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name: stable-baselines3 description: "Use Stable-Baselines3 for PyTorch reinforcement learning: train algorithms, validate Gymnasium environments, vectorize envs, customize policies, evaluate callbacks, and save/load SB3 models." disable-model-invocation: true metadata: disco-role: operating license: MIT

Stable-Baselines3

Use this skill when a task involves Stable-Baselines3 (SB3), the PyTorch reinforcement-learning library with sklearn-like RL algorithms for Gymnasium environments.

Install and Import Check

Stable-Baselines3 requires Python 3.10+ and PyTorch. For normal use:

pip install stable-baselines3
python - <<'PY'
import stable_baselines3 as sb3
print(sb3.__version__)
print(sb3.__all__)
PY

Use pip install 'stable-baselines3[extra]' only when the task needs optional rendering, TensorBoard/progress bars, Atari, plotting, or result-loading extras.

For a quick public API diagnostic, run scripts/sb3_doctor.py with --help first.

Route by Task

• Choose or train an algorithm: use training-and-algorithms for A2C/PPO/DQN/SAC/TD3/DDPG selection, constructor parameters, learn(), action-space compatibility, and tiny smoke training.
• Build, validate, or vectorize environments: use environments-and-vectorization for Gymnasium custom env APIs, check_env, make_vec_env, DummyVecEnv, SubprocVecEnv, Monitor, VecNormalize, and VecEnv API differences.
• Evaluate, log, checkpoint, or load models: use evaluation-and-persistence for evaluate_policy, callbacks, TensorBoard/logger behavior, save(), load(), replay buffers, VecNormalize, and portability debugging.
• Customize policies and exploration: use policies-and-customization for policy_kwargs, custom feature extractors, MultiInputPolicy, action noise, gSDE, and HerReplayBuffer.

Common Starting Points

• For a first CartPole-style training script, route to training-and-algorithms and use its scripts/train_smoke.py as a safe pattern.
• For custom Gymnasium env failures, route to environments-and-vectorization before changing the RL algorithm.
• For model portability or pickle/load errors, route to evaluation-and-persistence and use print_system_info=True guidance.
• For Dict observations, first validate spaces in environments-and-vectorization, then design MultiInputPolicy details in policies-and-customization.

Shared References

• Read references/troubleshooting.md for cross-cutting install/import, optional dependency, CPU/GPU, render, TensorBoard, and Gymnasium compatibility issues.
• Read references/native-verification.md when selecting safe native SB3 tests or understanding which original repo behaviors informed this skill.
• Read references/repo-provenance.md before deciding whether this skill matches a current Stable-Baselines3 checkout or needs refresh.

Boundaries

This is a runtime user skill, not a maintainer release guide. It does not cover Docker image building, CI workflows, website generation, release publishing, or long benchmark training. If a task requires SB3 Contrib, RL Baselines3 Zoo, or SBX, treat those as adjacent projects and do not assume their APIs are included here.