RLX is a collection of Reinforcement Learning algorithms implemented based on the implementations from CleanRL in MLX, Apple's new Machine Learning framework. This project aims to leverage the unified memory capabilities of Apple's M series chips to enhance the performance and efficiency of these algorithms.

• Python 3.9 or later
• Poetry for dependency management
• An Apple device with an M-series chip

Clone the repository to your local machine:

git clone https://github.com/noahfarr/rlx.git
cd rlx

Install dependencies using Poetry:

poetry install

The project is organized into directories by algorithm. Each directory contains the implementation of a specific Reinforcement Learning algorithm, making the project modular and scalable. Here's an overview:

• alg1/: Implementation of Algorithm 1
• alg2/: Implementation of Algorithm 2 ...

To run a specific algorithm, navigate to its directory and execute the main script. For example:

cd alg1
poetry run python main.py

Replace alg1 with the directory of the algorithm you wish to run.

Contributions to RLX are welcome. To contribute, please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature (git checkout -b feature/AmazingFeature).
3. Commit your changes (git commit -m 'Add some AmazingFeature').
4. Push to the branch (git push origin feature/AmazingFeature).
5. Open a pull request.

This project is licensed under the MIT License - see the LICENSE file for details.

Special thanks to the MLX team for providing the framework. This project is designed to run optimally on Apple's M series chips.