Stable Baselines3
Stable Baselines3 (SB3) is a collection of reliable implementations of deep reinforcement learning algorithms built on PyTorch. It serves as the successor to Stable Baselines and provides a unified interface for training and comparing various reinforcement learning models. The library supports Gymnasium environments as its primary backend and includes vectorized environment support for efficient training. It is open-source and maintained with automated unit tests covering 95% of the codebase, ensuring robustness and reliability. SB3 also offers extensive documentation, examples, and Tensorboard integration for monitoring training progress. The project is actively maintained with releases supporting the latest Python versions and Gymnasium updates. It supports multiple observation space types such as Box, Discrete, MultiDiscrete, MultiBinary, and Dict spaces, though tuple observation spaces are not supported. The library is designed for developers and researchers working on reinforcement learning tasks in environments like Atari, PyBullet, or custom Gym/Gymnasium setups.
Stable Baselines3 is an open-source PyTorch library providing tested and documented implementations of reinforcement learning algorithms with support for Gymnasium environments.
Training Reinforcement Learning Agents
Developers and researchers can train RL agents on standard benchmarks like Atari or PyBullet using PyTorch implementations.
Algorithm Benchmarking and Comparison
Users can benchmark different RL algorithms under a unified interface to evaluate performance on custom or standard environments.
pip install stable-baselines3 to install the library.model = PPO("MlpPolicy", env, verbose=1).model.learn(total_timesteps=10000).model.save("path") and load it later using model = PPO.load("path").