reinforcement-learning-skill

name	Reinforcement Learning Skill
description	RL training for robot control using simulation with sim-to-real transfer
slug	rl-robotics
category	Learning
allowed-tools	["Bash","Read","Write","Edit","Glob","Grep"]
graph	{"domains":["domain:robotics"],"specializations":["specialization:robotics-simulation"],"skillAreas":["skill-area:motion-planning","skill-area:sensor-fusion"],"roles":["role:research-engineer"]}

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)