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Discrete-Time Markov Control Processes - Basic Optimality Criteria (Paperback, Softcover reprint of the original 1st ed. 1996)
Loot Price: R4,466
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Discrete-Time Markov Control Processes - Basic Optimality Criteria (Paperback, Softcover reprint of the original 1st ed. 1996)
Series: Stochastic Modelling and Applied Probability, 30
Expected to ship within 10 - 15 working days
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This book presents the first part of a planned two-volume series
devoted to a systematic exposition of some recent developments in
the theory of discrete-time Markov control processes (MCPs).
Interest is mainly confined to MCPs with Borel state and control
(or action) spaces, and possibly unbounded costs and noncompact
control constraint sets. MCPs are a class of stochastic control
problems, also known as Markov decision processes, controlled
Markov processes, or stochastic dynamic pro grams; sometimes,
particularly when the state space is a countable set, they are also
called Markov decision (or controlled Markov) chains. Regardless of
the name used, MCPs appear in many fields, for example,
engineering, economics, operations research, statistics, renewable
and nonrenewable re source management, (control of) epidemics, etc.
However, most of the lit erature (say, at least 90%) is
concentrated on MCPs for which (a) the state space is a countable
set, and/or (b) the costs-per-stage are bounded, and/or (c) the
control constraint sets are compact. But curiously enough, the most
widely used control model in engineering and economics--namely the
LQ (Linear system/Quadratic cost) model-satisfies none of these
conditions. Moreover, when dealing with "partially observable"
systems) a standard approach is to transform them into equivalent
"completely observable" sys tems in a larger state space (in fact,
a space of probability measures), which is uncountable even if the
original state process is finite-valued.
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