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Ritabrata-Chakraborty
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stable-baselines3

Train single-agent reinforcement learning models in Gymnasium environments with PyTorch.

Updated Jun 6, 2026
Search Tags:#algorithm implementation#agent training#reinforcement learning#pytorch#rl#gymnasium
One-click install
npx skills add https://github.com/Ritabrata-Chakraborty/Claude-Setup --skill stable-baselines3-ritabrata-chakraborty
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: stable-baselines3
Source: https://github.com/Ritabrata-Chakraborty/Claude-Setup/tree/main/skills/stable-baselines3
Command: npx skills add https://github.com/Ritabrata-Chakraborty/Claude-Setup --skill stable-baselines3-ritabrata-chakraborty

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires python>=3.10, torch, stable_baselines3>=2.8, gymnasium, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill solves the challenge of implementing and training various reinforcement learning (RL) agents. It offers a comprehensive toolkit to streamline RL experiments, prototyping, and model implementations, especially for single-agent RL with Gymnasium environments.

Core Features & Use Cases

  • Reinforcement Learning Algorithms: Offers production-ready algorithms like PPO, SAC, DQN, TD3, DDPG, A2C, etc.
  • Unified API: Facilitates easy experimentation and rapid prototyping with a scikit-learn-like interface.
  • Gymnasium Compatibility: Best suited for RL tasks with Gymnasium environments, supporting single-agent RL.
  • Use Case: Imagine you want to implement a custom environment for a new game. You can use this Skill to define the environment and train agents like DQN or PPO with ease.

Quick Start

Use the stable-baselines3 skill to train a reinforcement learning agent on your custom Gymnasium environment. First, create your environment by extending gymnasium.Env and define your action and observation spaces. Then, instantiate an agent using the desired algorithm, train it with the appropriate number of timesteps, and evaluate its performance.

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 in a custom Gymnasium environment?▼

To train a reinforcement learning agent in a custom Gymnasium environment, extend the gymnasium.Env class, define your observation and action spaces, instantiate an agent like PPO or DQN, and train it for the desired timesteps.

What is the best way to implement single-agent RL algorithms with PyTorch?▼

The best way to implement single-agent RL algorithms with PyTorch is using a unified, scikit-learn-like API that offers production-ready algorithms like PPO, SAC, DQN, TD3, DDPG, and A2C for rapid prototyping.

Do I need Python 3.10 to use stable-baselines3 for reinforcement learning?▼

Yes, stable-baselines3 for reinforcement learning requires Python 3.10 or higher, along with PyTorch >= 2.3 and stable-baselines3 version 2.8 or higher to run correctly.

Can I use stable-baselines3 for multi-agent reinforcement learning tasks?▼

No, stable-baselines3 cannot be used for multi-agent reinforcement learning tasks. It is specifically designed and best suited for single-agent reinforcement learning experiments within Gymnasium environments.

What reinforcement learning algorithms are available for agent training?▼

Available reinforcement learning algorithms for agent training include PPO, SAC, DQN, TD3, DDPG, and A2C, which can be easily instantiated and trained using a unified scikit-learn-like API.

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