Finite approximations in discrete-time stochastic control :
General Material Designation
[Book]
Other Title Information
quantized models and asymptotic optimality /
First Statement of Responsibility
Naci Saldi, Tamás Linder, Serdar Yüksel.
.PUBLICATION, DISTRIBUTION, ETC
Place of Publication, Distribution, etc.
Cham, Switzerland :
Name of Publisher, Distributor, etc.
Birkhäuser,
Date of Publication, Distribution, etc.
[2018]
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
1 online resource (vii, 198 pages) :
Other Physical Details
illustrations
SERIES
Series Title
Systems & control: foundations & applications,
ISSN of Series
2324-9749
INTERNAL BIBLIOGRAPHIES/INDEXES NOTE
Text of Note
Includes bibliographical references and index.
CONTENTS NOTE
Text of Note
Introduction and Summary -- Part I: Finite Model Approximations in Stochastic Control -- Prelude to Part I -- Finite Action Approximation of Markov Decision Processes -- Finite-State Approximation of Markov Decision Processes -- Approximations for Partially Observed Markov Decision Processes -- Approximations for Constrained Markov Decision Problems -- Part II: Finite Model Approximations in Decentralized Stochastic Control -- Prelude to Part II -- Finite Model Approximations in Decentralized Stochastic Control -- Asymptotic Optimality of Finite Models for Specific Systems -- Index -- References.
0
SUMMARY OR ABSTRACT
Text of Note
In a unified form, this monograph presents fundamental results on the approximation of centralized and decentralized stochastic control problems, with uncountable state, measurement, and action spaces. It demonstrates how quantization provides a system-independent and constructive method for the reduction of a system with Borel spaces to one with finite state, measurement, and action spaces. In addition to this constructive view, the book considers both the information transmission approach for discretization of actions, and the computational approach for discretization of states and actions. Part I of the text discusses Markov decision processes and their finite-state or finite-action approximations, while Part II builds from there to finite approximations in decentralized stochastic control problems. This volume is perfect for researchers and graduate students interested in stochastic controls. With the tools presented, readers will be able to establish the convergence of approximation models to original models and the methods are general enough that researchers can build corresponding approximation results, typically with no additional assumptions.--
ACQUISITION INFORMATION NOTE
Source for Acquisition/Subscription Address
Springer Nature
Stock Number
com.springer.onix.9783319790336
OTHER EDITION IN ANOTHER MEDIUM
Title
Finite approximations in discrete-time stochastic control.