CleanRL

Overview

CleanRL is a Deep Reinforcement Learning library that provides high-quality single-file implementation with research-friendly features. The implementation is clean and simple, yet we can scale it to run thousands of experiments using AWS Batch. The highlight features of CleanRL are:

Single-file Implementation
- Every detail about an algorithm is put into the algorithm's own file. Therefore, it's easier for you to fully understand an algorithm and do research with it.
Benchmarked Implementation on 7+ algorithms and 34+ games
Tensorboard Logging
Local Reproducibility via Seeding
Videos of Gameplay Capturing
Experiment Management with Weights and Biases
Cloud Integration with Docker and AWS

You can read more about CleanRL in our technical paper and documentation.

Good luck have fun 🚀

Citing CleanRL

If you use CleanRL in your work, please cite our technical paper:

@article{huang2021cleanrl,
    title={CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning Algorithms}, 
    author={Shengyi Huang and Rousslan Fernand Julien Dossa and Chang Ye and Jeff Braga},
    year={2021},
    eprint={2111.08819},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}