In Episode 26 of '100 Days of Data,' Jonas and Amy dive into the world of Reinforcement Learning (RL), likening it to training a dog—with rewards, penalties, and lots of trial and error. They explain how AI agents learn by interacting with environments, adjusting their actions to maximize rewards over time. The discussion covers core RL concepts like agents, states, actions, policies, exploration vs. exploitation, and value functions. Real-world examples include AlphaGo, autonomous vehicles, finance, and personalized healthcare. The hosts also touch on key challenges such as reward design, safety constraints, and the extensive data needs RL systems face. Whether you're new to AI or need a refresher, this episode offers practical insights and a relatable introduction to one of the most exciting areas in machine learning today.