🚀 Imitation Learning on LunarLander

📌 Overview

This project explores Imitation Learning in the LunarLander environment from Gymnasium.
Instead of learning through trial-and-error like traditional Reinforcement Learning, the agent learns to act by mimicking expert behavior using supervised learning techniques.

The goal is to train a model that can replicate expert decisions and successfully land the spacecraft.

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🧠 Key Idea

Imitation Learning bridges the gap between:

• Supervised Learning (learning from labeled data)
• Reinforcement Learning (learning from rewards)

In this project, we apply Behavior Cloning, where the model learns a direct mapping: state -> action.

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🎮 Environment

• Environment: LunarLander (Gymnasium)
• State Space: 8-dimensional continuous vector
• Action Space: 4 discrete actions:
  • Do nothing
  • Fire left engine
  • Fire main engine
  • Fire right engine

The objective is to land safely between the flags with minimal velocity and correct orientation.

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⚙️ Methodology

1. Data Collection

• Generated expert trajectories using a heuristic / rule-based policy
• Collected (state, action) pairs

2. Behavior Cloning

• Trained a neural network using supervised learning
• Input: state vector
• Output: predicted action

3. Model Training

• Loss function: Cross-Entropy
• Optimization: Gradient-based optimization
• Goal: Minimize difference between predicted and expert actions

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📊 Results

• The trained agent is able to imitate expert-like behavior
• Successfully performs controlled descent in many scenarios
• Demonstrates stable policy learning without reward-based training

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📁 Project Structure

├── notebook.ipynb # Main training and evaluation notebook ├── data/ # Collected expert data (if exists) ├── models/ # Saved models (optional) └── README.md

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▶️ How to Run

1. Open the notebook in Google Colab / Jupyter
2. Run all cells
3. The agent will:
   • Train on expert data
   • Evaluate performance in the environment

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🛠️ Technologies

• Python
• TensorFlow / Keras
• Gymnasium
• NumPy
• Matplotlib

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💡 Key Takeaways

• Imitation Learning can simplify RL problems by removing the need for reward design
• Behavior Cloning is simple but sensitive to distribution shift
• Demonstrates how supervised learning can be applied to sequential decision-making

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✨ Future Improvements

• Implement DAgger for better generalization
• Compare with RL methods (DQN / Policy Gradient)
• Improve robustness to unseen states