My practice with Reinforcement Learning on various environments in the Gymnasium RL Library. Reach out with any issues, or if you need help with ML implementations.
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Gymnasium is a common library for Reinforcement Learning training and development. Since my main interests are in AI and ML, the Gymnasium environments were a perfect opportunity to practice implementing these algorithms for different problems. The environments implemented in this repository are representative of many multi-state problems with varying observation and action spaces for the game Agent. Check out the code and let me know if you find any issues. If you just want more help with AI/ML, I'll be happy to communicate with you.
Each folder with a relevant environment contains an image of example results in episode length, error, and rewards. Use those as comparisons for your results. Implementations may require additional blocks of code to make sure that all dependencies are met. These include Numpy, TQDM, MatPlotLib.pyplot, Gymnasium, PyGame, Moviepy, and Seaborn. Make sure you check your versions!
For more examples, please refer to the Documentation
Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Distributed under the MIT License. See LICENSE.txt for more information.
Luke Howard - lhhoward.125@gmail.com