DLR-RM/stable-baselines3
PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
1 release in the last 90 days, the latest 7 days ago, with 163 contributors on GitHub.
Gained 32% fewer stars in the last 90 days than in the 90 days before.
Use Cases & Benefits
- Provides reliable PyTorch implementations of state-of-the-art reinforcement learning algorithms for training and evaluating RL agents.
- Offers a stable, well-documented, and extensible framework with support for custom environments, policies, and multiprocess training.
- Use for developing and benchmarking reinforcement learning models in research or industry projects requiring reproducible baselines.
- Use for experimenting with advanced RL algorithms in robotics or control tasks using Gymnasium-compatible environments.
- Use for educational purposes to learn and prototype reinforcement learning techniques with clear APIs and extensive documentation.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
MIT
Permissive: commercial use, modification and redistribution are allowed with attribution.
Community files
- README: present
- Contributing guide: present
- Code of conduct: present
Watch-outs
No watch-outs from GStars' checks.
General guidance from repository metadata, not legal advice. How we score
From the README
Stable Baselines3 (SB3) is a set of reliable implementations of reinforcement learning algorithms in PyTorch. It is the next major version of Stable Baselines.
You can read a detailed presentation of Stable Baselines3 in the v1.0 blog post or our JMLR paper.
These algorithms will make it easier for the research community and industry to replicate, refine, and identify new ideas, and will create good baselines to build projects on top of. We expect these tools will be used as a base around which new ideas can be added, and as a tool for comparing a new approach against existing ones. We also hope that the simplicity of these tools will allow beginners to experiment with a more advanced toolset, without being buried in implementation details.
Note: Despite its simplicity of use, Stable Baselines3 (SB3) assumes you have some knowledge about Reinforcement Learning (RL). You should not utilize this library without some practice. To that extent, we provide good resources in the documentation to get started with RL.
The performance of each algorithm was tested (see Results section in their respective page), you can take a look at the issues #48 and #49 for more details.
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
Forks
2.2K+14
Open issues
89+3
Compared with peers in Machine 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 |
| OpenBMB/VoxCPM | 42.2K | +1.5K | 75 | 183 days ago |
| zeroclaw-labs/zeroclaw | 33.3K | +197 | 75 | 14 days ago |
| GeeeekExplorer/nano-vllm | 16.3K | — | 12 | — |
| DLR-RM/stable-baselines3 | 14.0K | — | 65 | 7 days ago |
| omnigent-ai/omnigent | 11.2K | — | 60 | 6 days ago |
| Category median | 33.3K | +1.5K | 75 | — |
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