From watching AI conquer Super Mario to building production-ready Reinforcement Learning (RL) systems, this talk explores how Python developers can dive into RL without requiring advanced degrees or big tech resources. Drawing on our three-year journey of building a production RL system, I’ll show how developers can leverage RL through practical strategies and accessible tools. Using pi_optimal, an open-source RL toolkit, we’ll bridge the gap between cutting-edge RL research and real-world applications. Attendees will gain actionable insights, implementation techniques, and hands-on experience to confidently start their own RL projects.