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Presents new branches for Markov Decision Processes (MDP)
Applies new methodology for MDPs
Offers new applications of MDPs
Shows the validity of the optimality equation and its properties from the definition of the model by reducing the scale of MDP models based on action reduction and state decomposition
Presents two new optimal control problems for discrete event systems
Examines two optimal replacement problems in stochastic environments
Studies continuous time MDPs and semi-Markov decision processes in a semi-Markov environment
Markov decision processes (MDPs), also called stochastic dynamic programming, were first studied in the 1960s. MDPs can be used to model and solve dynamic decision-making problems that are multi-period and occur in stochastic circumstances. There are three basic branches in MDPs: discrete-time MDPs, continuous-time MDPs and semi-Markov decision processes. Starting from these three branches, many generalized MDPs models have been applied to various practical problems. These models include partially observable MDPs, adaptive MDPs, MDPs in stochastic environments, and MDPs with multiple objectives, constraints or imprecise parameters.
Markov Decision Processes With Their Applications examines MDPs and their applications in the optimal control of discrete event systems (DESs), optimal replacement, and optimal allocations in sequential online auctions. The book presents four main topics that are used to study optimal control problems:
*a new methodology for MDPs with discounted total reward criterion;
*transformation of continuous-time MDPs and semi-Markov decision processes into a discrete-time MDPs model, thereby simplifying the application of MDPs;
*MDPs in stochastic environments, which greatly extends the area where MDPs can be applied;
*applications of MDPs in optimal control of discrete event systems, optimal replacement, and optimal allocation in sequential online auctions.
This book is intended for researchers, mathematicians, advanced graduate students, and engineers who are interested in optimal control, operation research, communications, manufacturing, economics, and electronic commerce.
Content Level »Research
Keywords »Markov decision process - Observable - Optimal control - decision making problems - decision processes - discrete event systems - stochastic dynamic programming
Discretetimemarkovdecisionprocesses: Total Reward.- Discretetimemarkovdecisionprocesses: Average Criterion.- Continuous Time Markov Decision Processes.- Semi-Markov Decision Processes.- Markovdecisionprocessesinsemi-Markov Environments.- Optimal control of discrete event systems: I.- Optimal control of discrete event systems: II.- Optimal replacement under stochastic Environments.- Optimalal location in sequential online Auctions.