All Tools
S
AgentsFreeOpen Source
STABLE BASELINES3
Reliable PyTorch implementations of reinforcement learning algorithms
MIT
ABOUT
Research RL code is often brittle, undocumented, and hard to reproduce, so teams rewrite PPO or SAC from scratch. Stable Baselines3 ships battle-tested algorithm implementations with a consistent API, logging, and Gymnasium support so you can train, evaluate, and ship policies without reimplementing the stack.
INSTALL
pip install stable-baselines3INTEGRATION GUIDE
1. Train a PPO or SAC policy on a Gymnasium robotics or control environment
2. Fine-tune a DQN agent on Atari with callbacks for checkpoints and evaluation
3. Prototype a custom reward or observation wrapper on top of a documented SB3 algorithm
4. Compare baseline RL algorithms on the same env before writing a custom trainer
TAGS
pythonpytorchreinforcement-learningpposacbaselinesgymnasium