We will provide an overview of a book in preparation, which will provide a synthesis of optimization/control and artificial intelligence methodologies as they relate to sequential decision problems. In particular, we consider large and challenging multistage problems, which can be solved in principle by dynamic programming, 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 neuro-dynamic programming. Our subject has benefited greatly from the interplay of ideas from optimal control and from artificial intelligence. One of the aims of the book is to explore the common boundary between these two fields and to form a bridge that is accessible by workers with a background in either field.
Dr. Bertsekas has held faculty positions in several universities, including Stanford University (1971-1974) and the University of Illinois, Urbana (1974-1979). Since 1979 he has been teaching at the Electrical Engineering and Computer Science Department of the Massachusetts Institute of Technology, where he is currently McAfee Professor of Engineering.
Professor Bertsekas was awarded the INFORMS 1997 Prize for Research Excellence in the Interface Between Operations Research and Computer Science for his book "Neuro-Dynamic Programming" (co-authored with John Tsitsiklis), the 2000 Greek National Award for Operations Research, the 2001 ACC John R. Ragazzini Education Award, the 2009 INFORMS Expository Writing Award, the 2014 ACC Richard E. Bellman Control Heritage Award, the 2014 INFORMS Khachiyan Prize, and the SIAM/MOS 2015 George B. Dantzig Prize, and the 2018 INFORMS John von Neumann Theory Prize (jointly with John Tsitsiklis), for the contributions of the research monographs "Parallel and Distributed Computation" and "Neuro-Dynamic Programming". In 2001, he was elected to the United States National Academy of Engineering.
Dr. Bertsekas' recent books are "Convex Optimization Algorithms" (2015), "Nonlinear Programming" (3rd edition, 2016), "Dynamic Programming and Optimal Control" (4th edition, 2017), and "Abstract Dynamic Programming" (2nd edition, 2018), all published by Athena Scientific. He is currently finalizing a book on "Reinforcement Learning and Optimal Control", which aims to bridge the optimization/control and artificial intelligence methodologies as they relate to approximate dynamic programming.
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