Interactive Lecture: Reinforcement Learning

This lecture covers advanced topics in machine learning, focusing on reinforcement learning (RL). The instructor explains RL policies, models of the world, optimal policy finding, value functions, V-Q-value functions, Bellman recursion, and discount factors. Exercises involve drawing optimal policies in gridworlds. The lecture also delves into different update rules in dynamic programming, Monte Carlo sampling, and on-policy TD control. It discusses offline vs. online search, combining RL with supervised learning, and the importance of realistic simulators. The session concludes with a summary of RL concepts and their applications.