Explore an overview of deep reinforcement learning methods in this 25-minute lecture. Dive into deep Q-learning, actor-critic methods, deep policy networks, and policy gradient optimization algorithms. Learn about the latest advancements in reinforcement learning as part of a comprehensive series based on the new Chapter 11 from the 2nd edition of "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Brunton and Kutz. Gain insights into the fundamental concepts of reinforcement learning, including deep policy networks, policy gradient optimization, and advantage actor-critic networks. Access additional resources, including the book's website, PDF, and Amazon link, to further enhance your understanding of these cutting-edge machine learning techniques.
Overview
Syllabus
Intro
REINFORCEMENT LEARNING
DEEP POLICY NETWORK
POLICY GRADIENT OPTIMIZATION
DEEP Q-LEARNING
ADVANTAGE ACTOR-CRITIC NETWORK
Taught by
Steve Brunton
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