The theory of Markov Decision Processes - also known under several
other names including sequential stochastic optimization,
discrete-time stochastic control, and stochastic dynamic
programming - studies sequential optimization of discrete time
stochastic systems. Fundamentally, this is a methodology that
examines and analyzes a discrete-time stochastic 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. Its objective is to select
a "good" control policy. In real life, decisions that humans and
computers make on all levels usually have two types of impacts: (i)
they cost or save time, 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 of future events.
Markov Decision Processes (MDPs) model this paradigm and provide
results on the structure and existence of good policies and on
methods for their calculations. MDPs are attractive to many
researchers because they are important both from the practical and
the intellectual points of view. MDPs provide tools for the
solution of important real-life problems. In particular, many
business and engineering applications use MDP models. Analysis of
various problems arising in MDPs leads to a large variety of
interesting mathematical and computational problems. Accordingly,
the Handbook of Markov Decision Processes is split into three
parts: Part I deals with models with finite state and action spaces
and Part II deals with infinite state problems, and Part
IIIexamines specific applications. Individual chapters are written
by leading experts on the subject.
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