Synopses & Reviews
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.
Synopsis
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.
About the Author
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 now 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.
Table of Contents
Markov Decision Processes.- Multi-stage Adaptive Sampling Algorithms.- Population-based Evolutionary Approaches.- Model Reference Adaptive Search.- On-line Control Methods via Simulation.- Game-theoretic Methods via Simulation.