stable-baselines3 and stable-baselines3-contrib
The contrib package extends the main library with experimental RL algorithms and features, making them complements designed to be used together rather than alternatives.
About stable-baselines3
DLR-RM/stable-baselines3
PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
Implements canonical on-policy (PPO, A2C) and off-policy (DQN, SAC, TD3) algorithms with a unified sklearn-like API, supporting Dict observation spaces and custom policies via modular network architectures. Integrates with Gymnasium for environment interaction, TensorBoard for experiment tracking, and Weights & Biases/Hugging Face for model management and sharing. Includes companion tools like RL Zoo for hyperparameter tuning and SB3-Contrib for experimental features (masked action support, recurrent policies).
About stable-baselines3-contrib
Stable-Baselines-Team/stable-baselines3-contrib
Contrib package for Stable-Baselines3 - Experimental reinforcement learning (RL) code
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