vwxyzjn/cleanrl
High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
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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).
General guidance from repository metadata, not legal advice. How we score
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/gymthat will continue to be maintained and introduce new features. Please see their announcement for further detail. We are migrating togymnasiumand 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).
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
| Repository | Stars | +7d | Health | Last release |
|---|---|---|---|---|
| rohitg00/ai-engineering-from-scratch | 78.3K | +1.6K | 75 | 1 day ago |
| SYSTRAN/faster-whisper | 26.5K | — | 40 | 368 days ago |
| GeeeekExplorer/nano-vllm | 16.3K | — | 12 | — |
| Lightning-AI/litgpt | 13.8K | — | 58 | 129 days ago |
| data-privacy-stack/presidio | 11.8K | — | 64 | 10 days ago |
| vwxyzjn/cleanrl | 10.6K | — | 36 | — |
| Category median | 16.3K | — | 58 | — |
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