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stable-baselines3

combined

Trains and evaluates single-agent reinforcement learning with Stable Baselines3 (PPO, SAC, DQN, TD3, DDPG, A2C), Gymnasium custom environments, vectorized rollouts, callbacks, and checkpoint normalization. Applies to reproducible RL experiments, continuous control, discrete actions, and SB3-Contrib recurrent or masked policies. 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.

When to use

• reproducible RL experiments
• continuous control
• discrete actions
• SB3-Contrib recurrent or masked policies
• training RL agents
• creating custom environments
• implementing callbacks
• optimizing training workflows

Skill page

Conflict

The K-Dense copy is the more current, version-pinned procedure (SB3 2.9.0, Python 3.10+, PyTorch >= 2.8, Gymnasium 1.3.0, zsh quoting, MuJoCo extra, SB3-Contrib/SBX/RL-Zoo pointers, device/seed guidance, and a note that the custom-env template shape changed from (2,) to (4,)). The Vibe-Skills copy adds a routing boundary and a pufferlib alternative, and it contains a vectorized-env snippet that explicitly uses SubprocVecEnv, which the K-Dense copy does not show. Because each copy has material steps or constraints the other lacks, crowning one would hide a real alternative.