# Reinforcement Learning Skill

> RL training for robot control using simulation with sim-to-real transfer

- Skill: `majiayu000/reinforcement-learning-skill` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds add majiayu000/reinforcement-learning-skill`
- Raw SKILL.md: https://api.skillmd.com/api/skills/majiayu000/reinforcement-learning-skill/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: majiayu000 (https://skillmd.com/u/majiayu000)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/majiayu000/reinforcement-learning-skill

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# Reinforcement Learning Skill

## Overview

Expert skill for training reinforcement learning agents for robot control tasks, including environment design, training pipelines, and sim-to-real transfer.

## Capabilities

- Configure Gym/Gymnasium environments for robots
- Set up Stable Baselines3 training (PPO, SAC, TD3)
- Implement custom observation and action spaces
- Design reward shaping strategies
- Configure parallel environment training
- Implement domain randomization for sim-to-real
- Set up curriculum learning
- Configure vision-based RL with CNNs
- Implement policy distillation
- Export policies for deployment (ONNX, TorchScript)

## Target Processes

- rl-robot-control.js
- imitation-learning.js
- sim-to-real-validation.js
- nn-model-optimization.js

## Dependencies

- Stable Baselines3
- Gymnasium
- Isaac Gym
- rsl_rl

## Usage Context

This skill is invoked when processes require RL-based robot control, learning from simulation, or transferring learned policies to real robots.

## Output Artifacts

- Gymnasium environment implementations
- Training configurations
- Reward function designs
- Domain randomization configs
- Trained policy checkpoints
- Deployment-ready models (ONNX)