Reinforcement Learning Series: Overview of Methods

Автор: Steve Brunton

Загружено: 3 янв. 2022 г.

Просмотров: 128 485 просмотров

Описание:

This video introduces the variety of methods for model-based and model-free reinforcement learning, including: dynamic programming, value and policy iteration, Q-learning, deep RL, TD-learning, SARSA, policy gradient optimization, among others.

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

This is the overview 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
RL Chapter: https://faculty.washington.edu/sbrunt...

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

Brunton Website: eigensteve.com

This video was produced at the University of Washington

Reinforcement Learning Series: Overview of Methods

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