Download or read book Connectedness Conditions for Denumerable State Markov Decision Processes written by L. C. Thomas and published by . This book was released on 1978 with total page 23 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Recurrence Conditions in Denumerable State Markov Decision Processes written by A. Federgruen and published by . This book was released on 1977 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Connectedness Conditions Used in Finite State Markov Decision Processes written by L. C. Thomas and published by . This book was released on 1977 with total page 9 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Handbook of Markov Decision Processes written by Eugene A. Feinberg and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 560 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Markov Decision Processes written by Martin L. Puterman and published by John Wiley & Sons. This book was released on 2014-08-28 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "This text is unique in bringing together so many results hitherto found only in part in other texts and papers. . . . The text is fairly self-contained, inclusive of some basic mathematical results needed, and provides a rich diet of examples, applications, and exercises. The bibliographical material at the end of each chapter is excellent, not only from a historical perspective, but because it is valuable for researchers in acquiring a good perspective 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 have now a complete volume (with more than 600 pages) devoted to this topic. . . . Markov Decision Processes: Discrete Stochastic Dynamic Programming represents an up-to-date, unified, and rigorous treatment of theoretical and computational aspects of discrete-time Markov decision processes." —Journal of the American Statistical Association
Download or read book Markov Decision Processes with Their Applications written by Qiying Hu and published by Springer Science & Business Media. This book was released on 2007-09-14 with total page 305 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Markov Decision Problems with Countable State Spaces written by H. M. Dietz and published by Walter de Gruyter GmbH & Co KG. This book was released on 1984-01-14 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: No detailed description available for "Markov Decision Problems with Countable State Spaces".
Download or read book Markov Processes and Controlled Markov Chains written by Zhenting Hou and published by Springer Science & Business Media. This book was released on 2013-12-01 with total page 501 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Recent Developments in Markov Decision Processes written by Roger Hartley and published by . This book was released on 1980 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Recurrence Conditions in Denumerable State Markov Decision Processes written by Awi Federgruen and published by . This book was released on 1977 with total page 18 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Modeling Uncertainty written by Moshe Dror and published by Springer. This book was released on 2019-11-05 with total page 770 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modeling Uncertainty: An Examination of Stochastic Theory, Methods, and Applications, is a volume undertaken by the friends and colleagues of Sid Yakowitz in his honor. Fifty internationally known scholars have collectively contributed 30 papers on modeling uncertainty to this volume. Each of these papers was carefully reviewed and in the majority of cases the original submission was revised before being accepted for publication in the book. The papers cover a great variety of topics in probability, statistics, economics, stochastic optimization, control theory, regression analysis, simulation, stochastic programming, Markov decision process, application in the HIV context, and others. There are papers with a theoretical emphasis and others that focus on applications. A number of papers survey the work in a particular area and in a few papers the authors present their personal view of a topic. It is a book with a considerable number of expository articles, which are accessible to a nonexpert - a graduate student in mathematics, statistics, engineering, and economics departments, or just anyone with some mathematical background who is interested in a preliminary exposition of a particular topic. Many of the papers present the state of the art of a specific area or represent original contributions which advance the present state of knowledge. In sum, it is a book of considerable interest to a broad range of academic researchers and students of stochastic systems.
Download or read book Markov Decision Processes written by and published by . This book was released on 1991 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Dynamic Programming and Optimal Control written by Dimitri Bertsekas and published by Athena Scientific. This book was released on 2012-10-23 with total page 715 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the leading and most up-to-date textbook on the far-ranging algorithmic methodology of Dynamic Programming, which can be used for optimal control, Markovian decision problems, planning and sequential decision making under uncertainty, and discrete/combinatorial optimization. The treatment focuses on basic unifying themes, and conceptual foundations. It illustrates the versatility, power, and generality of the method with many examples and applications from engineering, operations research, and other fields. It also addresses extensively the practical application of the methodology, possibly through the use of approximations, and provides an extensive treatment of the far-reaching methodology of Neuro-Dynamic Programming/Reinforcement Learning. Among its special features, the book 1) provides a unifying framework for sequential decision making, 2) treats simultaneously deterministic and stochastic control problems popular in modern control theory and Markovian decision popular in operations research, 3) develops the theory of deterministic optimal control problems including the Pontryagin Minimum Principle, 4) introduces recent suboptimal control and simulation-based approximation techniques (neuro-dynamic programming), which allow the practical application of dynamic programming to complex problems that involve the dual curse of large dimension and lack of an accurate mathematical model, 5) provides a comprehensive treatment of infinite horizon problems in the second volume, and an introductory treatment in the first volume.
Download or read book Optimality Conditions for a Denumerable State Markov Decision Chain with Unbounded Costs written by D. R. Robinson and published by . This book was released on 1979 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Finite State Approximations for Denumerable State Markov Decision Processes the Average Cost Case written by L. C. Thomas and published by . This book was released on 1978 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book SIAM Journal on Control and Optimization written by Society for Industrial and Applied Mathematics and published by . This book was released on 2003 with total page 708 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book The Optimality Equations in Multichain Denumerable State Markov Decision Processes with the Average Cost Criterion written by Willem Hendrik Maria Zijm and published by . This book was released on 1982 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt:
