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 fo…
---
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": "1.1", "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**.
```bash
# Basic installation
uv pip install "stable-baselines3>=2.8"
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