Adaptive Markov Control Processes

Name: Adaptive Markov Control Processes
Brand: Springer-Verlag New York Inc.
SKU: 9780387969664
Price: 240.46 PLN
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Autor: Onesimo Hernandez-Lerma

Język:

Oprawa: Twarda

Wydawca: Springer-Verlag New York Inc.

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This book is concerned with a class of discrete-time stochastic control processes known as controlle...

Informacje o książce

Autor

Onesimo Hernandez-Lerma

Język

Angielski

Oprawa

Książka - Twarda

strony

148

EAN

9780387969664

ISBN

0387969667

Enbook ID

01385009

Wydawca

Springer-Verlag New York Inc.

Waga

930

Wymiary

155 x 235 x 16

Pełny opis

This book is concerned with a class of discrete-time stochastic control processes known as controlled Markov processes (CMP's), also known as Markov decision processes or Markov dynamic programs. Starting in the mid-1950swith Richard Bellman, many contributions to CMP's have been made, and applications to engineering, statistics and operations research, among other areas, have also been developed. The purpose of this book is to present some recent developments on the theory of adaptive CMP's, i. e. , CMP's that depend on unknown parameters. Thus at each decision time, the controller or decision-maker must estimate the true parameter values, and then adapt the control actions to the estimated values. We do not intend to describe all aspects of stochastic adaptive control; rather, the selection of material reflects our own research interests. The prerequisite for this book is a knowledgeof real analysis and prob ability theory at the level of, say, Ash (1972) or Royden (1968), but no previous knowledge of control or decision processes is required. The pre sentation, on the other hand, is meant to beself-contained,in the sensethat whenever a result from analysisor probability is used, it is usually stated in full and references are supplied for further discussion, if necessary. Several appendices are provided for this purpose. The material is divided into six chapters. Chapter 1 contains the basic definitions about the stochastic control problems we are interested in; a brief description of some applications is also provided.

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