Lunar Lander Using Proximal Policy Optimization (PPO)

This repository contains code for training and evaluating a Lunar Lander agent using the Proximal Policy Optimization (PPO) algorithm.

Table of Contents

• Directory Structure
• Files and Functions
• Model Performance
• Installation Guide
• Further Improvements
• Acknowledgments
• License

Directory Structure

├── src
│ ├── utils.py
│ ├── lunar_lander_training.py
│ └── lunar_lander_evaluation.py
├── notebooks
│ ├── lunar_lander_training.ipynb
│ └── lunar_lander_evaluation.ipynb
├── environment.yml
└── README.md

Files and Functions

• utils.py : Utility functions for various tasks.
• lunar_lander_training.py : Functions for training the lunar lander.
• lunar_lander_evaluation.py : Functions for evaluating the lunar lander.
• lunar_lander_training.ipynb: Notebook for lunar lander training.
• lunar_lander_evaluation.ipynb: Notebook for lunar lander evaluation.

Model Performance

This section will be added

Installation Guide

To set up the project environment, use the environment.yml file to create a conda environment.

1. Clone the repository:

   git clone https://github.com/sadegh15khedry/Lunar-Lander-Using-PPO.git
   cd Lunar-Lander-Using-PPO

2. Create the conda environment:

   conda env create -f environment.yml

3. Activate the conda environment:

   conda activate lunar-lander

4. Verify the installation:

   python --version

Further Improvements

• Tune hyperparameters for better performance.
• Experiment with different algorithms available in Stable Baselines3.
• Implement additional evaluation metrics and visualizations.

Acknowledgment

This project is based on the tutorial by Nicholas Renotte on training a Lunar Lander agent. You can find the tutorial here https://www.youtube.com/watch?v=nRHjymV2PX8&t=551s .

License

This project is licensed under the Apache-2.0 License - see the LICENSE.md file for details.