stable-baselines3
Category: data-science-and-ml · License: MIT · Version: 1.3 · Includes scripts
Tags: reinforcement learning, PPO, SAC, DQN, Gymnasium
When to use it
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.Example requests
Ask your agent in plain language, for example:- “Train a PPO agent on CartPole and show me how to save and evaluate it.”
- “I wrote a custom Gymnasium env in warehouse_env.py with a Box observation space of shape (84,84,3) uint8 images, and SAC throws ‘You have passed a tuple to the constructor’ / complains about the observation space when I call model = SAC(‘CnnPolicy’, env). Also SAC doesn’t make sense for discrete actions, I think my action space is Discrete(4) with start=1. What’s going on and how do I fix it?”
- “I have a simulated robot arm with continuous joint torques and a reward function I’ve defined. I want a sample-efficient learner I can get running quickly with checkpoints, periodic evaluation that keeps the best model, and TensorBoard curves. Where do I start?”
Install
/plugin marketplace add KalarisLabs/research-agent-skills and install the whole data-science-and-ml category with /plugin install data-science-and-ml@research-agent-skills.