Stable-Baselines3 (SB3) provides reliable, PyTorch-based implementations of state-of-the-art reinforcement learning algorithms. It targets researchers and industry professionals seeking robust baselines for replication, refinement, and new idea development, offering a user-friendly interface for both beginners and experienced RL practitioners.

How It Works

SB3 offers a common, scikit-learn-like interface for various RL algorithms, abstracting away implementation complexities. It emphasizes code quality, documentation, and compatibility with custom environments and policies, facilitating easier experimentation and comparison of RL approaches.

Quick Start & Requirements

• Install with pip install 'stable-baselines3[extra]' (includes Tensorboard, OpenCV, etc.) or pip install stable-baselines3.
• Requires Python 3.9+ and PyTorch >= 2.3.
• Documentation: https://stable-baselines3.readthedocs.io/
• Colab Notebooks available for online execution.

Highlighted Details

• Implements a wide range of RL algorithms including A2C, PPO, SAC, TD3, DQN, and more.
• Supports various observation and action spaces (Box, Discrete, MultiDiscrete, MultiBinary, Dict).
• Integrates with Weights & Biases for experiment tracking and Hugging Face for model sharing.
• Offers companion repositories for extended features (SB3-Contrib), faster Jax variants (SBX), and a training framework (RL Baselines3 Zoo).

Maintenance & Community

• Actively maintained by a core team, with development focused on bug fixes and documentation.
• Community support via Discord, Reddit, and Stack Overflow.
• Contribution guide available in CONTRIBUTING.md.

Licensing & Compatibility

• MIT License. Permissive for commercial use and closed-source linking.

Limitations & Caveats

The core SB3 library is stable, with newer algorithms and experimental features available in the separate SB3-Contrib repository. The project explicitly states they do not provide technical support or consulting via email.