Reinforcement Learning for Robotics: A Comprehensive 2025 Guide

This comprehensive guide covers reinforcement learning for robotics from fundamentals to production deployment, written by an engineer with 12+ years of field experience deploying RL on real hardware. It addresses the critical gap between simulation and real-world deployment, covering algorithm selection, sim2real transfer, safety, and MLOps infrastructure. The author emphasizes that RL in robotics is 90% engineering discipline—reward design, safety systems, and production architecture—rather than algorithm choice.