Reinforcement Learning: Basics and Applications

This lecture introduces the fundamentals of reinforcement learning, focusing on learning through trial-and-error. It covers topics such as action values, Q-learning, deep reinforcement learning, and the application of RL in various scenarios like playing games and planning efficiently. The lecture also discusses the role of dopamine in signaling reward prediction errors and the distinction between goal-directed and habitual behavior in RL. Additionally, it explores the use of Monte Carlo estimation and model-based RL systems. The session concludes with insights on the challenges and benefits of scientific advancements in the context of societal wisdom.