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https://github.com/sadegh15khedry/lunar-lander-using-ppo
Training an agent to land a spacecraft in the LunarLander environment.
https://github.com/sadegh15khedry/lunar-lander-using-ppo
gymnasium jupyter-notebook lunar-lander ppo python reinforcement-learning reinforcement-learning-algorithms stable-baselines3
Last synced: 3 days ago
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Training an agent to land a spacecraft in the LunarLander environment.
- Host: GitHub
- URL: https://github.com/sadegh15khedry/lunar-lander-using-ppo
- Owner: sadegh15khedry
- License: apache-2.0
- Created: 2024-06-17T09:53:27.000Z (5 months ago)
- Default Branch: master
- Last Pushed: 2024-08-23T10:58:45.000Z (3 months ago)
- Last Synced: 2024-08-23T12:05:17.674Z (3 months ago)
- Topics: gymnasium, jupyter-notebook, lunar-lander, ppo, python, reinforcement-learning, reinforcement-learning-algorithms, stable-baselines3
- Language: Jupyter Notebook
- Homepage:
- Size: 525 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 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](#directory-structure)
- [Files and Functions](#files-and-functions)
- [Model Performance](#model-performance)
- [Installation Guide](#installation-guide)
- [Further Improvements](#further-improvements)
- [Acknowledgments](#acknowledgments)
- [License](#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:**
```bash
git clone https://github.com/sadegh15khedry/Lunar-Lander-Using-PPO.git
cd Lunar-Lander-Using-PPO
```
2. **Create the conda environment:**
```bash
conda env create -f environment.yml
```
3. **Activate the conda environment:**
```bash
conda activate lunar-lander
```
4. **Verify the installation:**
```bash
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.