Handbook of Markov Decision Processes

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  • Publisher : Springer Science & Business Media
  • Release : 06 December 2012
  • ISBN : 9781461508052
  • Page : 565 pages
  • Rating : 4.5/5 from 103 voters

Handbook of Markov Decision Processes Book PDF summary

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.

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Handbook of Markov Decision Processes

Handbook of Markov Decision Processes
  • Author : Eugene A. Feinberg,Adam Shwartz
  • Publisher : Springer Science & Business Media
  • Release Date : 2012-12-06
  • ISBN : 9781461508052
DOWNLOAD BOOKHandbook of Markov Decision Processes

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,

Handbook of Healthcare Analytics

Handbook of Healthcare Analytics
  • Author : Tinglong Dai,Sridhar Tayur
  • Publisher : John Wiley & Sons
  • Release Date : 2018-07-30
  • ISBN : 9781119300960
DOWNLOAD BOOKHandbook of Healthcare Analytics

How can analytics scholars and healthcare professionals access the most exciting and important healthcare topics and tools for the 21st century? Editors Tinglong Dai and Sridhar Tayur, aided by a team of internationally acclaimed experts, have curated this timely volume to help newcomers and seasoned researchers alike to rapidly comprehend a diverse set of thrusts and tools in this rapidly growing cross-disciplinary field. The Handbook covers a wide range of macro-, meso- and micro-level thrusts—such as market design, competing

Markov Decision Processes in Practice

Markov Decision Processes in Practice
  • Author : Richard J. Boucherie,Nico M. van Dijk
  • Publisher : Springer
  • Release Date : 2017-03-10
  • ISBN : 9783319477664
DOWNLOAD BOOKMarkov Decision Processes in Practice

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 the solution approach. The book is divided into six parts. Part 1 is devoted to the state-of-the-art theoretical foundation of MDP, including approximate methods such as policy improvement, successive approximation and infinite state spaces as well as an instructive chapter on Approximate Dynamic

Markov Chains and Decision Processes for Engineers and Managers

Markov Chains and Decision Processes for Engineers and Managers
  • Author : Theodore J. Sheskin
  • Publisher : CRC Press
  • Release Date : 2016-04-19
  • ISBN : 9781420051124
DOWNLOAD BOOKMarkov Chains and Decision Processes for Engineers and Managers

Recognized as a powerful tool for dealing with uncertainty, Markov modeling can enhance your ability to analyze complex production and service systems. However, most books on Markov chains or decision processes are often either highly theoretical, with few examples, or highly prescriptive, with little justification for the steps of the algorithms used to solve Markov models. Providing a unified treatment of Markov chains and Markov decision processes in a single volume, Markov Chains and Decision Processes for Engineers and Managers

Markov Decision Processes with Applications to Finance

Markov Decision Processes with Applications to Finance
  • Author : Nicole Bäuerle,Ulrich Rieder
  • Publisher : Springer Science & Business Media
  • Release Date : 2011-06-06
  • ISBN : 9783642183249
DOWNLOAD BOOKMarkov Decision Processes with Applications to Finance

The theory of Markov decision processes focuses on controlled Markov chains in discrete time. The authors establish the theory for general state and action spaces and at the same time show its application by means of numerous examples, mostly taken from the fields of finance and operations research. By using a structural approach many technicalities (concerning measure theory) are avoided. They cover problems with finite and infinite horizons, as well as partially observable Markov decision processes, piecewise deterministic Markov decision

Simulation-based Algorithms for Markov Decision Processes

Simulation-based Algorithms for Markov Decision Processes
  • Author : Hyeong Soo Chang,Michael C. Fu,Jiaqiao Hu,Steven I. Marcus
  • Publisher : Springer Science & Business Media
  • Release Date : 2007-05-01
  • ISBN : 9781846286902
DOWNLOAD BOOKSimulation-based Algorithms for Markov Decision Processes

Markov decision process (MDP) models are widely used for modeling sequential decision-making problems that arise in engineering, economics, computer science, and the social sciences. This book brings the state-of-the-art research together for the first time. It provides practical modeling methods for many real-world problems with high dimensionality or complexity which have not hitherto been treatable with Markov decision processes.

Continuous-Time Markov Decision Processes

Continuous-Time Markov Decision Processes
  • Author : Xianping Guo,Onésimo Hernández-Lerma
  • Publisher : Springer Science & Business Media
  • Release Date : 2009-09-18
  • ISBN : 9783642025471
DOWNLOAD BOOKContinuous-Time Markov Decision Processes

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

Examples in Markov Decision Processes

Examples in Markov Decision Processes
  • Author : A. B. Piunovskiy
  • Publisher : World Scientific
  • Release Date : 2013
  • ISBN : 9781848167933
DOWNLOAD BOOKExamples in Markov Decision Processes

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.