Model Based Reinforcement Learning: Policy Iteration, Value Iteration, and Dynamic Programming

Here we introduce dynamic programming, which is a cornerstone of model-based reinforcement learning. We demonstrate dynamic programming for policy iteration and value iteration, leading to the quality function and Q-learning. This is a lecture in a series on reinforcement learning, following the new Chapter 11 from the 2nd edition of our book “Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control“ by Brunton and Kutz Book Website: Book PDF: Amazon: Brunton Website: