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stable-baselines3

Train and evaluate reinforcement learning agents with Stable Baselines3.

3|Updated Mar 11, 2026
Search Tags:#callbacks#reinforcement-learning#vectorized-env#sb3#rl#training-pipeline#gymnasium
One-click install
npx skills add https://github.com/JunMA98/Computer-science-claude-skills --skill stable-baselines3-junma98
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: stable-baselines3
Source: https://github.com/JunMA98/Computer-science-claude-skills/tree/main/skills/stable-baselines3
Command: npx skills add https://github.com/JunMA98/Computer-science-claude-skills --skill stable-baselines3-junma98

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Stable Baselines3 simplifies building, training, and evaluating reinforcement learning agents by providing robust, well-documented implementations and a reusable workflow.

Core Features & Use Cases

  • Training RL agents (PPO, A2C, SAC, TD3, DQN) and evaluating them with standardized APIs.
  • Guidance for custom environments, vectorized training, callbacks, and model persistence to support repeatable experiments.
  • End-to-end pipelines including training scripts, evaluation templates, and references for algorithm selection and environment design.

Quick Start

Run the provided train_rl_agent.py with your environment ID to start a full SB3 training workflow.

Frequently Asked Questions about stable-baselines3

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

FAQPage Schema
How do I train a reinforcement learning agent using Stable Baselines3 and Gymnasium?▼

You can train a reinforcement learning agent using Stable Baselines3 by running the provided train_rl_agent.py script with your Gymnasium environment ID to execute a complete SB3 training workflow.

What RL algorithms does Stable Baselines3 support for vectorized environment training?▼

Stable Baselines3 supports training reinforcement learning agents using PPO, A2C, SAC, TD3, and DQN algorithms across single or multi-environment vectorized setups for fast prototyping and production experiments.

Can I use callbacks and model persistence for repeatable RL experiments in SB3?▼

Yes, Stable Baselines3 provides guidance for custom environments, vectorized training, callbacks, and model persistence to support repeatable reinforcement learning experiments and standardized evaluation.

Does Stable Baselines3 work with TensorBoard and VecNormalize for monitoring RL training?▼

Stable Baselines3 relies on optional tools like VecNormalize, VecVideoRecorder, and TensorBoard for enhanced performance and monitoring during reinforcement learning agent training and evaluation.

What's the best way to evaluate trained reinforcement learning agents with SB3?▼

The best way to evaluate trained reinforcement learning agents is using the standardized SB3 APIs and provided evaluation templates that ensure consistent performance measurement across different algorithms.

Do I need NumPy and Gymnasium to start training RL agents with Stable Baselines3?▼

Yes, you need Stable Baselines3, Gymnasium, and NumPy installed as required dependencies to start training and evaluating reinforcement learning agents using the provided templates and scripts.

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