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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:

    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},
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