Here we describe Q-learning, which is one of the most popular methods in reinforcement learning. Q-learning is a type of temporal difference learning. We discuss other TD algorithms, such as SARSA, and connections to biological learning through dopamine. Q-learning is also one of the most common frameworks for deep reinforcement learning. Citable link for this video: https://doi.org/10.52843/cassyni.ss11hp 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-Science-Engineering-Learning-Dynamical/dp/1108422098/ Brunton Website: eigensteve.com This video was produced at the University of Washington