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Control systems and reinforcement learning

Control systems and reinforcement learning / Sean Meyn.
9781009051873 (ebook)
9781316511961 (hardback)
Cambridge : Cambridge University Press, 2022.
Physical Description
1 online resource (xv, 435 pages) : digital, PDF file(s).
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Access is available to the Yale community.
Title from publisher's bibliographic system (viewed on 07 Apr 2022).
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A high school student can create deep Q-learning code to control her robot, without any understanding of the meaning of 'deep' or 'Q', or why the code sometimes fails. This book is designed to explain the science behind reinforcement learning and optimal control in a way that is accessible to students with a background in calculus and matrix algebra. A unique focus is algorithm design to obtain the fastest possible speed of convergence for learning algorithms, along with insight into why reinforcement learning sometimes fails. Advanced stochastic process theory is avoided at the start by substituting random exploration with more intuitive deterministic probing for learning. Once these ideas are understood, it is not difficult to master techniques rooted in stochastic control. These topics are covered in the second part of the book, starting with Markov chain theory and ending with a fresh look at actor-critic methods for reinforcement learning.
Variant and related titles
Cambridge core frontlist 2022.
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Added to Catalog
June 27, 2022

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