Markov Decision Processes

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Markov Decision Processes

Martin L. Puterman
Markov Decision Processes Book PDF

Author : Martin L. Puterman
Publisher : John Wiley & Sons
Page : 684 pages
File Size : 46,7 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

Handbook of Markov Decision Processes

Eugene A. Feinberg,Adam Shwartz
Handbook of Markov Decision Processes Book PDF

Author : Eugene A. Feinberg,Adam Shwartz
Publisher : Springer Science & Business Media
Page : 560 pages
File Size : 48,6 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 Practice

Richard J. Boucherie,Nico M. van Dijk
Markov Decision Processes in Practice Book PDF

Author : Richard J. Boucherie,Nico M. van Dijk
Publisher : Springer
Page : 552 pages
File Size : 48,6 Mb
Release : 2017-03-10
Category : Business & Economics
ISBN : 9783319477664

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Markov Decision Processes in Practice by Richard J. Boucherie,Nico M. van Dijk Pdf

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 Programming. It then continues with five parts of specific and non-exhaustive application areas. Part 2 covers MDP healthcare applications, which includes different screening procedures, appointment scheduling, ambulance scheduling and blood management. Part 3 explores MDP modeling within transportation. This ranges from public to private transportation, from airports and traffic lights to car parking or charging your electric car . Part 4 contains three chapters that illustrates the structure of approximate policies for production or manufacturing structures. In Part 5, communications is highlighted as an important application area for MDP. It includes Gittins indices, down-to-earth call centers and wireless sensor networks. Finally Part 6 is dedicated to financial modeling, offering an instructive review to account for financial portfolios and derivatives under proportional transactional costs. The MDP applications in this book illustrate a variety of both standard and non-standard aspects of MDP modeling and its practical use. This book should appeal to readers for practitioning, academic research and educational purposes, with a background in, among others, operations research, mathematics, computer science, and industrial engineering.

Markov Decision Processes with Applications to Finance

Nicole Bäuerle,Ulrich Rieder
Markov Decision Processes with Applications to Finance Book PDF

Author : Nicole Bäuerle,Ulrich Rieder
Publisher : Springer Science & Business Media
Page : 393 pages
File Size : 40,8 Mb
Release : 2011-06-06
Category : Mathematics
ISBN : 9783642183249

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Markov Decision Processes with Applications to Finance by Nicole Bäuerle,Ulrich Rieder Pdf

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 processes and stopping problems. The book presents Markov decision processes in action and includes various state-of-the-art applications with a particular view towards finance. It is useful for upper-level undergraduates, Master's students and researchers in both applied probability and finance, and provides exercises (without solutions).

Markov Decision Processes in Artificial Intelligence

Olivier Sigaud,Olivier Buffet
Markov Decision Processes in Artificial Intelligence Book PDF

Author : Olivier Sigaud,Olivier Buffet
Publisher : John Wiley & Sons
Page : 367 pages
File Size : 45,7 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.

Continuous-Time Markov Decision Processes

Xianping Guo,Onésimo Hernández-Lerma
Continuous Time Markov Decision Processes Book PDF

Author : Xianping Guo,Onésimo Hernández-Lerma
Publisher : Springer Science & Business Media
Page : 240 pages
File Size : 53,9 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.

Competitive Markov Decision Processes

Jerzy Filar,Koos Vrieze
Competitive Markov Decision Processes Book PDF

Author : Jerzy Filar,Koos Vrieze
Publisher : Springer Science & Business Media
Page : 400 pages
File Size : 46,7 Mb
Release : 2012-12-06
Category : Business & Economics
ISBN : 9781461240549

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Competitive Markov Decision Processes by Jerzy Filar,Koos Vrieze Pdf

This book is intended as a text covering the central concepts and techniques of Competitive Markov Decision Processes. It is an attempt to present a rig orous treatment that combines two significant research topics: Stochastic Games and Markov Decision Processes, which have been studied exten sively, and at times quite independently, by mathematicians, operations researchers, engineers, and economists. Since Markov decision processes can be viewed as a special noncompeti tive case of stochastic games, we introduce the new terminology Competi tive Markov Decision Processes that emphasizes the importance of the link between these two topics and of the properties of the underlying Markov processes. The book is designed to be used either in a classroom or for self-study by a mathematically mature reader. In the Introduction (Chapter 1) we outline a number of advanced undergraduate and graduate courses for which this book could usefully serve as a text. A characteristic feature of competitive Markov decision processes - and one that inspired our long-standing interest - is that they can serve as an "orchestra" containing the "instruments" of much of modern applied (and at times even pure) mathematics. They constitute a topic where the instruments of linear algebra, applied probability, mathematical program ming, analysis, and even algebraic geometry can be "played" sometimes solo and sometimes in harmony to produce either beautifully simple or equally beautiful, but baroque, melodies, that is, theorems.

Planning with Markov Decision Processes

Mausam Natarajan,Andrey Poole
Planning with Markov Decision Processes Book PDF

Author : Mausam Natarajan,Andrey Poole
Publisher : Springer Nature
Page : 194 pages
File Size : 51,9 Mb
Release : 2022-06-01
Category : Computers
ISBN : 9783031015595

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Planning with Markov Decision Processes by Mausam Natarajan,Andrey Poole Pdf

Markov Decision Processes (MDPs) are widely popular in Artificial Intelligence for modeling sequential decision-making scenarios with probabilistic dynamics. They are the framework of choice when designing an intelligent agent that needs to act for long periods of time in an environment where its actions could have uncertain outcomes. MDPs are actively researched in two related subareas of AI, probabilistic planning and reinforcement learning. Probabilistic planning assumes known models for the agent's goals and domain dynamics, and focuses on determining how the agent should behave to achieve its objectives. On the other hand, reinforcement learning additionally learns these models based on the feedback the agent gets from the environment. This book provides a concise introduction to the use of MDPs for solving probabilistic planning problems, with an emphasis on the algorithmic perspective. It covers the whole spectrum of the field, from the basics to state-of-the-art optimal and approximation algorithms. We first describe the theoretical foundations of MDPs and the fundamental solution techniques for them. We then discuss modern optimal algorithms based on heuristic search and the use of structured representations. A major focus of the book is on the numerous approximation schemes for MDPs that have been developed in the AI literature. These include determinization-based approaches, sampling techniques, heuristic functions, dimensionality reduction, and hierarchical representations. Finally, we briefly introduce several extensions of the standard MDP classes that model and solve even more complex planning problems. Table of Contents: Introduction / MDPs / Fundamental Algorithms / Heuristic Search Algorithms / Symbolic Algorithms / Approximation Algorithms / Advanced Notes

Markov Decision Processes with Their Applications

Qiying Hu,Wuyi Yue
Markov Decision Processes with Their Applications Book PDF

Author : Qiying Hu,Wuyi Yue
Publisher : Springer Science & Business Media
Page : 305 pages
File Size : 47,8 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.

Constrained Markov Decision Processes

Eitan Altman
Constrained Markov Decision Processes Book PDF

Author : Eitan Altman
Publisher : Routledge
Page : 256 pages
File Size : 47,9 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.

Examples in Markov Decision Processes

A. B. Piunovskiy
Examples in Markov Decision Processes Book PDF

Author : A. B. Piunovskiy
Publisher : World Scientific
Page : 308 pages
File Size : 46,5 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.

Partially Observed Markov Decision Processes

Vikram Krishnamurthy
Partially Observed Markov Decision Processes Book PDF

Author : Vikram Krishnamurthy
Publisher : Cambridge University Press
Page : 128 pages
File Size : 53,9 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?

Decision Making Under Uncertainty

Mykel J. Kochenderfer
Decision Making Under Uncertainty Book PDF

Author : Mykel J. Kochenderfer
Publisher : MIT Press
Page : 350 pages
File Size : 40,5 Mb
Release : 2015-07-24
Category : Computers
ISBN : 9780262331715

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Decision Making Under Uncertainty by Mykel J. Kochenderfer Pdf

An introduction to decision making under uncertainty from a computational perspective, covering both theory and applications ranging from speech recognition to airborne collision avoidance. Many important problems involve decision making under uncertainty—that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance. Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance. Decision Making Under Uncertainty unifies research from different communities using consistent notation, and is accessible to students and researchers across engineering disciplines who have some prior exposure to probability theory and calculus. It can be used as a text for advanced undergraduate and graduate students in fields including computer science, aerospace and electrical engineering, and management science. It will also be a valuable professional reference for researchers in a variety of disciplines.

Applied Probability Models with Optimization Applications

Sheldon M. Ross
Applied Probability Models with Optimization Applications Book PDF

Author : Sheldon M. Ross
Publisher : Courier Corporation
Page : 224 pages
File Size : 40,5 Mb
Release : 2013-04-15
Category : Mathematics
ISBN : 9780486318646

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Applied Probability Models with Optimization Applications by Sheldon M. Ross Pdf

Concise advanced-level introduction to stochastic processes that arise in applied probability. Poisson process, renewal theory, Markov chains, Brownian motion, much more. Problems. References. Bibliography. 1970 edition.

Markov Chains and Decision Processes for Engineers and Managers

Theodore J. Sheskin
Markov Chains and Decision Processes for Engineers and Managers Book PDF

Author : Theodore J. Sheskin
Publisher : CRC Press
Page : 478 pages
File Size : 50,5 Mb
Release : 2016-04-19
Category : Mathematics
ISBN : 9781420051124

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Markov Chains and Decision Processes for Engineers and Managers by Theodore J. Sheskin Pdf

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 u