{"id":28851813,"url":"https://github.com/sadegh-khedry/lunar-lander-using-ppo","last_synced_at":"2026-05-09T07:38:22.585Z","repository":{"id":244797884,"uuid":"816226753","full_name":"Sadegh-Khedry/Lunar-Lander-Using-PPO","owner":"Sadegh-Khedry","description":"Training an agent to land a spacecraft in the LunarLander environment.","archived":false,"fork":false,"pushed_at":"2024-08-23T10:58:45.000Z","size":538,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-06-06T20:00:59.038Z","etag":null,"topics":["gymnasium","jupyter-notebook","lunar-lander","ppo","python","reinforcement-learning","reinforcement-learning-algorithms","stable-baselines3"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Lunar Lander Using Proximal Policy Optimization (PPO)\r\n\r\nThis repository contains code for training and evaluating a Lunar Lander agent using the Proximal Policy Optimization (PPO) algorithm.\r\n\r\n\r\n## Table of Contents\r\n\r\n\r\n- [Directory Structure](#directory-structure)\r\n- [Files and Functions](#files-and-functions)\r\n- [Model Performance](#model-performance)\r\n- [Installation Guide](#installation-guide)\r\n- [Further Improvements](#further-improvements)\r\n- [Acknowledgments](#acknowledgments)\r\n- [License](#license)\r\n\r\n\r\n## Directory Structure\r\n```\r\n├── src\r\n│ ├── utils.py\r\n│ ├── lunar_lander_training.py\r\n│ └── lunar_lander_evaluation.py\r\n├── notebooks\r\n│ ├── lunar_lander_training.ipynb\r\n│ └── lunar_lander_evaluation.ipynb\r\n├── environment.yml\r\n└── README.md\r\n```\r\n\r\n\r\n## Files and Functions\r\n\r\n- `utils.py` : Utility functions for various tasks.\r\n- `lunar_lander_training.py` : Functions for training the lunar lander.\r\n- `lunar_lander_evaluation.py` : Functions for evaluating the lunar lander.\r\n- `lunar_lander_training.ipynb`: Notebook for lunar lander training.\r\n- `lunar_lander_evaluation.ipynb`: Notebook for lunar lander evaluation.\r\n\r\n\r\n## Model Performance\r\n\r\nThis section will be added\r\n  \r\n## Installation Guide\r\n\r\nTo set up the project environment, use the `environment.yml` file to create a conda environment.\r\n\r\n1. **Clone the repository:**\r\n\r\n    ```bash\r\n    git clone https://github.com/sadegh15khedry/Lunar-Lander-Using-PPO.git\r\n    cd Lunar-Lander-Using-PPO\r\n    ```\r\n\r\n2. **Create the conda environment:**\r\n\r\n    ```bash\r\n    conda env create -f environment.yml\r\n    ```\r\n\r\n3. **Activate the conda environment:**\r\n\r\n    ```bash\r\n    conda activate lunar-lander\r\n    ```\r\n\r\n4. **Verify the installation:**\r\n\r\n    ```bash\r\n    python --version\r\n    ```\r\n\r\n## Further Improvements\r\n\r\n- Tune hyperparameters for better performance.\r\n- Experiment with different algorithms available in Stable Baselines3.\r\n- Implement additional evaluation metrics and visualizations.\r\n\r\n\r\n## Acknowledgment\r\n\r\nThis 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\u0026t=551s .\r\n\r\n## License\r\n\r\nThis project is licensed under the Apache-2.0 License - see the LICENSE.md file for details.\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsadegh-khedry%2Flunar-lander-using-ppo","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsadegh-khedry%2Flunar-lander-using-ppo","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsadegh-khedry%2Flunar-lander-using-ppo/lists"}