/stable-baselines3

name: stable-baselines3
description: 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 training, multi-agent systems, or custom vectorized environments, use pufferlib instead.
license: MIT license
allowed-tools: Read Write Edit Bash
compatibility: Requires Python 3.10+, PyTorch >= 2.3, and stable-baselines3 2.8+. Gymnasium environments; optional extras for TensorBoard and Atari (ale-py).
metadata:
  version: "1.3"
  skill-author: K-Dense Inc.

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 **2.8.0** (April 2026). Docs: [stable-baselines3.readthedocs.io](https://stable-baselines3.readthedocs.io/en/master/).

Installation

Tested against **stable-baselines3 2.8.0**. Requires **Python 3.10+** (3.9 dropped in 2.8.0) and **PyTorch >= 2.3**.

# Basic installation
uv pip install "stable-baselines3>=2.8"

# With extra dependencies (TensorBoard, ale-py for Atari, etc.)
uv pip install "stable-baselines3[extra]>=2.8"

On zsh, quote brackets: `uv pip install 'stable-baselines3[extra]>=2.8'`.

For MuJoCo continuous-control benchmarks:

uv pip install "gymnasium[mujoco]"

Check your version:

import stable_baselines3
print(stable_baselines3.__version__)

Related Projects

• **[SB3-Contrib](https://github.com/Stable-Baselines-Team/stable-baselines3-contrib)**: experimental algorithms (MaskablePPO, CrossQ, QR-DQN, RecurrentPPO) — separate `sb3-contrib` package
• **[RL Baselines3 Zoo](https://github.com/DLR-RM/rl-baselines3-zoo)**: pre-trained agents, hyperparameters, training scripts
• **[SBX](https://github.com/araffin/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)

# 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)

**Important Notes:**

• `total_timesteps` is a lower bound; actual training may exceed this due to batch collection
• Use `model.load()` as a static method, not on an existing instance
• The replay buffer is NOT saved with the model to save space

**Algorithm Selection:** Use `references/algorithms.md` for detailed algorithm characteristics and selection guidance. Quick reference:

• **PPO/A2C**: General-purpose, supports all action space types, good for multiprocessing
• **SAC/TD3**: Continuous control, off-policy, sample-efficient
• **DQN**: Discrete actions, off-policy
• **HER**: Goal-conditioned tasks

See `scripts/train_rl_agent.py` for a complete training template with best practices.

2. Custom Environments

**Requirements:** Custom environments must inherit from `gymnasium.Env` and implement:

• `__init__()`: Define action_space and observation_space
• `reset(seed, options)`: Return initial observation and info dict
• `step(action)`: Return observation, reward, terminated, truncated, info
• `render()`: Visualization (optional)
• `close()`: Cleanup resources

**Key Constraints:**

• Image observations must be `np.uint8` in range [0, 255]
• Use channel-first format when possible (channels, height, width)
• SB3 normalizes images automatically by dividing by 255
• Set `normalize_images=False` in policy_kwargs if pre-normalized
• SB3 does NOT support `Discrete` or `MultiDiscrete` spaces with `start!=0`

**Validation:**

from stable_baselines3.common.env_checker import check_env

check_env(env, warn=True)

See `scripts/custom_env_template.py` for a complete custom environment template and `references/custom_environments.md` for comprehensive guidance.

3. Vectorized Environments

**Purpose:** Vectorized environments run multiple environment instances in parallel, accelerating training and enabling certain wrappers (frame-stacking, normalization).

**Types:**

• **DummyVecEnv**: Sequential execution on current process (for lightweight environments)
• **SubprocVecEnv**: Parallel execution across processes (for compute-heavy environments)

**Quick Setup:**

from stable_baselines3.common.env_util import make_vec_env

# Create 4 parallel environments
env = make_vec_env("CartPole-v1", n_envs=4, vec_env_cls=SubprocVecEnv)

model = PPO("MlpPolicy", env, verbose=1)
model.learn(total_timesteps=25000)

**Off-Policy Optimization:** When using multiple environments with off-policy algorithms (SAC, TD3, DQN), set `gradient_steps=-1` to perform one gradient update per environment step, balancing wall-clock time and sample efficiency.

**API Differences:**

• `reset()` returns only observations (info available in `vec_env.reset_infos`)
• `step()` returns 4-tuple: `(obs, rewards, dones, infos)` not 5-tuple
• Environments auto-reset after episodes
• Terminal observations available via `infos[env_idx]["terminal_observation"]`

See `references/vectorized_envs.md` for detailed information on wrappers and advanced usage.

4. Callbacks for Monitoring and Control

**Purpose:** Callbacks enable monitoring metrics, saving checkpoints, implementing early stopping, and custom training logic without modifying core algorithms.

**Common Callbacks:**

• **EvalCallback**: Evaluate periodically and save best mod