Constrained Markov Decision Processes

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Constrained Markov Decision Processes

Author : Eitan Altman
Publisher : CRC Press
Page : 260 pages
File Size : 48,9 Mb
Release : 1999-03-30
Category : Mathematics
ISBN : 0849303826

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Constrained Markov Decision Processes by Eitan Altman Pdf

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. The first part explains the theory for the finite state space. The author characterizes the set of achievable expected occupation measures as well as performance vectors, and identifies simple classes of policies among which optimal policies exist. This allows the reduction of the original dynamic into a linear program. A Lagranian approach is then used to derive the dual linear program using dynamic programming techniques. In the second part, these results are extended to the infinite state space and action spaces. The author provides two frameworks: the case where costs are bounded below and the contracting framework. The third part builds upon the results of the first two parts and examines asymptotical results of the convergence of both the value and the policies in the time horizon and in the discount factor. Finally, several state truncation algorithms that enable the approximation of the solution of the original control problem via finite linear programs are given.

Constrained Markov Decision Processes

Author : Eitan Altman
Publisher : Routledge
Page : 256 pages
File Size : 51,7 Mb
Release : 2021-12-17
Category : Mathematics
ISBN : 9781351458245

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Constrained Markov Decision Processes by Eitan Altman Pdf

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.

Constrained Markov Decision Processes

Author : E. Altman
Publisher : Unknown
Page : 115 pages
File Size : 45,7 Mb
Release : 1995
Category : Electronic
ISBN : OCLC:897847191

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Constrained Markov Decision Processes by E. Altman Pdf

Continuous-Time Markov Decision Processes

Author : Xianping Guo,Onésimo Hernández-Lerma
Publisher : Springer Science & Business Media
Page : 240 pages
File Size : 40,7 Mb
Release : 2009-09-18
Category : Mathematics
ISBN : 9783642025471

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Continuous-Time Markov Decision Processes by Xianping Guo,Onésimo Hernández-Lerma Pdf

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.

Examples in Markov Decision Processes

Author : A. B. Piunovskiy
Publisher : World Scientific
Page : 308 pages
File Size : 47,6 Mb
Release : 2013
Category : Mathematics
ISBN : 9781848167933

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Examples in Markov Decision Processes by A. B. Piunovskiy Pdf

This invaluable book provides approximately eighty examples illustrating the theory of controlled discrete-time Markov processes. Except for applications of the theory to real-life problems like stock exchange, queues, gambling, optimal search etc, the main attention is paid to counter-intuitive, unexpected properties of optimization problems. Such examples illustrate the importance of conditions imposed in the theorems on Markov Decision Processes. Many of the examples are based upon examples published earlier in journal articles or textbooks while several other examples are new. The aim was to collect them together in one reference book which should be considered as a complement to existing monographs on Markov decision processes. The book is self-contained and unified in presentation. The main theoretical statements and constructions are provided, and particular examples can be read independently of others. Examples in Markov Decision Processes is an essential source of reference for mathematicians and all those who apply the optimal control theory to practical purposes. When studying or using mathematical methods, the researcher must understand what can happen if some of the conditions imposed in rigorous theorems are not satisfied. Many examples confirming the importance of such conditions were published in different journal articles which are often difficult to find. This book brings together examples based upon such sources, along with several new ones. In addition, it indicates the areas where Markov decision processes can be used. Active researchers can refer to this book on applicability of mathematical methods and theorems. It is also suitable reading for graduate and research students where they will better understand the theory.

Handbook of Markov Decision Processes

Author : Eugene A. Feinberg,Adam Shwartz
Publisher : Springer Science & Business Media
Page : 560 pages
File Size : 45,5 Mb
Release : 2012-12-06
Category : Business & Economics
ISBN : 9781461508052

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Handbook of Markov Decision Processes by Eugene A. Feinberg,Adam Shwartz Pdf

Eugene A. Feinberg Adam Shwartz This volume deals with the theory of Markov Decision Processes (MDPs) and their applications. Each chapter was written by a leading expert in the re spective area. The papers cover major research areas and methodologies, and discuss open questions and future research directions. The papers can be read independently, with the basic notation and concepts ofSection 1.2. Most chap ters should be accessible by graduate or advanced undergraduate students in fields of operations research, electrical engineering, and computer science. 1.1 AN OVERVIEW OF MARKOV DECISION PROCESSES The theory of Markov Decision Processes-also known under several other names including sequential stochastic optimization, discrete-time stochastic control, and stochastic dynamic programming-studiessequential optimization ofdiscrete time stochastic systems. The basic object is a discrete-time stochas tic system whose transition mechanism can be controlled over time. Each control policy defines the stochastic process and values of objective functions associated with this process. The goal is to select a "good" control policy. In real life, decisions that humans and computers make on all levels usually have two types ofimpacts: (i) they cost orsavetime, money, or other resources, or they bring revenues, as well as (ii) they have an impact on the future, by influencing the dynamics. In many situations, decisions with the largest immediate profit may not be good in view offuture events. MDPs model this paradigm and provide results on the structure and existence of good policies and on methods for their calculation.

Markov Decision Processes in Artificial Intelligence

Author : Olivier Sigaud,Olivier Buffet
Publisher : John Wiley & Sons
Page : 367 pages
File Size : 45,5 Mb
Release : 2013-03-04
Category : Technology & Engineering
ISBN : 9781118620106

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Markov Decision Processes in Artificial Intelligence by Olivier Sigaud,Olivier Buffet Pdf

Markov Decision Processes (MDPs) are a mathematical framework for modeling sequential decision problems under uncertainty as well as reinforcement learning problems. Written by experts in the field, this book provides a global view of current research using MDPs in artificial intelligence. It starts with an introductory presentation of the fundamental aspects of MDPs (planning in MDPs, reinforcement learning, partially observable MDPs, Markov games and the use of non-classical criteria). It then presents more advanced research trends in the field and gives some concrete examples using illustrative real life applications.

Markov Decision Processes

Author : Martin L. Puterman
Publisher : John Wiley & Sons
Page : 684 pages
File Size : 53,8 Mb
Release : 2014-08-28
Category : Mathematics
ISBN : 9781118625873

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Markov Decision Processes by Martin L. Puterman Pdf

The Wiley-Interscience Paperback Series consists of selected booksthat have been made more accessible to consumers in an effort toincrease global appeal and general circulation. With these newunabridged softcover volumes, Wiley hopes to extend the lives ofthese works by making them available to future generations ofstatisticians, mathematicians, and scientists. "This text is unique in bringing together so many resultshitherto found only in part in other texts and papers. . . . Thetext is fairly self-contained, inclusive of some basic mathematicalresults needed, and provides a rich diet of examples, applications,and exercises. The bibliographical material at the end of eachchapter is excellent, not only from a historical perspective, butbecause it is valuable for researchers in acquiring a goodperspective of the MDP research potential." —Zentralblatt fur Mathematik ". . . it is of great value to advanced-level students,researchers, and professional practitioners of this field to havenow a complete volume (with more than 600 pages) devoted to thistopic. . . . Markov Decision Processes: Discrete Stochastic DynamicProgramming represents an up-to-date, unified, and rigoroustreatment of theoretical and computational aspects of discrete-timeMarkov decision processes." —Journal of the American Statistical Association

Partially Observed Markov Decision Processes

Author : Vikram Krishnamurthy
Publisher : Cambridge University Press
Page : 128 pages
File Size : 51,5 Mb
Release : 2016-03-21
Category : Technology & Engineering
ISBN : 9781316594780

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Partially Observed Markov Decision Processes by Vikram Krishnamurthy Pdf

Covering formulation, algorithms, and structural results, and linking theory to real-world applications in controlled sensing (including social learning, adaptive radars and sequential detection), this book focuses on the conceptual foundations of partially observed Markov decision processes (POMDPs). It emphasizes structural results in stochastic dynamic programming, enabling graduate students and researchers in engineering, operations research, and economics to understand the underlying unifying themes without getting weighed down by mathematical technicalities. Bringing together research from across the literature, the book provides an introduction to nonlinear filtering followed by a systematic development of stochastic dynamic programming, lattice programming and reinforcement learning for POMDPs. Questions addressed in the book include: when does a POMDP have a threshold optimal policy? When are myopic policies optimal? How do local and global decision makers interact in adaptive decision making in multi-agent social learning where there is herding and data incest? And how can sophisticated radars and sensors adapt their sensing in real time?

Markov Decision Processes with Their Applications

Author : Qiying Hu,Wuyi Yue
Publisher : Springer Science & Business Media
Page : 305 pages
File Size : 51,8 Mb
Release : 2007-09-14
Category : Business & Economics
ISBN : 9780387369518

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Markov Decision Processes with Their Applications by Qiying Hu,Wuyi Yue Pdf

Put together by two top researchers in the Far East, this text examines Markov Decision Processes - also called stochastic dynamic programming - and their applications in the optimal control of discrete event systems, optimal replacement, and optimal allocations in sequential online auctions. This dynamic new book offers fresh applications of MDPs in areas such as the control of discrete event systems and the optimal allocations in sequential online auctions.

Finite Approximations in Discrete-Time Stochastic Control

Author : Naci Saldi,Tamás Linder,Serdar Yüksel
Publisher : Birkhäuser
Page : 198 pages
File Size : 46,7 Mb
Release : 2018-05-11
Category : Mathematics
ISBN : 9783319790336

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Finite Approximations in Discrete-Time Stochastic Control by Naci Saldi,Tamás Linder,Serdar Yüksel Pdf

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.

Markov Processes and Controlled Markov Chains

Author : Zhenting Hou,Jerzy A. Filar,Anyue Chen
Publisher : Springer Science & Business Media
Page : 536 pages
File Size : 40,5 Mb
Release : 2002-09-30
Category : Business & Economics
ISBN : 1402008031

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Markov Processes and Controlled Markov Chains by Zhenting Hou,Jerzy A. Filar,Anyue Chen Pdf

The general theory of stochastic processes and the more specialized theory of Markov processes evolved enormously in the second half of the last century. In parallel, the theory of controlled Markov chains (or Markov decision processes) was being pioneered by control engineers and operations researchers. Researchers in Markov processes and controlled Markov chains have been, for a long time, aware of the synergies between these two subject areas. However, this may be the first volume dedicated to highlighting these synergies and, almost certainly, it is the first volume that emphasizes the contributions of the vibrant and growing Chinese school of probability. The chapters that appear in this book reflect both the maturity and the vitality of modern day Markov processes and controlled Markov chains. They also will provide an opportunity to trace the connections that have emerged between the work done by members of the Chinese school of probability and the work done by the European, US, Central and South American and Asian scholars.

2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT)

Author : IEEE Staff
Publisher : Unknown
Page : 128 pages
File Size : 48,8 Mb
Release : 2019-07-06
Category : Electronic
ISBN : 1538659077

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2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) by IEEE Staff Pdf

The 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) aims to provide a forum that brings together International researchers from academia and practitioners in the industry to meet and exchange ideas and recent research work on all aspects of Information and Communication Technologies including Computing, communication, IOT, LiDAR, Image Analysis, wireless communication and other new technologies

Stochastic Recursive Algorithms for Optimization

Author : S. Bhatnagar,H.L. Prasad,L.A. Prashanth
Publisher : Springer
Page : 302 pages
File Size : 53,6 Mb
Release : 2012-08-11
Category : Technology & Engineering
ISBN : 9781447142850

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Stochastic Recursive Algorithms for Optimization by S. Bhatnagar,H.L. Prasad,L.A. Prashanth Pdf

Stochastic Recursive Algorithms for Optimization presents algorithms for constrained and unconstrained optimization and for reinforcement learning. Efficient perturbation approaches form a thread unifying all the algorithms considered. Simultaneous perturbation stochastic approximation and smooth fractional estimators for gradient- and Hessian-based methods are presented. These algorithms: • are easily implemented; • do not require an explicit system model; and • work with real or simulated data. Chapters on their application in service systems, vehicular traffic control and communications networks illustrate this point. The book is self-contained with necessary mathematical results placed in an appendix. The text provides easy-to-use, off-the-shelf algorithms that are given detailed mathematical treatment so the material presented will be of significant interest to practitioners, academic researchers and graduate students alike. The breadth of applications makes the book appropriate for reader from similarly diverse backgrounds: workers in relevant areas of computer science, control engineering, management science, applied mathematics, industrial engineering and operations research will find the content of value.

Robust Optimization

Author : Aharon Ben-Tal,Laurent El Ghaoui,Arkadi Nemirovski
Publisher : Princeton University Press
Page : 576 pages
File Size : 48,7 Mb
Release : 2009-08-10
Category : Mathematics
ISBN : 9781400831050

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Robust Optimization by Aharon Ben-Tal,Laurent El Ghaoui,Arkadi Nemirovski Pdf

Robust optimization is still a relatively new approach to optimization problems affected by uncertainty, but it has already proved so useful in real applications that it is difficult to tackle such problems today without considering this powerful methodology. Written by the principal developers of robust optimization, and describing the main achievements of a decade of research, this is the first book to provide a comprehensive and up-to-date account of the subject. Robust optimization is designed to meet some major challenges associated with uncertainty-affected optimization problems: to operate under lack of full information on the nature of uncertainty; to model the problem in a form that can be solved efficiently; and to provide guarantees about the performance of the solution. The book starts with a relatively simple treatment of uncertain linear programming, proceeding with a deep analysis of the interconnections between the construction of appropriate uncertainty sets and the classical chance constraints (probabilistic) approach. It then develops the robust optimization theory for uncertain conic quadratic and semidefinite optimization problems and dynamic (multistage) problems. The theory is supported by numerous examples and computational illustrations. An essential book for anyone working on optimization and decision making under uncertainty, Robust Optimization also makes an ideal graduate textbook on the subject.