Constrained Markov Decision Processes

This book provides a unified approach for the study of constrained Markov decision processes with a finite state space and unbounded costs. Unlike the single controller case considered in many other books, the author considers a single controller with several objectives, such as minimizing delays and loss, probabilities, and maximization of throughputs. It is desirable to design a controller that minimizes one cost objective, subject to inequality constraints on other cost objectives. This framework describes dynamic decision problems arising frequently in many engineering fields. A thorough overview of these applications is presented in the introduction.
The book is then divided into three sections that build upon each other.

1101525018
Constrained Markov Decision Processes

This book provides a unified approach for the study of constrained Markov decision processes with a finite state space and unbounded costs. Unlike the single controller case considered in many other books, the author considers a single controller with several objectives, such as minimizing delays and loss, probabilities, and maximization of throughputs. It is desirable to design a controller that minimizes one cost objective, subject to inequality constraints on other cost objectives. This framework describes dynamic decision problems arising frequently in many engineering fields. A thorough overview of these applications is presented in the introduction.
The book is then divided into three sections that build upon each other.

172.99 In Stock
Constrained Markov Decision Processes

Constrained Markov Decision Processes

by Eitan Altman
Constrained Markov Decision Processes

Constrained Markov Decision Processes

by Eitan Altman

eBook

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Overview

This book provides a unified approach for the study of constrained Markov decision processes with a finite state space and unbounded costs. Unlike the single controller case considered in many other books, the author considers a single controller with several objectives, such as minimizing delays and loss, probabilities, and maximization of throughputs. It is desirable to design a controller that minimizes one cost objective, subject to inequality constraints on other cost objectives. This framework describes dynamic decision problems arising frequently in many engineering fields. A thorough overview of these applications is presented in the introduction.
The book is then divided into three sections that build upon each other.


Product Details

ISBN-13: 9781351458238
Publisher: CRC Press
Publication date: 12/24/2021
Series: Stochastic Modeling Series
Sold by: Barnes & Noble
Format: eBook
Pages: 256
File size: 12 MB
Note: This product may take a few minutes to download.

About the Author

Eitan Altman (INRIA, Valbonne, Cedex, France)

Table of Contents

Examples of Constrained Dynamic Control Problems, On Solution Approaches for CMDPs with Expected Costs, Other Types of CMDPs, Cost Criteria and Assumptions, The Convex Analytical Approach and Occupation Measures, Linear Programming and Lagrangian Approach for CMDPs, About the Methodology, The Structure of the Book, PART ONE: FINITE MDPS, MARKOV DECISION PROCESSES, THE DISCOUNTED COST, THE EXPECTED AVERAGE COST, FLOW AND SERVICE CONTROL IN A SINGLE-SERVER QUEUE, PART TWO: INFINITE MDPS MDPS WITH INFINITE STATE AND ACTION SPACES, THE TOTAL COST: CLASSIFICATION OF MDPS, THE TOTAL COST: OCCUPATION MEASURES AND THE PRIMAL LP, THE TOTAL COST: DYNAMIC AND LINEAR PROGRAMMING, THE DISCOUNTED COST, THE EXPECTED AVERAGE COST, EXPECTED AVERAGE COST: DYNAMIC PROGRAMMING AND LP, PART THREE: ASYMPTOTIC METHODS AND APPROXIMATIONS SENSITIVITY ANALYSIS, CONVERGENCE OF DISCOUNTED CONSTRAINED MDPS, CONVERGENCE AS THE HORIZON TENDS TO INFINITY, STATE TRUNCATION AND APPROXIMATION

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