CleanRL is a collection of single-file implementations of deep reinforcement learning algorithms designed for research and experimentation.
The project addresses the challenge of understanding and modifying reinforcement learning algorithms by implementing each one as a standalone, self-contained file rather than as part of a larger framework. This approach makes it straightforward to read through an entire algorithm implementation, understand how all components fit together, and make targeted modifications for research purposes. The implementations cover a range of algorithms including PPO, DQN, C51, DDPG, TD3, SAC, and PPG, each optimized for clarity and reproducibility.
Researchers and practitioners who need to understand algorithm internals or experiment with modifications will find this structure more accessible than navigating a complex framework. The single-file design is particularly suited to projects where you need to fork or adapt an algorithm rather than use it as a black box. This approach trades the convenience of a unified API and shared utilities for transparency and ease of customization. The project integrates with Weights and Biases for experiment tracking, supporting the research workflow of logging and comparing runs.
Development activity shows consistent engagement with the codebase through regular updates and refinements to implementations. The project maintains responsiveness to issues and pull requests, indicating active maintenance. Documentation is provided through a dedicated site, suggesting investment in making the implementations accessible to new users. The maintainers demonstrate attention to keeping implementations aligned with research standards and best practices in the reinforcement learning community.
