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Handbook of Markov Decision Processes - Methods and Applications (Paperback, Softcover reprint of the original 1st ed. 2002)
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Handbook of Markov Decision Processes - Methods and Applications (Paperback, Softcover reprint of the original 1st ed. 2002)
Series: International Series in Operations Research & Management Science, 40
Expected to ship within 10 - 15 working days
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Eugene A. Feinberg Adam Shwartz This volume deals with the theory
of Markov Decision Processes (MDPs) and their applications. Each
chapter was written by a leading expert in the re spective area.
The papers cover major research areas and methodologies, and
discuss open questions and future research directions. The papers
can be read independently, with the basic notation and concepts
ofSection 1.2. Most chap ters should be accessible by graduate or
advanced undergraduate students in fields of operations research,
electrical engineering, and computer science. 1.1 AN OVERVIEW OF
MARKOV DECISION PROCESSES The theory of Markov Decision
Processes-also known under several other names including sequential
stochastic optimization, discrete-time stochastic control, and
stochastic dynamic programming-studiessequential optimization
ofdiscrete time stochastic systems. The basic object is a
discrete-time stochas tic system whose transition mechanism can be
controlled over time. Each control policy defines the stochastic
process and values of objective functions associated with this
process. The goal is to select a "good" control policy. In real
life, decisions that humans and computers make on all levels
usually have two types ofimpacts: (i) they cost orsavetime, money,
or other resources, or they bring revenues, as well as (ii) they
have an impact on the future, by influencing the dynamics. In many
situations, decisions with the largest immediate profit may not be
good in view offuture events. MDPs model this paradigm and provide
results on the structure and existence of good policies and on
methods for their calculation."
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