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HaykTarkhanyan
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stable-baselines3

Train RL agents with SB3 algorithms in Gymnasium environments.

Updated Mar 31, 2026
Search Tags:#evaluation#reinforcement-learning#stable-baselines3#custom-environments#vectorized-environments#training-templates
One-click install
npx skills add https://github.com/HaykTarkhanyan/dst_research --skill stable-baselines3-hayktarkhanyan
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: stable-baselines3
Source: https://github.com/HaykTarkhanyan/dst_research/tree/main/.claude/skills/stable-baselines3
Command: npx skills add https://github.com/HaykTarkhanyan/dst_research --skill stable-baselines3-hayktarkhanyan

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Stable Baselines3 provides production-ready reinforcement learning algorithms with a unified PyTorch API, enabling reliable training, quick prototyping, and accessible experimentation for developers and researchers.

Core Features & Use Cases

  • Training RL agents with PPO, SAC, DQN, TD3, DDPG, A2C via SB3's unified API for single-agent tasks and reproducible experiments.
  • Custom Environments: guidelines and templates for Gymnasium environments, with validation and best practices for environment design.
  • Vectorized Environments: support through make_vec_env, DummyVecEnv, and SubprocVecEnv to accelerate training.
  • Callbacks for Monitoring and Control: EvalCallback, CheckpointCallback, StopTrainingOnRewardThreshold, and more for robust training pipelines.
  • Model Persistence and Evaluation: save/load models, normalize statistics, and evaluate performance with evaluate_policy.
  • Workflow and References: template scripts and reference guides to streamline RL project work.

Quick Start

Install stable-baselines3 and run the training template (scripts/train_rl_agent.py) to kick off training.

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 stable-baselines3 and Gymnasium?▼

Train reinforcement learning agents by running the provided training template scripts with stable-baselines3 algorithms. You can apply PPO, SAC, or DQN to single-agent Gymnasium environments to accelerate prototyping and ensure reproducible experiments.

Can I use vectorized environments to accelerate reinforcement learning training?▼

Yes, you can accelerate reinforcement learning training by using vectorized Gymnasium environments. The templates support make_vec_env, DummyVecEnv, and SubprocVecEnv to run multiple environment instances in parallel for faster processing.

How do I monitor and save models during reinforcement learning training?▼

Monitor and save reinforcement learning models using callbacks like EvalCallback, CheckpointCallback, and StopTrainingOnRewardThreshold. These callbacks enable robust training pipelines by automatically evaluating performance and persisting model states.

What is the best way to evaluate a trained reinforcement learning policy?▼

Evaluate a trained reinforcement learning policy using the evaluate_policy function. This allows you to assess agent performance, normalize statistics, and save or load models for future experimentation and deployment.

Does this provide templates for building custom Gymnasium environments?▼

Yes, it provides guidelines and templates for building custom Gymnasium environments. These templates include validation and best practices for environment design to streamline your reinforcement learning workflow.

What are the limitations when using stable-baselines3 for reinforcement learning?▼

Limitations include requiring gymnasium and stable-baselines3 as core dependencies. The templates focus on single-agent and vectorized environments, meaning multi-agent reinforcement learning scenarios are not natively supported by this specific workflow.

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