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 foryourhealth111-pixel/Vibe-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 ## Routing Boundary Use this skill only for Stable-Baselines3, SB3, PPO/SAC/DQN, reinforcement learning agents, Gymnasium environments, policies, rollouts, and RL training workflows. Do not use it for ordinary scikit-learn, random forests, supervised classification, tabular regression, or generic machine-learning model training. ## Overview Stable Baselines3 (SB3) is a PyTorch-
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 foryourhealth111-pixel/Vibe-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 foryourhealth111-pixel/Vibe-Skills, a repository with 2,593 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.