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Simulation-Based Algorithms for Markov Decision Processes (Communications and Control Engineering)

Chang, Hyeong Soo, Hu, Jiaqiao, Fu, Michael C., Marcus, Steven I.

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Description for Simulation-Based Algorithms for Markov Decision Processes (Communications and Control Engineering) Hardcover. The updated 2nd edition of this book covers MDPs in constrained settings and with uncertain transition properties; approximation stochastic annealing, a population-based on-line simulation-based algorithm; game-theoretic method for solving MDPs and more. Series: Communications and Control Engineering. Num Pages: 246 pages, 48 black & white illustrations, 1 colour illustrations, 12 black & white tables, biograph. BIC Classification: GPFC; KJT; PBT; TJFM; UMB. Category: (P) Professional & Vocational. Dimension: 235 x 155 x 18. Weight in Grams: 550.
Markov decision process (MDP) models are widely used for modeling sequential decision-making problems that arise in engineering, economics, computer science, and the social sciences. Many real-world problems modeled by MDPs have huge state and/or action spaces, giving an opening to the curse of dimensionality and so making practical solution of the resulting models intractable. In other cases, the system of interest is too complex to allow explicit specification of some of the MDP model parameters, but simulation samples are readily available (e.g., for random transitions and costs). For these settings, various sampling and population-based algorithms have been developed to overcome ... Read more

Product Details

Format

Hardback

Publication date

2013

Publisher

Springer

Condition

New

Series

Communications and Control Engineering

Number of Pages

229

Place of Publication

England, United Kingdom

ISBN

9781447150213

SKU

V9781447150213

Shipping Time

Usually ships in 15 to 20 working days

Ref

99-15

About Chang, Hyeong Soo, Hu, Jiaqiao, Fu, Michael C., Marcus, Steven I.

Hyeong Soo Chang (SM’07 of the IEEE, Member of INFORMS) received the B.S. and M.S. degrees in electrical engineering and the Ph.D. degree in electrical and computer engineering, all from Purdue University,West Lafayette, IN, in 1994, 1996, and 2001, respectively. Since 2003, he has been with the Department of Computer Science and Engineering, Sogang University, Seoul, Korea, where he is ... Read morenow an Associate Professor. He has about 30 journal papers in the area of MDPs and related areas. His main research interests include Markov decision processes, Markov games, computational learning theory, computational intelligence, and stochastic optimization. He currently serves as an Associate Editor for the IEEE Transactions on Automatic Control. Jiaqiao Hu (M’11 of the IEEE, Member of INFORMS) received the B.S. degree in automation from Shanghai Jiao Tong University, Shanghai, China, in 1997, the M.S. degree in applied mathematics from the University of Maryland, Baltimore County, in 2001, and the Ph.D. degree in electrical engineering from the University of Maryland, College Park, in 2006. Since 2006, he has been with the Department of Applied Mathematics and Statistics, State University of New York, Stony Brook, where he is currently an Assistant Professor Markov decision processes, simulation-based optimization, global optimization, applied probability, and stochastic modeling and analysis. Michael Fu (Fellow of the IEEE, Member of INFORMS) received his Ph.D. and M.S degrees in applied mathematics from Harvard University in 1989 and 1986, respectively. He received S.B. and S.M. degrees in electrical engineering and an S.B. degree in mathematics from the Massachusetts Institute of Technology in 1985. Since 1989, he has been at the University of Maryland, College Park, in the College of Business and Management. He was the Simulation Area Editor for Operations and is an Associate Editor for Management Science, and has served on the Editorial Boards ofthe INFORMS Journal on Computing, Production and Operations Management and IIE Transactions. He was on the program committee for the Spring 1996 INFORMS National Meeting, in charge of contributed papers. In 1995, he received the Maryland Business School's annual Allen J. Krowe Award for Teaching Excellence. He is the co-author (with Jian-Qiang Hu) of the book, Conditional Monte Carlo: Gradient Estimation and Optimization Applications (0-7923-9873-4, 1997), which received the 1998 INFORMS College on Simulation Outstanding Publication Award. Other awards include the 1999 IIE Operations Research Division Award and a 1998 IIE Transactions Best Paper Award. In 2002, he received ISR's Outstanding Systems Engineering Faculty Award. He currently serves as a director of National Science Foundation Operations Research Program. Dr. Fu's research interests lie in the areas of stochastic derivative estimation and simulation optimization of discrete-event systems, particularly with applications towards manufacturing systems, inventory control, and the pricing of financial derivatives. Steven I. Marcus (Fellow of the IEEE, Fellow of SIAM, Member of INFORMS) received his Ph.D. and S.M. from the Massachusetts Institute of Technology in 1975 and 1972, respectively. He received a B.A. from Rice University in 1971. From 1975 to 1991, he was with the Department of Electrical and Computer Engineering at the University of Texas at Austin, where he was the L.B. (Preach) Meaders Professor in Engineering. He was Associate Chairman of the Department during the period 1984-89. In 1991, he joined the University of Maryland, College Park, where he was Director of the Institute for Systems Research until 1996. He is currently a Professor in the Electrical Engineering Department and the Institute for Systems Research. He has served as an Editor of the SIAM Journal on Control and Optimization, and Associate Editor of Mathematics of Control, Signals, and Systems, Journal on Discrete Event Dynamic Systems, and Acta Applicandae Mathematicae. He has authored or co-authored more than 100 articles, conference proceedings, and book chapters. Dr. Marcus's research interests lie in the areas of control and systems engineering, analysis and control of stochastic systems, Markov decision processes, stochastic and adaptive control, learning, fault detection, and discrete event systems, with applications in manufacturing, acoustics, and communication networks. Show Less

Reviews for Simulation-Based Algorithms for Markov Decision Processes (Communications and Control Engineering)

From the book reviews: “The book consists of five chapters. … This well-written book is addressed to researchers in MDPs and applied modeling with an interests in numerical computations, but the book is also accessible to graduate students in operation research, computer science, and economics. The authors gives many pseudocodes of algorithms, numerical examples, algorithms convergence analysis and bibliographical ... Read more

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Simulation-Based Algorithms for Markov Decision Processes (Communications and Control Engineering)

Chang, Hyeong Soo, Hu, Jiaqiao, Fu, Michael C., Marcus, Steven I.

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