stable-baselines3
Production-ready reinforcement learning algorithms (PPO, SAC, DQN, TD3, DDPG, A2C) with scikit-learn-like API. Use for standard RL experiments, quick prototyping, and well-documented algorithm implementations. Best for single-agent RL with Gymnasium environments. For high-performance parallel traini
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Stable Baselines3
Overview
Stable Baselines3 (SB3) is a PyTorch-based library providing reliable implementations of reinforcement learning algorithms. This skill provides comprehensive guidance for training RL agents, creating custom environments, implementing callbacks, and optimizing training workflows using SB3's unified API.
Current upstream: SB3 2.9.0 (June 15, 2026). Docs: stable-baselines3.readthedocs.io.
Installation
Tested against stable-baselines3 2.9.0. Requires Python 3.10+ (3.9 dropped in 2.8.0) and PyTorch >= 2.8.
# Basic installation
uv pip install "stable-baselines3==2.9.0"
# With extra dependencies (TensorBoard, ale-py for Atari, etc.)
uv pip install "stable-baselines3[extra]==2.9.0"
The 2.9.0 release supports Gymnasium >=0.29.1,<2.0; this review exercised Gymnasium 1.3.0, PyTorch 2.14.1 and Python 3.13 on CPU. pandas/matplotlib are now optional extras. Installation requires network unless packages are cached; local RL training needs no credentials or service endpoints.
On zsh, quote brackets: uv pip install 'stable-baselines3[extra]==2.9.0'.
For MuJoCo continuous-control benchmarks:
uv pip install "gymnasium[mujoco]"
Check your version:
import stable_baselines3
print(stable_baselines3.__version__)
Related Projects
- SB3-Contrib: experimental algorithms (MaskablePPO, CrossQ, QR-DQN, RecurrentPPO) — separate
sb3-contribpackage - RL Baselines3 Zoo: pre-trained agents, hyperparameters, training scripts
- SBX: SB3 + JAX implementations for users who prefer JAX over PyTorch
Core Capabilities
1. Training RL Agents
Basic Training Pattern:
import gymnasium as gym
from stable_baselines3 import PPO
# Create environment
env = gym.make("CartPole-v1")
# Initialize agent (device="cpu" is often faster for MlpPolicy on small envs)
model = PPO("MlpPolicy", env, verbose=1, device="cpu", seed=0)
# Train the agent
model.learn(total_timesteps=10000)
# Save the model
model.save("ppo_cartpole")
# Load the model (without prior instantiation)
model = PPO.load("ppo_cartpole", env=env, device="cpu")
env.close()