stable-baselines3

Train and evaluate reinforcement learning agents with PyTorch and Gymnasium.

Updated Dec 17, 2025

Search Tags:#PyTorch #agent training #reinforcement learning #RL #gymnasium

Ready to run

npx skills add https://github.com/robotlearning123/claude-scientific-skills --skill stable-baselines3-robotlearning123

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: stable-baselines3
Source: https://github.com/robotlearning123/claude-scientific-skills/tree/main/scientific-skills/stable-baselines3
Command: npx skills add https://github.com/robotlearning123/claude-scientific-skills --skill stable-baselines3-robotlearning123

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires stable_baselines3[extra], gymnasium, torch, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill simplifies the process of training and evaluating reinforcement learning agents, making it accessible to users without deep expertise in the field.

Core Features & Use Cases

Reinforcement Learning Algorithms: Provides implementations for popular algorithms like PPO, SAC, TD3, and DQN.
Custom Environments: Allows users to create custom Gym environments for training agents.
Vectorized Environments: Enables parallel training of multiple agents for faster results.
Callbacks: Offers callbacks for monitoring training progress, saving checkpoints, and early stopping.
Model Persistence: Allows saving and loading trained models for future use.
Evaluation: Includes tools for evaluating agent performance.
Use Case: A user could use this Skill to train a reinforcement learning agent to play a game like CartPole or Pong.

Quick Start

Use the stable-baselines3 skill to train a PPO agent on the CartPole environment.

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 PyTorch?▼

Train reinforcement learning agents using PyTorch by utilizing provided implementations of algorithms like PPO, SAC, TD3, and DQN. This Skill supports custom Gymnasium environments for applications like robotics and game playing.

What is the best way to train a PPO agent on a CartPole environment?▼

Train a PPO agent on the CartPole environment by using the quick start functionality. This Skill simplifies the reinforcement learning training loop and includes tools for evaluating the agent's performance.

Can I use custom Gymnasium environments for reinforcement learning agent training?▼

Yes, you can use custom Gymnasium environments for reinforcement learning agent training. The Skill allows creating custom environments and supports vectorized environments for parallel training to achieve faster results.

Does this reinforcement learning tool support callbacks for monitoring training progress?▼

Yes, this reinforcement learning tool supports callbacks for monitoring training progress, saving checkpoints, and early stopping. It also allows model persistence to save and load trained models for future use.

Do I need PyTorch and Gymnasium installed to evaluate reinforcement learning agents?▼

Yes, you need PyTorch and Gymnasium installed to evaluate reinforcement learning agents. The Skill requires PyTorch, Gymnasium, and specific algorithm dependencies to handle the training and evaluation processes.