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LearningWithSimpleModels

LearningWithSimpleModels

Reference implementation and some experiments from our paper "Enabling Efficient, Reliable Real-World Reinforcement Learning with Approximate Physics-Based Models".

@inproceedings{westenbroek2023feedback,
  title={Enabling Efficient, Reliable Real-World Reinforcement Learning with Approximate Physics-Based Models},
  author={Westenbroek, Tyler and Levy, Jacob and Fridovich-Keil, David},
  booktitle={7th Annual Conference on Robot Learning},
  year={2023}
}

Installation

First, clone the repository and start Julia.

git clone https://github.com/CLeARoboticsLab/LearningWithSimpleModels.jl.git
cd LearningWithSimpleModels.jl
julia

Next, activate the project and install the dependencies.

using Pkg
Pkg.activate(".")
Pkg.instantiate()

Experiments

Car, Simulated

To run a simulated version of the car experiment, run the below commands from the Julia REPL. The results can be found in the .data directory.

include("experiments/unicycle/unicycle_experiment.jl")
train_unicycle_experiment(); evaluate_unicycle_experiment()

To run the car mismatch study found in the appendix, run the below commands from the Julia REPL. The results can be found in the .data/results/mismatch_study/ directory.

include("experiments/unicycle/unicycle_experiment.jl")
include("experiments/unicycle/mismatch_study.jl")
mismatch_study()
plot_mismatch_study()
mismatch_car_plots()

Quadruped, Simulated

To run a simulated version of the quadruped experiment (where the quadruped is only modeled as a unicycle), run the below commands from the Julia REPL. The results can be found in the .data directory.

using Pkg
Pkg.activate(".")
using LearningWithSimpleModels
push!(LOAD_PATH, joinpath(pwd(), "experiments/quadruped_simulated/src"))
Pkg.activate("experiments/quadruped_simulated/")
Pkg.develop(path=".")
Pkg.instantiate()
using QuadrupedSimulatedExperiment
QuadrupedSimulatedExperiment.run()

Double Pendulum

To run the Double Pendulum experiment found in the appendix, start by importing the subpackage by running the below commands from the Julia REPL.

using Pkg
Pkg.activate(".")
using LearningWithSimpleModels
push!(LOAD_PATH, joinpath(pwd(), "experiments/double_pendulum/src"))
Pkg.activate("experiments/double_pendulum/")
Pkg.develop(path=".")
Pkg.instantiate()
using DoublePendulumExperiment

To perform a single training run, for both the closed-loop and open-loop cases, run the below. The results can be found in the .data directory.

DoublePendulumExperiment.run()
DoublePendulumExperiment.run_open_loop()

To perform training over 64 random seeds, use the following command. The results can be found in the .data/results_pend/ directory.

DoublePendulumExperiment.generate_comparison_data()
DoublePendulumExperiment.final_variances_plot()
DoublePendulumExperiment.final_variances_plot_mismatch()