stable-baselines3
Trains and evaluates single-agent reinforcement learning with Stable Baselines3 (PPO, SAC, DQN, TD3, DDPG, A2C), Gymnasium custom environments, vectorized rollouts, callbacks, and checkpoint normalization. Applies to reproducible RL experiments, continuous control, discrete actions, and SB3-Contrib recurrent or masked policies.
- Version
- 2.0
- License
- MIT license
- Compatibility
- Requires Python 3.10+, PyTorch >= 2.8, and stable-baselines3 2.9.0. Gymnasium environments; optional extras for TensorBoard and Atari (ale-py).
Pinned to revision 68105dd992f1, so it is the text this page describes rather than whatever the author pushed since.
Pre-approved tools experimental
Experimental field. Support varies between clients, so this list is what the author declared, not what your client will enforce.
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- Bash
Files
- skills/stable-baselines3/SKILL.md
- skills/stable-baselines3/SKILL_CN.md
- skills/stable-baselines3/references/algorithms.md
- skills/stable-baselines3/references/callbacks.md
- skills/stable-baselines3/references/custom_environments.md
- skills/stable-baselines3/references/vectorized_envs.md
- skills/stable-baselines3/scripts/custom_env_template.py
- skills/stable-baselines3/scripts/evaluate_agent.py
- skills/stable-baselines3/scripts/train_rl_agent.py
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