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 training, multi-agent systems, or custom vectorized environments, use pufferlib instead.
npx skills add K-Dense-AI/scientific-agent-skills --skill stable-baselines3 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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" # 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: ```bash uv pip install "gymnasium[mujoco]" ``` Check your version: ```python import stable_baselines3 print(stable_baselines3.__version__) ``` ## Related Projects - **[SB3-Contrib](https://github.com/Stable-Baselines-Team/stable-baselines3-contrib)**: experimental algorithms (MaskableP
- Overview
- Installation
- Related Projects
- Core Capabilities
- 1. Training RL Agents
- 2. Custom Environments
- 3. Vectorized Environments
- 4. Callbacks for Monitoring and Control
- 5. Model Persistence and Inspection
- 6. Evaluation and Recording
- 7. Advanced Features
- Workflow Guidance
- Resources
- scripts/
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" uv pip install "gymnasium[mujoco]"
What does the stable-baselines3 skill do?
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.
How do I install it?
Run `npx skills add K-Dense-AI/scientific-agent-skills --skill stable-baselines3 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From K-Dense-AI/scientific-agent-skills, a repository with 32,619 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.