logit_sizes[0] = action_size; } net->num_agents = num_agents; net->ego_dim = ego_dim; - net->obs_self = calloc(num_agents * ego_dim, sizeof(float)); - net->obs_partner = calloc(num_agents * 63 * 7, sizeof(float)); - net->obs_road = calloc(num_agents * 200 * 13, sizeof(float)); - net->partner_linear_output = calloc(num_agents * 63 * input_size, sizeof(float)); - net->road_linear_output = calloc(num_agents * 200 * input_size, sizeof(float)); - net->partner_linear_output_two = calloc(num_agents * 63 * input_size, sizeof(float)); - net->road_linear_output_two = calloc(num_agents * 200 * input_size, sizeof(float)); - net->partner_layernorm_output = calloc(num_agents * 63 * input_size, sizeof(float)); - net->road_layernorm_output = calloc(num_agents * 200 * input_size, sizeof(float)); - net->ego_encoder = make_linear(weights, num_agents, ego_dim, input_size); - net->ego_layernorm = make_layernorm(weights, num_agents, input_size); - net->ego_encoder_two = make_linear(weights, num_agents, input_size, input_size); - net->road_encoder = make_linear(weights, num_agents, 13, input_size); - net->road_layernorm = make_layernorm(weights, num_agents, input_size); - net->road_encoder_two = make_linear(weights, num_agents, input_size, input_size); - net->partner_encoder = make_linear(weights, num_agents, 7, input_size); - net->partner_layernorm = make_layernorm(weights, num_agents, input_size); - net->partner_encoder_two = make_linear(weights, num_agents, input_size, input_size); - net->partner_max = make_max_dim1(num_agents, 63, input_size); - net->road_max = make_max_dim1(num_agents, 200, input_size); - net->cat1 = make_cat_dim1(num_agents, input_size, input_size); - net->cat2 = make_cat_dim1(num_agents, input_size + input_size, input_size); - net->gelu = make_gelu(num_agents, 3 * input_size); - net->shared_embedding = make_linear(weights, num_agents, input_size * 3, hidden_size); - net->relu = make_relu(num_agents, hidden_size); - net->actor = make_linear(weights, num_agents, hidden_size, action_size); - net->value_fn = make_linear(weights, num_agents, hidden_size, 1); - net->lstm = make_lstm(weights, num_agents, hidden_size, 256); - memset(net->lstm->state_h, 0, num_agents * 256 * sizeof(float)); - memset(net->lstm->state_c, 0, num_agents * 256 * sizeof(float)); - net->multidiscrete = make_multidiscrete(num_agents, logit_sizes, action_dim); + net->input_size = input_size; + net->backbone_hidden_size = backbone_hidden_size; + net->max_partner_obs = max_partner_obs; + net->partner_features = partner_features; + net->max_lane_obs = max_lane_obs; + net->max_boundary_obs = max_boundary_obs; + net->road_features = road_features; + net->max_traffic_light_obs = max_traffic_light_obs; + net->traffic_light_features_raw = traffic_light_features_raw; + net->traffic_light_features_onehot = traffic_light_features_onehot; + net->conditioning_dim = conditioning_dim; + + // Create encoders (weight order must match PyTorch export) + // actor_backbone.ego_encoder.{0,1,4} + net->ego_encoder = make_encoder(weights, num_agents, ego_dim, input_size); + // actor_backbone.lane_encoder.{0,1,4} + net->lane_encoder = make_encoder(weights, num_agents, road_features, input_size); + // actor_backbone.boundary_encoder.{0,1,4} + net->boundary_encoder = make_encoder(weights, num_agents, road_features, input_size); + // actor_backbone.partner_encoder.{0,1,4} + net->partner_encoder = make_encoder(weights, num_agents, partner_features, input_size); + // actor_backbone.traffic_light_encoder.{0,1,4} + net->traffic_light_encoder = make_encoder(weights, num_agents, traffic_light_features_onehot, input_size); + // actor_backbone.conditioning_encoder.{0,1,4} + net->conditioning_encoder = make_encoder(weights, num_agents, conditioning_dim, input_size); + + // Pooling output buffers + net->lane_pooled = calloc(num_agents * input_size, sizeof(float)); + net->boundary_pooled = calloc(num_agents * input_size, sizeof(float)); + net->partner_pooled = calloc(num_agents * input_size, sizeof(float)); + net->traffic_light_pooled = calloc(num_agents * input_size, sizeof(float)); + net->traffic_light_onehot_buf = calloc(num_agents * max_traffic_light_obs * traffic_light_features_onehot, sizeof(float)); + + // Backbone: GELU → Linear → GELU → Linear → ... → GELU + // actor_backbone.backbone.{1,3,5,7} are the Linear layers (0,2,4,6,8 are GELUs) + net->num_backbone_layers = backbone_num_layers; + net->backbone_gelus = calloc(backbone_num_layers + 1, sizeof(GELU *)); + net->backbone_linears = calloc(backbone_num_layers, sizeof(Linear *)); + + int bb_in = 6 * input_size; // 6 feature sets * 64 = 384 + for (int i = 0; i < backbone_num_layers; i++) { + net->backbone_gelus[i] = make_gelu(num_agents, bb_in); + net->backbone_linears[i] = make_linear(weights, num_agents, bb_in, backbone_hidden_size); + bb_in = backbone_hidden_size; + } + net->final_gelu = make_gelu(num_agents, backbone_hidden_size); + + // Actor and critic heads + // actor_head.0, critic_head.0 + net->actor = make_linear(weights, num_agents, backbone_hidden_size, action_size); + net->value_fn = make_linear(weights, num_agents, backbone_hidden_size, 1); + + // Concat buffer + net->concat_buf = calloc(num_agents * 6 * input_size, sizeof(float)); + + // Action selection + net->multidiscrete = make_multidiscrete(num_agents, logit_sizes, 1); + return net; } void free_drivenet(DriveNet *net) { - free(net->obs_self); - free(net->obs_partner); - free(net->obs_road); - free(net->partner_linear_output); - free(net->road_linear_output); - free(net->partner_linear_output_two); - free(net->road_linear_output_two); - free(net->partner_layernorm_output); - free(net->road_layernorm_output); - free(net->ego_encoder); - free(net->road_encoder); - free(net->partner_encoder); - free(net->ego_layernorm); - free(net->road_layernorm); - free(net->partner_layernorm); - free(net->ego_encoder_two); - free(net->road_encoder_two); - free(net->partner_encoder_two); - free(net->partner_max); - free(net->road_max); - free(net->cat1); - free(net->cat2); - free(net->gelu); - free(net->shared_embedding); - free(net->relu); + free_encoder(net->ego_encoder); + free_encoder(net->lane_encoder); + free_encoder(net->boundary_encoder); + free_encoder(net->partner_encoder); + free_encoder(net->traffic_light_encoder); + free_encoder(net->conditioning_encoder); + free(net->lane_pooled); + free(net->boundary_pooled); + free(net->partner_pooled); + free(net->traffic_light_pooled); + free(net->traffic_light_onehot_buf); + for (int i = 0; i < net->num_backbone_layers; i++) { + free(net->backbone_gelus[i]); + free(net->backbone_linears[i]); + } + free(net->backbone_gelus); + free(net->backbone_linears); + free(net->final_gelu); + free(net->concat_buf); free(net->multidiscrete); free(net->actor); free(net->value_fn); - free(net->lstm); free(net); } void forward(DriveNet *net, float *observations, int *actions) { + int n = net->num_agents; int ego_dim = net->ego_dim; + int input_size = net->input_size; - // Clear previous observations - memset(net->obs_self, 0, net->num_agents * ego_dim * sizeof(float)); - memset(net->obs_partner, 0, net->num_agents * 63 * 7 * sizeof(float)); - memset(net->obs_road, 0, net->num_agents * 200 * 13 * sizeof(float)); - - for (int b = 0; b < net->num_agents; b++) { - int b_offset = b * (ego_dim + 63 * 7 + 200 * 7); - int partner_offset = b_offset + ego_dim; - int road_offset = b_offset + ego_dim + 63 * 7; + // Observation layout (from torch.py forward): + // [ego | conditioning | partners | lanes | boundaries | traffic_lights | stop_signs] + int cond_dim = net->conditioning_dim; + int partner_dim = net->max_partner_obs * net->partner_features; + int lane_dim = net->max_lane_obs * net->road_features; + int boundary_dim = net->max_boundary_obs * net->road_features; + int tl_dim = net->max_traffic_light_obs * net->traffic_light_features_raw; - // Process self observation - for (int i = 0; i < ego_dim; i++) { - net->obs_self[b * ego_dim + i] = observations[b_offset + i]; - } + int obs_stride = ego_dim + cond_dim + partner_dim + lane_dim + boundary_dim + tl_dim; - // Process partner observation - for (int i = 0; i < 63; i++) { - for (int j = 0; j < 7; j++) { - net->obs_partner[b * 63 * 7 + i * 7 + j] = observations[partner_offset + i * 7 + j]; - } - } + // 1. Ego encoder (flat, no pooling) + // Extract ego observations into contiguous buffer + float *ego_buf = calloc(n * ego_dim, sizeof(float)); + for (int b = 0; b < n; b++) { + memcpy(&ego_buf[b * ego_dim], &observations[b * obs_stride], ego_dim * sizeof(float)); + } + encoder_forward(net->ego_encoder, ego_buf); - // Process road observation - for (int i = 0; i < 200; i++) { - for (int j = 0; j < 7; j++) { - net->obs_road[b * 200 * 13 + i * 13 + j] = observations[road_offset + i * 7 + j]; - } - for (int j = 0; j < 7; j++) { - if (j == observations[road_offset + i * 7 + 6]) { - net->obs_road[b * 200 * 13 + i * 13 + 6 + j] = 1.0f; - } else { - net->obs_road[b * 200 * 13 + i * 13 + 6 + j] = 0.0f; - } - } - } + // 2. Conditioning encoder (flat, no pooling) + float *cond_buf = calloc(n * cond_dim, sizeof(float)); + for (int b = 0; b < n; b++) { + memcpy(&cond_buf[b * cond_dim], &observations[b * obs_stride + ego_dim], cond_dim * sizeof(float)); } + encoder_forward(net->conditioning_encoder, cond_buf); - // Forward pass through the network - linear(net->ego_encoder, net->obs_self); - layernorm(net->ego_layernorm, net->ego_encoder->output); - linear(net->ego_encoder_two, net->ego_layernorm->output); - for (int b = 0; b < net->num_agents; b++) { - for (int obj = 0; obj < 63; obj++) { - // Get the 7 features for this object - float *obj_features = &net->obs_partner[b * 63 * 7 + obj * 7]; - // Apply linear layer to this object - _linear(obj_features, net->partner_encoder->weights, net->partner_encoder->bias, - &net->partner_linear_output[b * 63 * 64 + obj * 64], 1, 7, 64); - } + // 3. Lane encoder (per-object + max pool) + float *lane_buf = calloc(n * lane_dim, sizeof(float)); + for (int b = 0; b < n; b++) { + int offset = ego_dim + cond_dim + partner_dim; + memcpy(&lane_buf[b * lane_dim], &observations[b * obs_stride + offset], lane_dim * sizeof(float)); } + encoder_forward_objects(net->lane_encoder, lane_buf, net->lane_pooled, + n, net->max_lane_obs, net->road_features, input_size); - for (int b = 0; b < net->num_agents; b++) { - for (int obj = 0; obj < 63; obj++) { - float *after_first = &net->partner_linear_output[b * 63 * 64 + obj * 64]; - _layernorm(after_first, net->partner_layernorm->weights, net->partner_layernorm->bias, - &net->partner_layernorm_output[b * 63 * 64 + obj * 64], 1, 64); - } + // 4. Boundary encoder (per-object + max pool) + float *boundary_buf = calloc(n * boundary_dim, sizeof(float)); + for (int b = 0; b < n; b++) { + int offset = ego_dim + cond_dim + partner_dim + lane_dim; + memcpy(&boundary_buf[b * boundary_dim], &observations[b * obs_stride + offset], boundary_dim * sizeof(float)); } - for (int b = 0; b < net->num_agents; b++) { - for (int obj = 0; obj < 63; obj++) { - // Get the 7 features for this object - float *obj_features = &net->partner_layernorm_output[b * 63 * 64 + obj * 64]; - // Apply linear layer to this object - _linear(obj_features, net->partner_encoder_two->weights, net->partner_encoder_two->bias, - &net->partner_linear_output_two[b * 63 * 64 + obj * 64], 1, 64, 64); - } + encoder_forward_objects(net->boundary_encoder, boundary_buf, net->boundary_pooled, + n, net->max_boundary_obs, net->road_features, input_size); + + // 5. Partner encoder (per-object + max pool) + float *partner_buf = calloc(n * partner_dim, sizeof(float)); + for (int b = 0; b < n; b++) { + int offset = ego_dim + cond_dim; + memcpy(&partner_buf[b * partner_dim], &observations[b * obs_stride + offset], partner_dim * sizeof(float)); } + encoder_forward_objects(net->partner_encoder, partner_buf, net->partner_pooled, + n, net->max_partner_obs, net->partner_features, input_size); - // Process road objects: apply linear to each object individually - for (int b = 0; b < net->num_agents; b++) { - for (int obj = 0; obj < 200; obj++) { - // Get the 13 features for this object - float *obj_features = &net->obs_road[b * 200 * 13 + obj * 13]; - // Apply linear layer to this object - _linear(obj_features, net->road_encoder->weights, net->road_encoder->bias, - &net->road_linear_output[b * 200 * 64 + obj * 64], 1, 13, 64); + // 6. Traffic light encoder (per-object + max pool, with one-hot encoding) + // Raw: [n * max_tl * 6], need to convert last feature to one-hot → [n * max_tl * 9] + memset(net->traffic_light_onehot_buf, 0, n * net->max_traffic_light_obs * net->traffic_light_features_onehot * sizeof(float)); + for (int b = 0; b < n; b++) { + int raw_offset = ego_dim + cond_dim + partner_dim + lane_dim + boundary_dim; + for (int obj = 0; obj < net->max_traffic_light_obs; obj++) { + float *raw = &observations[b * obs_stride + raw_offset + obj * net->traffic_light_features_raw]; + float *out = &net->traffic_light_onehot_buf[(b * net->max_traffic_light_obs + obj) * net->traffic_light_features_onehot]; + // Copy continuous features (first 5) + for (int f = 0; f < net->traffic_light_features_raw - 1; f++) { + out[f] = raw[f]; + } + // One-hot encode the last feature (traffic light state) + int state = (int)raw[net->traffic_light_features_raw - 1]; + if (state >= 0 && state < 4) { + out[net->traffic_light_features_raw - 1 + state] = 1.0f; + } } } + encoder_forward_objects(net->traffic_light_encoder, net->traffic_light_onehot_buf, net->traffic_light_pooled, + n, net->max_traffic_light_obs, net->traffic_light_features_onehot, input_size); - // Apply layer norm and second linear to each road object - for (int b = 0; b < net->num_agents; b++) { - for (int obj = 0; obj < 200; obj++) { - float *after_first = &net->road_linear_output[b * 200 * 64 + obj * 64]; - _layernorm(after_first, net->road_layernorm->weights, net->road_layernorm->bias, - &net->road_layernorm_output[b * 200 * 64 + obj * 64], 1, 64); - } + // Concatenate all features: [ego | lane | boundary | partner | traffic_light | conditioning] + for (int b = 0; b < n; b++) { + int off = 0; + memcpy(&net->concat_buf[b * 6 * input_size + off], &net->ego_encoder->linear2->output[b * input_size], input_size * sizeof(float)); + off += input_size; + memcpy(&net->concat_buf[b * 6 * input_size + off], &net->lane_pooled[b * input_size], input_size * sizeof(float)); + off += input_size; + memcpy(&net->concat_buf[b * 6 * input_size + off], &net->boundary_pooled[b * input_size], input_size * sizeof(float)); + off += input_size; + memcpy(&net->concat_buf[b * 6 * input_size + off], &net->partner_pooled[b * input_size], input_size * sizeof(float)); + off += input_size; + memcpy(&net->concat_buf[b * 6 * input_size + off], &net->traffic_light_pooled[b * input_size], input_size * sizeof(float)); + off += input_size; + memcpy(&net->concat_buf[b * 6 * input_size + off], &net->conditioning_encoder->linear2->output[b * input_size], input_size * sizeof(float)); } - for (int b = 0; b < net->num_agents; b++) { - for (int obj = 0; obj < 200; obj++) { - float *after_first = &net->road_layernorm_output[b * 200 * 64 + obj * 64]; - _linear(after_first, net->road_encoder_two->weights, net->road_encoder_two->bias, - &net->road_linear_output_two[b * 200 * 64 + obj * 64], 1, 64, 64); - } + + // Backbone: GELU → Linear → GELU → Linear → ... → GELU + float *bb_input = net->concat_buf; + for (int i = 0; i < net->num_backbone_layers; i++) { + gelu(net->backbone_gelus[i], bb_input); + linear(net->backbone_linears[i], net->backbone_gelus[i]->output); + bb_input = net->backbone_linears[i]->output; } + gelu(net->final_gelu, bb_input); - max_dim1(net->partner_max, net->partner_linear_output_two); - max_dim1(net->road_max, net->road_linear_output_two); - cat_dim1(net->cat1, net->ego_encoder_two->output, net->road_max->output); - cat_dim1(net->cat2, net->cat1->output, net->partner_max->output); - gelu(net->gelu, net->cat2->output); - linear(net->shared_embedding, net->gelu->output); - relu(net->relu, net->shared_embedding->output); - lstm(net->lstm, net->relu->output); - linear(net->actor, net->lstm->state_h); - linear(net->value_fn, net->lstm->state_h); + // Actor and critic heads + linear(net->actor, net->final_gelu->output); + linear(net->value_fn, net->final_gelu->output); // Get action by taking argmax of actor output softmax_multidiscrete(net->multidiscrete, net->actor->output, actions); + + // Cleanup temp buffers + free(ego_buf); + free(cond_buf); + free(lane_buf); + free(boundary_buf); + free(partner_buf); } diff --git a/pufferlib/ocean/drive/render.h b/pufferlib/ocean/drive/render.h index c3a91661ae..d3ee4bd332 100644 --- a/pufferlib/ocean/drive/render.h +++ b/pufferlib/ocean/drive/render.h @@ -415,6 +415,61 @@ void draw_scene(Drive *env, Client *client, int mode, int obs_only, int lasers, DrawLine3D((Vector3){env->grid_map->top_left_x, env->grid_map->bottom_right_y, 0}, (Vector3){env->grid_map->bottom_right_x, env->grid_map->bottom_right_y, 0}, PUFF_CYAN); + // Draw road edges (collision boundaries) as red lines + rlSetLineWidth(4.0f); + for (int i = 0; i < env->num_road_elements; i++) { + RoadMapElement *elem = &env->road_elements[i]; + if (!is_road_edge(elem->type)) continue; + for (int j = 0; j < elem->segment_length - 1; j++) { + DrawLine3D( + (Vector3){elem->x[j], elem->y[j], elem->z[j] + 1.0f}, + (Vector3){elem->x[j+1], elem->y[j+1], elem->z[j+1] + 1.0f}, + RED); + } + } + // Draw drivable lanes as green lines + rlSetLineWidth(2.0f); + for (int i = 0; i < env->num_road_elements; i++) { + RoadMapElement *elem = &env->road_elements[i]; + if (!is_drivable_road_lane(elem->type)) continue; + for (int j = 0; j < elem->segment_length - 1; j++) { + DrawLine3D( + (Vector3){elem->x[j], elem->y[j], elem->z[j] + 0.5f}, + (Vector3){elem->x[j+1], elem->y[j+1], elem->z[j+1] + 0.5f}, + GREEN); + } + } + + // Draw traffic lights and stop signs + for (int i = 0; i < env->num_traffic_elements; i++) { + TrafficControlElement *tc = &env->traffic_elements[i]; + Vector3 p1 = {tc->stop_line[0], tc->stop_line[1], tc->stop_line[2] + 0.2f}; + Vector3 p2 = {tc->stop_line[3], tc->stop_line[4], tc->stop_line[5] + 0.2f}; + + if (tc->type == TRAFFIC_LIGHT) { + // Get current state + int state = TL_STATE_DISABLED; + if (tc->states && env->timestep < tc->state_length) { + state = tc->states[env->timestep]; + } + Color tl_color = GRAY; + if (is_traffic_light_red(state)) tl_color = RED; + else if (is_traffic_light_yellow(state)) tl_color = YELLOW; + else if (is_traffic_light_green(state)) tl_color = GREEN; + + rlSetLineWidth(4.0f); + DrawLine3D(p1, p2, tl_color); + // Draw a sphere at midpoint for visibility + Vector3 mid = {(p1.x + p2.x) / 2, (p1.y + p2.y) / 2, (p1.z + p2.z) / 2 + 0.5f}; + DrawSphere(mid, 0.8f, tl_color); + } else if (tc->type == STOP_SIGN) { + rlSetLineWidth(3.0f); + DrawLine3D(p1, p2, MAGENTA); + Vector3 mid = {(p1.x + p2.x) / 2, (p1.y + p2.y) / 2, (p1.z + p2.z) / 2 + 0.5f}; + DrawSphere(mid, 0.6f, MAGENTA); + } + } + for (int i = 0; i < env->num_total_agents; i++) { Agent *agent = &env->agents[i]; // Draw objects @@ -452,22 +507,20 @@ void draw_scene(Drive *env, Client *client, int mode, int obs_only, int lasers, float cos_heading = cosf(heading); float sin_heading = sinf(heading); - // Calculate half dimensions + // Compute corners fresh from current agent state float half_len = agent->sim_length * 0.5f; - float half_width = agent->sim_width * 0.5f; - - // Calculate the four corners of the collision box + float half_wid = agent->sim_width * 0.5f; + float ch = cosf(agent->sim_heading); + float sh = sinf(agent->sim_heading); Vector3 corners[4] = { - (Vector3){position.x + (half_len * cos_heading - half_width * sin_heading), - position.y + (half_len * sin_heading + half_width * cos_heading), position.z}, - - (Vector3){position.x + (half_len * cos_heading + half_width * sin_heading), - position.y + (half_len * sin_heading - half_width * cos_heading), position.z}, - (Vector3){position.x + (-half_len * cos_heading + half_width * sin_heading), - position.y + (-half_len * sin_heading - half_width * cos_heading), position.z}, - (Vector3){position.x + (-half_len * cos_heading - half_width * sin_heading), - position.y + (-half_len * sin_heading + half_width * cos_heading), position.z}, - + {agent->sim_x + (-half_len * ch - half_wid * sh), + agent->sim_y + (-half_len * sh + half_wid * ch), position.z}, + {agent->sim_x + ( half_len * ch - half_wid * sh), + agent->sim_y + ( half_len * sh + half_wid * ch), position.z}, + {agent->sim_x + ( half_len * ch + half_wid * sh), + agent->sim_y + ( half_len * sh - half_wid * ch), position.z}, + {agent->sim_x + (-half_len * ch + half_wid * sh), + agent->sim_y + (-half_len * sh - half_wid * ch), position.z}, }; if (agent_index == env->human_agent_idx && !agent->metrics_array[REACHED_GOAL_IDX]) { @@ -483,24 +536,26 @@ void draw_scene(Drive *env, Client *client, int mode, int obs_only, int lasers, car_color = GOLD; // expert replay if (is_active_agent) car_color = BLUE; // policy-controlled - if (is_active_agent && (agent->metrics_array[COLLISION_IDX] > 0 || agent->metrics_array[OFFROAD_IDX] > 0 || - agent->metrics_array[RED_LIGHT_IDX] > 0)) - car_color = RED; + if (is_active_agent && agent->metrics_array[COLLISION_IDX] > 0) + car_color = ORANGE; // vehicle collision + if (is_active_agent && agent->metrics_array[OFFROAD_IDX] > 0) + car_color = RED; // offroad + if (is_active_agent && agent->metrics_array[RED_LIGHT_IDX] > 0) + car_color = YELLOW; // red light violation rlSetLineWidth(3.0f); for (int j = 0; j < 4; j++) { DrawLine3D(corners[j], corners[(j + 1) % 4], car_color); } // --- Draw a heading arrow pointing forward --- Vector3 arrowStart = position; - Vector3 arrowEnd = {position.x + cos_heading * half_len * 1.5f, // extend arrow beyond car - position.y + sin_heading * half_len * 1.5f, position.z}; + Vector3 arrowEnd = {position.x + agent->cos_heading * half_len * 1.5f, + position.y + agent->sin_heading * half_len * 1.5f, position.z}; DrawLine3D(arrowStart, arrowEnd, car_color); DrawSphere(arrowEnd, 0.2f, car_color); // arrow tip } else { rlPushMatrix(); - // Translate to position, rotate around Y axis, then draw rlTranslatef(position.x, position.y, position.z); rlRotatef(heading * RAD2DEG, 0.0f, 0.0f, 1.0f); // Convert radians to degrees // Determine color based on status @@ -530,29 +585,26 @@ void draw_scene(Drive *env, Client *client, int mode, int obs_only, int lasers, DrawModelEx(car_model, (Vector3){0, 0, 0}, (Vector3){1, 0, 0}, 90.0f, scale, WHITE); { - float cos_heading = cosf(heading); - float sin_heading = sinf(heading); + // Corners in local space — rlRotatef already applies heading rotation float half_len = agent->sim_length * 0.5f; float half_width = agent->sim_width * 0.5f; Vector3 corners[4] = { - (Vector3){0 + (half_len * cos_heading - half_width * sin_heading), - 0 + (half_len * sin_heading + half_width * cos_heading), 0}, - (Vector3){0 + (half_len * cos_heading + half_width * sin_heading), - 0 + (half_len * sin_heading - half_width * cos_heading), 0}, - (Vector3){0 + (-half_len * cos_heading + half_width * sin_heading), - 0 + (-half_len * sin_heading - half_width * cos_heading), 0}, - (Vector3){0 + (-half_len * cos_heading - half_width * sin_heading), - 0 + (-half_len * sin_heading + half_width * cos_heading), 0}, + (Vector3){ half_len, half_width, 0}, + (Vector3){ half_len, -half_width, 0}, + (Vector3){-half_len, -half_width, 0}, + (Vector3){-half_len, half_width, 0}, }; Color wire_color = GRAY; // static if (!is_active_agent && agent->mark_as_expert == 1) wire_color = GOLD; // expert replay if (is_active_agent) wire_color = BLUE; // policy - if (is_active_agent && - (agent->metrics_array[COLLISION_IDX] > 0 || agent->metrics_array[OFFROAD_IDX] > 0 || - agent->metrics_array[RED_LIGHT_IDX] > 0)) - wire_color = RED; + if (is_active_agent && agent->metrics_array[COLLISION_IDX] > 0) + wire_color = ORANGE; // vehicle collision + if (is_active_agent && agent->metrics_array[OFFROAD_IDX] > 0) + wire_color = RED; // offroad + if (is_active_agent && agent->metrics_array[RED_LIGHT_IDX] > 0) + wire_color = YELLOW; // red light violation rlSetLineWidth(2.0f); for (int j = 0; j < 4; j++) { DrawLine3D(corners[j], corners[(j + 1) % 4], wire_color); diff --git a/pufferlib/ocean/drive/visualize.c b/pufferlib/ocean/drive/visualize.c index 739f1a6a88..616d9c8e92 100644 --- a/pufferlib/ocean/drive/visualize.c +++ b/pufferlib/ocean/drive/visualize.c @@ -74,10 +74,10 @@ void renderTopDownView(Drive *env, Client *client, int map_height, int obs, int // Top-down orthographic camera Camera3D camera = {0}; - camera.position = (Vector3){0.0f, 0.0f, 500.0f}; // above the scene - camera.target = (Vector3){0.0f, 0.0f, 0.0f}; // look at origin + camera.position = (Vector3){env->grid_map->top_left_x, env->grid_map->bottom_right_y, 500.0f}; + camera.target = (Vector3){env->grid_map->top_left_x, env->grid_map->bottom_right_y, 0.0f}; camera.up = (Vector3){0.0f, -1.0f, 0.0f}; - camera.fovy = map_height; + camera.fovy = 2 * map_height; camera.projection = CAMERA_ORTHOGRAPHIC; client->width = img_width; @@ -192,10 +192,15 @@ int eval_gif(const char *map_name, const char *policy_name, int show_grid, int o // Parse configuration from INI file env_init_config conf = {0}; // Initialize to zero const char *ini_file = "pufferlib/config/ocean/drive.ini"; - if (load_env_config(ini_file, &conf) < 0) { - fprintf(stderr, "Error: Could not load %s. Cannot determine environment configuration.\n", ini_file); + int ini_result = load_env_config(ini_file, &conf); + if (ini_result != 0) { + fprintf(stderr, "Error: Could not load %s (ini_parse returned %d).\n", ini_file, ini_result); return -1; } + printf("Config: max_partner=%d max_lane=%d max_boundary=%d max_tl=%d target_type=%d num_waypoints=%d\n", + conf.max_partner_observations, conf.max_lane_segment_observations, + conf.max_boundary_segment_observations, conf.max_traffic_light_observations, + conf.target_type, conf.num_target_waypoints); char map_buffer[100]; if (map_name == NULL) { @@ -224,24 +229,44 @@ int eval_gif(const char *map_name, const char *policy_name, int show_grid, int o Drive env = { .dynamics_model = conf.dynamics_model, + .action_type = conf.action_type, .reward_vehicle_collision = conf.reward_vehicle_collision, .reward_offroad_collision = conf.reward_offroad_collision, .reward_ade = conf.reward_ade, + .reward_comfort = conf.reward_comfort, + .reward_lane_align = conf.reward_lane_align, + .reward_lane_center = conf.reward_lane_center, + .reward_velocity = conf.reward_velocity, + .reward_reverse = conf.reward_reverse, + .reward_traffic_light_violation = conf.reward_traffic_light_violation, + .reward_timestep = conf.reward_timestep, + .reward_overspeed = conf.reward_overspeed, + .reward_vel_align = conf.reward_vel_align, + .reward_center_bias = conf.reward_center_bias, .goal_radius = conf.goal_radius, .dt = conf.dt, .map_name = (char *)map_name, - .init_steps = init_steps, - .num_controllable_agents = max_controlled_agents, + .init_steps = (init_steps >= 0) ? init_steps : conf.init_steps, + .num_controllable_agents = (max_controlled_agents > 0) ? max_controlled_agents : conf.max_agents_per_env, .collision_behavior = conf.collision_behavior, .offroad_behavior = conf.offroad_behavior, .traffic_light_behavior = conf.traffic_light_behavior, .compute_eval_metrics = conf.compute_eval_metrics, - .init_mode = init_mode, - .control_mode = control_mode, + .reward_randomization = conf.reward_randomization, + .reward_conditioning = conf.reward_conditioning, + .init_mode = (init_mode >= 0) ? init_mode : conf.init_mode, + .control_mode = (control_mode >= 0) ? control_mode : conf.control_mode, .simulation_mode = conf.simulation_mode, .num_target_waypoints = conf.num_target_waypoints, .min_waypoint_spacing = conf.min_waypoint_spacing, .max_waypoint_spacing = conf.max_waypoint_spacing, + .target_type = conf.target_type, + .max_partner_observations = conf.max_partner_observations, + .max_lane_segment_observations = conf.max_lane_segment_observations, + .max_boundary_segment_observations = conf.max_boundary_segment_observations, + .max_traffic_light_observations = conf.max_traffic_light_observations, + .max_stop_sign_observations = conf.max_stop_sign_observations, + .termination_mode = conf.termination_mode, }; env.scenario_length = (scenario_length_override > 0) ? scenario_length_override @@ -264,7 +289,10 @@ int eval_gif(const char *map_name, const char *policy_name, int show_grid, int o float map_width = env.grid_map->bottom_right_x - env.grid_map->top_left_x; float map_height = env.grid_map->top_left_y - env.grid_map->bottom_right_y; - printf("Map size: %.1fx%.1f\n", map_width, map_height); + printf("Map size: %.1fx%.1f grid: top_left=(%.1f,%.1f) bottom_right=(%.1f,%.1f)\n", + map_width, map_height, + env.grid_map->top_left_x, env.grid_map->top_left_y, + env.grid_map->bottom_right_x, env.grid_map->bottom_right_y); float scale = 6.0f; // Can be used to increase the video quality // Calculate video width and height; round to nearest even number diff --git a/scripts/render_maps.py b/scripts/render_maps.py new file mode 100644 index 0000000000..3a80d1e172 --- /dev/null +++ b/scripts/render_maps.py @@ -0,0 +1,105 @@ +"""Render all maps showing road discretization: lanes, edges, and grid cells.""" +import os +import shutil +import tempfile +import matplotlib +matplotlib.use('Agg') +import matplotlib.pyplot as plt +import numpy as np +from collections import Counter +from pufferlib.ocean.drive import drive + +map_dir = "pufferlib/resources/drive/binaries/carla" +maps = sorted(f for f in os.listdir(map_dir) if f.endswith(".bin")) + +for map_file in maps: + map_path = os.path.join(map_dir, map_file) + map_name = map_file.replace(".bin", "") + print(f"Rendering {map_name}...") + + tmpdir = tempfile.mkdtemp() + shutil.copy(map_path, os.path.join(tmpdir, map_file)) + + try: + env = drive.Drive( + num_agents=32, + num_maps=1, + map_dir=tmpdir, + dynamics_model="jerk", + simulation_mode="gigaflow", + min_agents_per_env=1, + max_agents_per_env=32, + scenario_length=1024, + ) + env.reset() + state = env.get_state() + if isinstance(state, list): + state = state[0] + + road_elements = state.get("road_elements", []) + type_counts = Counter() + for elem in road_elements: + type_counts[elem.get("type", -1)] += 1 + print(f" Road element types: {dict(type_counts)}") + + fig, ax = plt.subplots(1, 1, figsize=(20, 20)) + ax.set_facecolor('#1a1a1a') + ax.set_aspect('equal') + ax.set_title(f"{map_name} — Road Discretization", fontsize=16, color='white') + + # Color by type + type_colors = { + # Lanes (drivable) + 1: ('green', 'Drivable Lane', 0.6), + 2: ('limegreen', 'Lane Type 2', 0.6), + 3: ('darkgreen', 'Lane Type 3', 0.6), + # Lines + 11: ('yellow', 'Road Line', 0.4), + 12: ('gold', 'Line Type 12', 0.4), + 13: ('orange', 'Line Type 13', 0.4), + 14: ('darkorange', 'Line Type 14', 0.4), + 15: ('khaki', 'Line Type 15', 0.4), + 16: ('wheat', 'Line Type 16', 0.4), + 17: ('lightyellow', 'Line Type 17', 0.4), + 18: ('palegoldenrod', 'Line Type 18', 0.4), + # Edges + 21: ('red', 'Road Edge', 0.8), + 22: ('orangered', 'Edge Type 22', 0.8), + 23: ('darkred', 'Edge Type 23', 0.8), + } + + plotted_types = set() + for elem in road_elements: + x = elem.get("x", []) + y = elem.get("y", []) + t = elem.get("type", 0) + if not x or not y: + continue + + color, label, lw = type_colors.get(t, ('gray', f'Type {t}', 0.3)) + show_label = t not in plotted_types + plotted_types.add(t) + ax.plot(x, y, color=color, linewidth=lw, alpha=0.7, + label=label if show_label else None) + + ax.legend(loc='upper right', fontsize=10, facecolor='#333', labelcolor='white') + ax.tick_params(colors='white') + for spine in ax.spines.values(): + spine.set_color('white') + + out_path = f"/tmp/map_{map_name}_discretized.png" + fig.savefig(out_path, dpi=150, bbox_inches='tight', facecolor='#1a1a1a') + plt.close(fig) + print(f" Saved to {out_path}") + env.close() + except Exception as e: + import traceback + traceback.print_exc() + print(f" ERROR: {e}") + finally: + shutil.rmtree(tmpdir) + +print("\nDone! Opening all maps...") +for map_file in maps: + map_name = map_file.replace(".bin", "") + os.system(f"open /tmp/map_{map_name}_discretized.png") From a4a1cee0ba4e7bfa357e26642fa6d9467d728e63 Mon Sep 17 00:00:00 2001 From: Eugene Vinitsky Date: Tue, 31 Mar 2026 23:24:26 -0400 Subject: [PATCH 19/24] Fix ini parser: return 1 (skip) for unknown sections instead of 0 (error) ini_parse treats return 0 as parse error. The handler was returning 0 for non-[env] sections like [base], [train], causing all config values to be zero. This made observations 18 floats instead of 874, causing heap-buffer-overflow in drivenet forward pass. Co-Authored-By: Claude Opus 4.6 (1M context) --- pufferlib/ocean/env_config.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pufferlib/ocean/env_config.h b/pufferlib/ocean/env_config.h index a709aec316..6de38ff707 100644 --- a/pufferlib/ocean/env_config.h +++ b/pufferlib/ocean/env_config.h @@ -182,7 +182,7 @@ static int handler(void *config, const char *section, const char *name, const ch } else if (MATCH("env", "max_stop_sign_observations")) { env_config->max_stop_sign_observations = atoi(value); } else { - return 0; // Unknown section/name, indicate failure to handle + return 1; // Unknown section/name, skip silently } #undef MATCH From 897997b963d0c04444509959a44ec920d580c812 Mon Sep 17 00:00:00 2001 From: Eugene Vinitsky Date: Tue, 31 Mar 2026 23:30:05 -0400 Subject: [PATCH 20/24] Warn on unknown keys in [env] section, skip other sections silently Co-Authored-By: Claude Opus 4.6 (1M context) --- pufferlib/ocean/env_config.h | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/pufferlib/ocean/env_config.h b/pufferlib/ocean/env_config.h index 6de38ff707..dc05fa9dc6 100644 --- a/pufferlib/ocean/env_config.h +++ b/pufferlib/ocean/env_config.h @@ -182,7 +182,10 @@ static int handler(void *config, const char *section, const char *name, const ch } else if (MATCH("env", "max_stop_sign_observations")) { env_config->max_stop_sign_observations = atoi(value); } else { - return 1; // Unknown section/name, skip silently + if (strcmp(section, "env") == 0) { + printf("Warning: unknown key '%s' in [env] section\n", name); + } + return 1; // Skip keys in non-env sections silently } #undef MATCH From 5104a9d246a196d377ced645025eeefcf81cb4de Mon Sep 17 00:00:00 2001 From: Eugene Vinitsky Date: Tue, 31 Mar 2026 23:50:14 -0400 Subject: [PATCH 21/24] Build visualize with fast mode (no ASAN), fix ini parser for non-env sections Co-Authored-By: Claude Opus 4.6 (1M context) --- pufferlib/pufferl.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pufferlib/pufferl.py b/pufferlib/pufferl.py index 5f9c687c8f..343bb74ee2 100644 --- a/pufferlib/pufferl.py +++ b/pufferlib/pufferl.py @@ -2372,7 +2372,7 @@ def ensure_drive_binary(): print("Building visualize binary...") try: result = subprocess.run( - ["bash", "scripts/build_ocean.sh", "visualize", "local"], capture_output=True, text=True, timeout=300 + ["bash", "scripts/build_ocean.sh", "visualize", "fast"], capture_output=True, text=True, timeout=300 ) if result.returncode == 0: From 6e40e7bc0bef6363bc83d1e58f3600f91245bfc7 Mon Sep 17 00:00:00 2001 From: Eugene Vinitsky Date: Wed, 1 Apr 2026 00:30:31 -0400 Subject: [PATCH 22/24] Increase render timeout from 120s to 600s 120s was too short for rendering 1024 frames with 32 agents, causing truncated videos (moov atom not found). Co-Authored-By: Claude Opus 4.6 (1M context) --- pufferlib/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pufferlib/utils.py b/pufferlib/utils.py index fc09cf7703..7a5efbe5a9 100644 --- a/pufferlib/utils.py +++ b/pufferlib/utils.py @@ -275,7 +275,7 @@ def render_videos(config, vecenv, logger, epoch, global_step, bin_path): cmd.extend(["--output-topdown", topdown_tmp]) cmd.extend(["--output-agent", agent_tmp]) - result = subprocess.run(cmd, cwd=os.getcwd(), capture_output=True, text=True, timeout=120, env=env_vars) + result = subprocess.run(cmd, cwd=os.getcwd(), capture_output=True, text=True, timeout=600, env=env_vars) vids_exist = os.path.exists(topdown_tmp) and os.path.exists(agent_tmp) From e84c9a63f6e86acb8e98948e8dc8ed1a9e6865ec Mon Sep 17 00:00:00 2001 From: Eugene Vinitsky Date: Wed, 1 Apr 2026 05:33:03 -0400 Subject: [PATCH 23/24] Check if video files exist regardless of exit code for wandb logging Visualize can exit non-zero (ASAN, signals) but still produce valid videos. Co-Authored-By: Claude Opus 4.6 (1M context) --- pufferlib/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pufferlib/utils.py b/pufferlib/utils.py index 7a5efbe5a9..ab93b46204 100644 --- a/pufferlib/utils.py +++ b/pufferlib/utils.py @@ -284,7 +284,7 @@ def render_videos(config, vecenv, logger, epoch, global_step, bin_path): if not vids_exist and result.stdout: print(f"Visualize stdout: {result.stdout[:500]}") - if result.returncode == 0 or (result.returncode == 1 and vids_exist): + if vids_exist: videos = [ ( topdown_tmp, From c3f30823f259b933d5dbb5006d3cb0b7d2eaa2b4 Mon Sep 17 00:00:00 2001 From: Eugene Vinitsky Date: Wed, 1 Apr 2026 05:35:47 -0400 Subject: [PATCH 24/24] Always log visualize exit code and output on non-zero return Co-Authored-By: Claude Opus 4.6 (1M context) --- pufferlib/utils.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/pufferlib/utils.py b/pufferlib/utils.py index ab93b46204..30316f0eb9 100644 --- a/pufferlib/utils.py +++ b/pufferlib/utils.py @@ -284,6 +284,13 @@ def render_videos(config, vecenv, logger, epoch, global_step, bin_path): if not vids_exist and result.stdout: print(f"Visualize stdout: {result.stdout[:500]}") + if result.returncode != 0: + print(f"Visualize exited with code {result.returncode} (map index {i})") + if result.stdout: + print(f" stdout: {result.stdout[:500]}") + if result.stderr: + print(f" stderr: {result.stderr[:500]}") + if vids_exist: videos = [ (

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