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.

Citable link for this video: https://doi.org/10.52843/cassyni.6fs4s9

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: http://databookuw.com
Book PDF: http://databookuw.com/databook.pdf

Amazon: https://www.amazon.com/Data-Driven-Sc...

Brunton Website: eigensteve.com

This video was produced at the University of Washington