Reinforcement Learning Skill

SkillDev tools

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

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the Reinforcement Learning Skill skill

What this skill tells your AI

The instructions your AI receives, as published by a5c-ai/babysitter in library/specializations/robotics-simulation/skills/rl-robotics/SKILL.md and read by ahel’s review.

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)

Signals

GitHub stars: 2k
Forks: 112
Last commit: Sep 2026

Advanced

Item type: skill
Key: reinforcement-learning-skill
Source: github.com/a5c-ai/babysitter

github.com/a5c-ai/babysitter