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

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License

MIT License

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CleanRL Web Site

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