Finite Approximations in Discrete-Time Stochastic Control: Quantized Models and Asymptotic Optimality (Systems & Control: Foundations & Applications) by Naci Saldi, Tam�s Linder, Serdar Y�ksel: New Hardcover (2018)

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Seller Inventory # 6666-GRD-9783319790329

Bibliographic Details

Title: Finite Approximations in Discrete-Time Stochastic Control: Quantized Models and Asymptotic Optimality (Systems & Control: Foundations & Applications)
Publisher: Birkh�user 2018-05-24
Publication Date: 2018
Language: English
ISBN 10: 3319790323
ISBN 13: 9783319790329
Binding: Hardcover
Condition: New

About this title

Synopsis

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.

From the Back Cover

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.

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