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stable-baselines3

Train and evaluate RL agents with unified SB3 algorithms in Gymnasium environments.

321|26|Updated Mar 10, 2026
Search Tags:#callbacks#reinforcement-learning#stable-baselines3#rl#gymnasium#vectorized-environments#training-evaluation
One-click install
npx skills add https://github.com/mkurman/tamux --skill stable-baselines3-mkurman
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: stable-baselines3
Source: https://github.com/mkurman/tamux/tree/main/skills/scientific-skills/stable-baselines3
Command: npx skills add https://github.com/mkurman/tamux --skill stable-baselines3-mkurman

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 provides a robust, unified set of reinforcement learning algorithms with a clean API to accelerate experimentation, training, and deployment of agents.

Core Features & Use Cases

  • Training & evaluation templates for PPO, A2C, SAC, TD3, DDPG, DQN, and HER.
  • Environment tooling including vectorized environments, custom env templates, and environment validation utilities.
  • Model persistence & logging with checkpoints, saved policies, and TensorBoard-compatible logging for reproducibility.
  • Workflow guidance covering end-to-end RL projects from setup to evaluation with reusable scripts.

Quick Start

Install stable-baselines3 and gymnasium, then run the provided training template to start training an RL agent.

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 with Gymnasium environments?▼

Vectorized environments allow multiple Gymnasium instances to run simultaneously, increasing data throughput. The Skill provides environment tooling and templates to easily configure and validate these vectorized setups for agent training.

Can I use callbacks for reinforcement learning training and evaluation?▼

Model persistence is supported through checkpoints and saved policies, ensuring reproducibility. Logging is TensorBoard-compatible, enabling you to track training metrics and evaluate reinforcement learning agent performance effectively.

Does Stable Baselines3 support custom Gymnasium environment templates?▼

Yes, it provides custom environment templates and validation utilities. These tools ensure your custom Gymnasium environments conform to the required API standards before you begin training reinforcement learning agents.

What's the best way to save and log reinforcement learning model policies?▼

The best way is using the built-in model persistence and logging features. You can save model policies as checkpoints and utilize TensorBoard-compatible logging to monitor training progress and ensure reproducible results.

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