In this episode we explore Reinforcement Learning, an AI framework used in systems such as ChatGPT. Reinforcement Learning, a subfield of Artificial Intelligence, is a method for machines to learn optimal decision-making through trial and error by receiving rewards or penalties for their actions. This beginner-friendly introduction covers fundamental aspects, such as basic terminology like agents, environments, and rewards, alongside core concepts like the Markov Decision Process. The text further explains the workflow of reinforcement learning, outlines its key characteristics including sequential decision-making and delayed feedback, and categorizes common algorithms and types like positive and negative reinforcement. Finally, it showcases practical applications of this technology across diverse fields, including robotics, autonomous vehicles, and game playing.