This episode deconstructs Reinforcement Learning (RL), the third and final paradigm of machine learning. We explore how an "agent" learns to make optimal decisions by interacting with an "environment" and receiving rewards, a trial-and-error process that mirrors human learning. This framework is the key to understanding AI's most famous achievements, from mastering complex games like Go to enabling autonomous robots.
