shawnhymel/reinforcement-learning-for-robotics

This repository is a robotics reinforcement-learning demonstration, taking a two-wheel balance bot from simulation environments through PPO training and toward embedded inference and remote operation. Its main progression includes baseline, domain-randomized, and command-conditioned simulation, followed by policy deployment on the robot. Separate hardware examples cover sensor checks and IMU calibration. The tree and README establish these product areas; sampled excerpts confirm environment behavior, PPO implementation, embedded actor inference, and WebSocket command input, but do not establish every notebook-to-module connection.