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. CleanRL is not a modular library and therefore it is not meant to be imported. At the cost of duplicate code, we make all implementation details of a DRL algorithm variant easy to understand, so CleanRL comes with its own pros and cons. You should consider using CleanRL if you want to 1) understand all implementation details of an algorithm's variant or 2) prototype advanced features that other modular DRL libraries do not support (CleanRL has minimal lines of code so it gives you great debugging experience and you don't have to do a lot of subclassing like sometimes in modular DRL libraries).

Features

  • Every detail about an algorithm variant is put into a single standalone file
  • Single-file implementation
  • Benchmarked Implementation, 7+ algorithms and 34+ games
  • Tensorboard Logging
  • Local Reproducibility via Seeding
  • Videos of Gameplay Capturing

Project Samples

Project Activity

See All Activity >

License

MIT License

Follow CleanRL

CleanRL Web Site

Other Useful Business Software
$300 Free Credits for Your Google Cloud Projects Icon
$300 Free Credits for Your Google Cloud Projects

Start building on Google Cloud with $300 in free credits. No commitment, no credit card required until you're ready to scale.

Launch your next project with $300 in free Google Cloud credits—no strings attached. Test, build, and deploy without risk. Use your credits across the entire Google Cloud platform to find what works best for your needs. After your credits are used, continue with always-free tier services. Only pay when you're ready to scale. Sign up in minutes and start exploring.
Start Free Trial
Rate This Project
Login To Rate This Project

User Reviews

Be the first to post a review of CleanRL!