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RamanEbrahimi
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stable-baselines3

Trains RL agents with PPO, SAC, DQN and other algorithms using PyTorch and gymnasium.

3|Updated Apr 17, 2026
Search Tags:#PyTorch#training#reinforcement learning#gymnasium#RL algorithms#algorithm implementations
One-click install
npx skills add https://github.com/RamanEbrahimi/raman-marketplace --skill stable-baselines3-ramanebrahimi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: stable-baselines3
Source: https://github.com/RamanEbrahimi/raman-marketplace/tree/main/plugins/agentic-research/skills/scientific-skills/stable-baselines3
Command: npx skills add https://github.com/RamanEbrahimi/raman-marketplace --skill stable-baselines3-ramanebrahimi

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill streamlines the process of training and evaluating reinforcement learning (RL) agents, providing a robust framework for various RL algorithms and environments.

Core Features & Use Cases

  • Training RL Agents: Offers access to production-ready RL algorithms including PPO, SAC, DQN, TD3, DDPG, A2C, and HER.
  • Custom Environments: Allows users to create and validate custom gym environments.
  • Vectorized Environments: Supports parallel training using vectorized environments to speed up training.
  • Callbacks: Incorporates monitoring and control features via callbacks for evaluation, checkpointing, and early stopping.
  • Model Persistence: Enables saving and loading of trained models.
  • Evaluation and Recording: Facilitates evaluation and video recording of agent behavior.
  • Advanced Features: Supports learning rate schedules, multi-input policies, and Hindsight Experience Replay.
  • Use Case: Ideal for developers and researchers looking to implement and test RL algorithms in a standardized environment.

Quick Start

Train a PPO agent on the "CartPole-v1" environment using the following command:

python train_rl_agent.py CartPole-v1

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 PyTorch and gymnasium?▼

To train reinforcement learning agents with PyTorch, this framework executes state-of-the-art algorithms like PPO, SAC, and DQN directly against gymnasium environments. You can initiate training on standard tasks using a simple Python script command.

What reinforcement learning algorithms are available for custom environments?▼

Available reinforcement learning algorithms include PPO, SAC, DQN, TD3, DDPG, A2C, and HER. These algorithms enable you to create and validate custom gym environments for decision-making tasks in games, robotics, and simulations.

Does this reinforcement learning framework support parallel training?▼

Yes, this reinforcement learning framework supports parallel training using vectorized environments to significantly speed up training. It also incorporates monitoring callbacks for evaluation, checkpointing, and early stopping during the parallel process.

Can I save and load trained RL models for later evaluation?▼

Yes, you can save and load trained RL models using built-in model persistence features. This enables saving trained models for later evaluation and facilitates video recording of agent behavior to analyze decision-making performance.

What are the limitations when using Hindsight Experience Replay for training?▼

Hindsight Experience Replay (HER) is supported as an advanced feature alongside learning rate schedules and multi-input policies. Successful training requires proper environment setup and validation using both PyTorch and gymnasium dependencies.

Do I need PyTorch and gymnasium to implement RL algorithms for robotics?▼

Yes, you need both PyTorch and gymnasium for algorithm execution and environment interaction. These dependencies are required to implement and test state-of-the-art RL algorithms for robotics, simulations, and games.

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