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9783642025464

Continuous-Time Markov Decision Processes

by ;
  • ISBN13:

    9783642025464

  • ISBN10:

    3642025463

  • Format: Hardcover
  • Copyright: 2009-10-01
  • Publisher: Springer Nature
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Summary

Continuous-time Markov decision processes (MDPs), also known as controlled Markov chains, are used for modeling decision-making problems that arise in operations research (for instance, inventory, manufacturing, and queueing systems), computer science, communications engineering, control of populations (such as fisheries and epidemics), and management science, among many other fields. This volume provides a unified, systematic, self-contained presentation of recent developments on the theory and applications of continuous-time MDPs. The MDPs in this volume include most of the cases that arise in applications, because they allow unbounded transition and reward/cost rates. Much of the material appears for the first time in book form.

Table of Contents

Introduction and Summaryp. 1
Introductionp. 1
Preliminary Examplesp. 1
Summary of the Following Chaptersp. 6
Continuous-Time Markov Decision Processesp. 9
Introductionp. 9
The Control Modelp. 10
Continuous-Time Markov Decision Processesp. 13
Basic Optimality Criteriap. 16
Average Optimality for Finite Modelsp. 19
Introductionp. 19
n-bias Optimality Criteriap. 20
Difference Formulas of n-biasesp. 23
Characterization of n-bias Policiesp. 29
Computation of n-bias Optimal Policiesp. 36
The Policy Iteration Algorithm for Average Optimalityp. 36
The 0-bias Policy Iteration Algorithmp. 39
n-bias Policy Iteration Algorithmsp. 43
The Linear Programming Approachp. 46
Linear Programming for Ergodic Modelsp. 46
Linear Programming for Multichain Modelsp. 49
Notesp. 52
Discount Optimality for Nonnegative Costsp. 55
Introductionp. 55
The Nonnegative Modelp. 55
Preliminariesp. 56
The Discounted Cost Optimality Equationp. 60
Existence of Optimal Policiesp. 63
Approximation Resultsp. 63
The Policy Iteration Approachp. 66
Examplesp. 68
Notesp. 69
Average Optimality for Nonnegative Costsp. 71
Introductionp. 71
The Average-Cost Criterionp. 72
The Minimum Nonnegative Solution Approachp. 73
The Average-Cost Optimality Inequalityp. 76
The Average-Cost Optimality Equationp. 80
Examplesp. 81
Notesp. 84
Discount Optimality for Unbounded Rewardsp. 87
Introductionp. 87
The Discounted-Reward Optimality Equationp. 89
Discount Optimal Stationary Policiesp. 95
A Value Iteration Algorithmp. 98
Examplesp. 98
Notesp. 102
Average Optimality for Unbounded Rewardsp. 105
Introductionp. 105
Exponential Ergodicity Conditionsp. 106
The Existence of AR Optimal Policiesp. 109
The Policy Iteration Algorithmp. 113
Examplesp. 119
Notesp. 124
Average Optimality for Pathwise Rewardsp. 127
Introductionp. 127
The Optimal Control Problemp. 129
Optimality Conditions and Preliminariesp. 129
The Existence of PAR Optimal Policiesp. 131
Policy and Value Iteration Algorithmsp. 138
An Examplep. 139
Notesp. 142
Advanced Optimality Criteriap. 143
Bias and Weakly Overtaking Optimalityp. 143
Sensitive Discount Optimalityp. 147
Blackwell Optimalityp. 159
Notesp. 160
Variance Minimizationp. 163
Introductionp. 163
Preliminariesp. 164
Computation of the Average Variancep. 164
Variance Minimizationp. 170
Examplesp. 171
Notesp. 173
Constrained Optimality for Discount Criteriap. 175
The Model with a Constraintp. 175
Preliminariesp. 177
Proof of Theorem 11.4p. 182
An Examplep. 184
Notesp. 186
Constrained Optimality for Average Criteriap. 187
Average Optimality with a Constraintp. 187
Preliminariesp. 188
Proof of Theorem 12.4p. 192
An Examplep. 192
Notesp. 194
p. 195
Limit Theoremsp. 195
Results from Measure Theoryp. 197
p. 203
Continuous-Time Markov Chainsp. 203
Stationary Distributions and Ergodicityp. 206
p. 209
The Construction of Transition Functionsp. 209
Ergodicity Based on the Q-Matrixp. 214
Dynkin's Formulap. 218
Referencesp. 221
Indexp. 229
Table of Contents provided by Ingram. All Rights Reserved.

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