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 Title Reinforcement Learning and Optimal Control
 Author(s) Dimitri P. Bertsekas
 Publisher: Athena Scientific 2019
 Hardcover/Paperback: 276 pages
 eBook: PDF files
 Language: English
 ISBN10: N/A
 ISBN13: N/A
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Book Description
Reinforcement Learning (RL), one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment.
The purpose of the book is to consider large and challenging multistage decision problems, which can be solved in principle by dynamic programming and optimal control, but their exact solution is computationally intractable. We discuss solution methods that rely on approximations to produce suboptimal policies with adequate performance. These methods are collectively referred to as reinforcement learning, and also by alternative names such as approximate dynamic programming, and neurodynamic programming.
The mathematical style of the book is somewhat different from the author's dynamic programming books, and the neurodynamic programming monograph, written jointly with John Tsitsiklis. We rely more on intuitive explanations and less on proofbased insights. Still we provide a rigorous short account of the theory of finite and infinite horizon dynamic programming, and some basic approximation methods, in an appendix. For this we require a modest mathematical background: calculus, elementary probability, and a minimal use of matrixvector algebra.
About the Authors Dimitri P. Bertsekas is an applied mathematician, electrical engineer, and computer scientist, and a professor at the department of Electrical Engineering and Computer Science in School of Engineering at the Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts.