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stable-baselines3

Train and evaluate reinforcement learning agents with Stable Baselines3 in Gymnasium environments.

1|Updated Mar 11, 2026
Search Tags:#evaluation#training#vectorization#reinforcement-learning#stable-baselines3#rl#gymnasium
One-click install
npx skills add https://github.com/SciMate-AI/scicli --skill stable-baselines3-scimate-ai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: stable-baselines3
Source: https://github.com/SciMate-AI/scicli/tree/main/internal/skills/bundled/claude-scientific-skills/skills/stable-baselines3
Command: npx skills add https://github.com/SciMate-AI/scicli --skill stable-baselines3-scimate-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires gymnasium, stable-baselines3, numpy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Stable Baselines3 provides ready-to-use RL algorithms and tools to train, evaluate, and compare reinforcement learning agents in Gymnasium environments, simplifying experimentation and deployment.

Core Features & Use Cases

  • Implementations: PPO, A2C, SAC, TD3, DQN, and HER-ready variants with a consistent API for rapid prototyping and production-grade experiments.
  • Vectorized training and evaluation: supports SubprocVecEnv and VecNormalize for scalable training and robust evaluation pipelines.
  • Training, persistence, and evaluation workflows: includes example scripts for training, evaluating, and benchmarking models, plus guidance on custom environments and callbacks.

Quick Start

Try a basic PPO run on CartPole-v1 using a 4-env vectorized setup.

Frequently Asked Questions about stable-baselines3

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I train reinforcement learning agents using PPO or SAC in Gymnasium environments?▼

To train reinforcement learning agents, you can use ready-to-use implementations like PPO, SAC, and TD3 with a consistent API for single-agent Gymnasium workflows. The Skill provides example scripts for rapid prototyping and production-grade experiments.

What is the best way to scale reinforcement learning training across multiple environments?▼

The best way to scale reinforcement learning training is using vectorized environments. The Skill supports SubprocVecEnv and VecNormalize to enable scalable training and robust evaluation pipelines across multiple environments simultaneously.

Does Stable Baselines3 work with custom Gymnasium environments and callbacks?▼

Yes, Stable Baselines3 works with custom Gymnasium environments and callbacks. The Skill includes guidance and example scripts for setting up custom environments, configuring training callbacks, and implementing persistence for your models.

How do I evaluate and benchmark reinforcement learning models after training?▼

To evaluate and benchmark reinforcement learning models, the Skill provides evaluation utilities and example scripts. These tools help you systematically assess trained agents, compare performance across algorithms, and measure results in Gymnasium environments.

Can I use Stable Baselines3 for rapid prototyping and production-grade reinforcement learning experiments?▼

Yes, you can use Stable Baselines3 for both rapid prototyping and production-grade reinforcement learning experiments. It offers HER-ready variants and a consistent API across algorithms like A2C, DQN, PPO, SAC, and TD3, simplifying deployment and comparison.

What are the limitations of using vectorized training for reinforcement learning workflows?▼

Vectorized training for reinforcement learning is limited to single-agent Gymnasium workflows. While it supports scalable training via SubprocVecEnv and VecNormalize, it does not natively handle multi-agent scenarios or environments outside the Gymnasium API scope.

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