vwxyzjn

    vwxyzjn/cleanrl

    High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)

    10.6K stars
    1.2K forks
    10.6K GitHub watchers
    Updated Nov 2, 2026
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    Where it stands

    Rank, growth pace and release cadence relative to everything GStars tracks.

    The project lists 44 contributors on GitHub.

    Gained 20% fewer stars in the last 90 days than in the 90 days before.

    Use Cases & Benefits

    • Implements high-quality single-file Deep Reinforcement Learning algorithms with research-friendly features in Python.
    • Offers clean, minimal codebases that enable easy understanding and debugging of complex RL algorithms without modular overhead.
    • Use for learning and prototyping advanced RL algorithms with full implementation details in a compact format.
    • Use for benchmarking and comparing RL algorithms across diverse environments with integrated experiment tracking and logging.
    • Use for scaling RL experiments on cloud platforms like AWS with support for reproducibility and gameplay video capture.

    Should you depend on it?

    License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.

    License

    Other

    GitHub could not identify this license; read the LICENSE file before depending on it.

    Community files

    • README: present
    • Contributing guide: present

    Watch-outs

    • License not identified by GitHub; read the LICENSE file before depending on it.
    • No commits in the last six months (last push Apr 2026).

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    From the README

    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:

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

    ℹ️ Support for Gymnasium: Farama-Foundation/Gymnasium is the next generation of openai/gym that will continue to be maintained and introduce new features. Please see their announcement for further detail. We are migrating to gymnasium and the progress can be tracked in vwxyzjn/cleanrl#277.

    ⚠️ NOTE: 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 do a lot of subclassing like sometimes in modular DRL libraries).

    Excerpt from the vwxyzjn/cleanrl README on GitHub (NOASSERTION license). © its authors.Read the full README on GitHub

    Daily snapshots

    Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots

    Forks

    1.2K+44

    Open issues

    110+0

    Compared with peers in Deep Learning

    Fastest-growing repositories GStars tracks in this cohort for the week of Nov 8, by 7-day star growth

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