Introduction Welcome to the Moon Lander project! This project implements a Moon Lander using the Q-Learning algorithm, allowing the lander to autonomously navigate and land on the lunar surface.
Overview The Moon Lander is a classic reinforcement learning problem where the goal is to safely land a spacecraft on the surface of the moon. In this project, we utilize Q-Learning, a popular reinforcement learning technique, to train the lander to make optimal decisions and achieve successful landings.
Features
- Implementation of Q-Learning algorithm.
- Simulation environment for the Moon Lander.
- Autonomous navigation and landing capabilities.
Installation To run the Moon Lander project, you need Python installed on your system. You also need to install the necessary dependencies. You can do this using pip:
Usage
- Run the main script to start the Moon Lander simulation:
- Follow the on-screen instructions to interact with the simulation.
License This project is licensed under the MIT License. See the LICENSE file for details.
Contributing Contributions are welcome! If you want to contribute to this project, please fork the repository and submit a pull request with your changes.
Acknowledgments
- This project was inspired by the classic Moon Lander problem.
- Special thanks to the OpenAI Gym environment for reinforcement learning simulations.
Contact For any questions, suggestions, or issues, feel free to contact the project maintainer: