Which RL algorithms are supported by this skill?

The skill supports all core SB3 algorithms, including PPO, SAC, DQN, TD3, DDPG, and A2C, with guidance on selecting the best one for your action space.

Does it integrate with TensorBoard?

Yes, the skill includes patterns for logging training progress to TensorBoard for visual analysis of rewards and loss metrics.

Does it support parallel processing for faster training?

Absolutely. The skill provides patterns for Vectorized Environments, specifically using SubprocVecEnv for compute-heavy parallel training tasks.

Can I create custom environments with this skill?

Yes, it includes comprehensive templates for inheriting from gymnasium.Env, defining observation/action spaces, and validating them with check_env.

How are models saved and loaded?

The skill provides standardized code for saving model weights and VecNormalize statistics, as well as loading them for inference or resumed training.

Stable Baselines3 Reinforcement Learning

Name: Stable Baselines3 Reinforcement Learning
Author: pur3v4d3r

bypur3v4d3r

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数据科学与机器学习

Implements and optimizes reinforcement learning workflows using the PyTorch-based Stable Baselines3 library.

关于

This skill empowers Claude to act as a reinforcement learning expert, providing standardized implementations for training agents with algorithms like PPO, SAC, and DQN. It streamlines the creation of custom Gymnasium environments, provides robust callback structures for monitoring, and optimizes performance through vectorized environments. Whether you are building a robotics simulation, a game AI, or a financial trading bot, this skill ensures best practices in model persistence, evaluation, and deep RL experimentation using the unified Stable Baselines3 API.

主要功能

Parallel training optimization using DummyVecEnv and SubprocVecEnv
Standardized training patterns for PPO, SAC, DQN, and other major RL algorithms
Integrated evaluation workflows including metrics and video recording
1 GitHub stars
Advanced monitoring with automated callbacks for checkpoints and evaluation
Custom Gymnasium environment creation templates and validation guides

使用场景

Developing and validating custom domain-specific Gym-compatible environments
Training autonomous agents for complex control tasks or gaming simulations
Implementing production-grade RL training pipelines with monitoring and model persistence

关于

主要功能

Parallel training optimization using DummyVecEnv and SubprocVecEnv
Standardized training patterns for PPO, SAC, DQN, and other major RL algorithms
Integrated evaluation workflows including metrics and video recording
1 GitHub stars
Advanced monitoring with automated callbacks for checkpoints and evaluation
Custom Gymnasium environment creation templates and validation guides

使用场景

Developing and validating custom domain-specific Gym-compatible environments
Training autonomous agents for complex control tasks or gaming simulations
Implementing production-grade RL training pipelines with monitoring and model persistence