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stable-baselines3

Train reinforcement learning agents with Stable Baselines3 algorithms in Gymnasium environments.

Updated Apr 2, 2026
Search Tags:#callbacks#reinforcement-learning#stable-baselines3#vectorized-envs#rl-algorithms#model-persistence
One-click install
npx skills add https://github.com/viniruggeri/applied-dynamical-systems --skill stable-baselines3-viniruggeri
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: stable-baselines3
Source: https://github.com/viniruggeri/applied-dynamical-systems/tree/main/.agents/skills/stable-baselines3
Command: npx skills add https://github.com/viniruggeri/applied-dynamical-systems --skill stable-baselines3-viniruggeri

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Provides production-ready reinforcement learning algorithms with a unified API to simplify training, evaluation, and deployment of agents.

Core Features & Use Cases

  • Supports popular Stable Baselines3 algorithms (PPO, SAC, TD3, DDPG, A2C, DQN, HER) for single-agent tasks and vectorized environments.
  • Includes training, evaluation, custom environments, callbacks, and model persistence workflows.
  • Enables experimentation and rapid prototyping with a consistent, well-documented API.

Quick Start

Install Stable Baselines3, create a Gymnasium environment, and start training an agent with PPO.

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 PPO or SAC in Gymnasium?▼

Train reinforcement learning agents in Gymnasium by instantiating an SB3 algorithm like PPO or SAC and calling the train method on your environment. This provides a unified API to simplify agent training and deployment workflows.

What is the best way to scale reinforcement learning training with vectorized environments?▼

Scale reinforcement learning training by applying vectorized environments and common wrappers like VecNormalize. This setup supports multiple environment instances simultaneously to accelerate agent training using SB3 algorithms.

Does Stable Baselines3 support custom callbacks during reinforcement learning training?▼

Yes, Stable Baselines3 supports custom callbacks during reinforcement learning training. Callbacks allow you to integrate custom logic, monitor training progress, and execute specific actions at defined training stages.

Can I use Stable Baselines3 for both continuous and discrete action spaces?▼

Yes, you can use Stable Baselines3 for both continuous and discrete action spaces. It includes algorithms like SAC and TD3 for continuous control, and DQN for discrete action tasks within Gymnasium environments.

How do I save and load reinforcement learning models for deployment?▼

Save and load reinforcement learning models for deployment using SB3's built-in model persistence tooling. This functionality allows you to store trained agents, reload them later, and resume training or run evaluations.

Why should I use Stable Baselines3 instead of other reinforcement learning libraries?▼

Stable Baselines3 provides production-ready reinforcement learning implementations with a unified, well-documented API. It enables rapid prototyping and experimentation by supporting single-agent and vectorized Gymnasium setups out of the box.

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