Reinforcement Learning Skill

Agent Building

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

QUICK START

How to use this skill

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Prompt to paste

I want to install this Agent Skill for this project in Codex.

Source SKILL.md: https://github.com/a5c-ai/babysitter/blob/HEAD/library/specializations/robotics-simulation/skills/rl-robotics/SKILL.md

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First, summarize what it does, its dependencies, license status if identifiable, and any risks. Show the exact files you propose to add under .agents/skills/reinforcement-learning-skill/. Do not write files or run scripts until I approve.

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Copying this prompt does not install or run the skill. Review third-party files before use. Codex skill guide

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)