Markov Decision Processes
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Markov Decision Processes

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Published by C O M A P, Incorporated .
Written in English

Subjects:

  • General,
  • Mathematics

Book details:

Edition Notes

SeriesUmap Expository Monograph Series
The Physical Object
FormatPaperback
Number of Pages95
ID Numbers
Open LibraryOL11404959M
ISBN 100912843047
ISBN 109780912843049

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About this book An up-to-date, unified and rigorous treatment of theoretical, computational and applied research on Markov decision process models. Concentrates on . Markov decision processes in artificial intelligence: MDPs, beyond MDPs and applications / edited by Olivier Sigaud, Olivier Buffet. p. cm. Includes bibliographical references and index. ISBN 1. Artificial intelligence--Mathematics. 2. Artificial intelligence--Statistical methods. 3. Markov processes. 4. Statistical decision. I. Markov Decision Processes: Discrete Stochastic Dynamic Programming (Wiley Series in Probability and Statistics series) by Martin L. Puterman. 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. From the reviews: "Markov decision processes (MDPs) are one of the most comprehensively investigated branches in mathematics. Very beneficial also are the notes and references at the end of each chapter. we can recommend the book for readers who are familiar with Markov decision theory and who are interested in a new approach to modelling, investigating and .

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. A Markov Decision Process (MDP) model contains: • A set of possible world states S • A set of possible actions A • A real valued reward function R(s,a) • A description Tof each action’s effects in each state. We assume the Markov Property: the effects of an action taken in a state depend only on that state and not on the prior history. Markov processes and Markov decision processes are widely used in computer science and other engineering fields. So reading this chapter will be useful for you not only in RL contexts but also for a much wider range of topics. Markov Decision Theory In practice, decision are often made without a precise knowledge of their impact on future behaviour of systems under consideration. The eld of Markov Decision Theory has developed a versatile appraoch to study and optimise the behaviour of random processes by taking appropriate actions that in uence future Size: KB.

  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 . This book presents classical Markov Decision Processes (MDP) for real-life applications and optimization. MDP allows users to develop and formally support approximate and simple decision rules, and this book showcases state-of-the-art applications in which MDP was key to .   Markov Decision Processes book. Read reviews from world’s largest community for readers. The Wiley-Interscience Paperback Series consists of selected boo /5(7). Chapter 4 Factored Markov Decision Processes 1 Introduction. Solution methods described in the MDP framework (Chapters 1 and 2) share a common bottleneck: they are not adapted to solve large , using non-structured representations requires an explicit enumeration of the possible states in the problem.