reinforcement-learning-skill

# 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)