[PDF] Robust Markov Decision Processes With Uncertain Transition Matrices - eBooks Review

Robust Markov Decision Processes With Uncertain Transition Matrices


Robust Markov Decision Processes With Uncertain Transition Matrices
DOWNLOAD

Download Robust Markov Decision Processes With Uncertain Transition Matrices PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Robust Markov Decision Processes With Uncertain Transition Matrices book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page



Robust Markov Decision Processes With Uncertain Transition Matrices


Robust Markov Decision Processes With Uncertain Transition Matrices
DOWNLOAD
Author : Arnab Nilim
language : en
Publisher:
Release Date : 2004

Robust Markov Decision Processes With Uncertain Transition Matrices written by Arnab Nilim and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with categories.




Markovian Decision Processes With Uncertain Transition Probabilities Of Rewards


Markovian Decision Processes With Uncertain Transition Probabilities Of Rewards
DOWNLOAD
Author : Edward Allan Silver
language : en
Publisher:
Release Date : 1963

Markovian Decision Processes With Uncertain Transition Probabilities Of Rewards written by Edward Allan Silver and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1963 with Markov processes categories.


In most Markov process studies to date it has been assumed that both the transition prob abilities and rewards are known exactly. The primary purpose of this thesis is to study the effects of relaxing these assumptions to allow more realistic models of real world situations. The Bayesian approach used leads to statistical decision frameworks for Markov processes. The first section is concerned with situations where the transition probabilities are not known ex actly. One approach used incorporates the con cept of multi-matrix Markov processes, processes where it is assumed that one of several known transition matrices is being utilized, but we only have a probability vector on the various matrices rather than knowing exactly which one is governing the process. The second approach assumes more directly that the transition prob abilities themselves are random variables. It is shown that the multidimensional Beta distri bution is a most convenient distribution (for Bayes calculations) to place over the prob abilities of a single row of the transition matrix. Several important properties of the distribution are displayed. Then a method is suggested for determining the multidimensional Beta prior distributions to use for any parti cular Markov process. (Author).



Markovian Decision Processes With Uncertain Transition Matrices Or And Probabilistic Observation Of States


Markovian Decision Processes With Uncertain Transition Matrices Or And Probabilistic Observation Of States
DOWNLOAD
Author : Jayantilal K. Satia
language : en
Publisher:
Release Date : 1968

Markovian Decision Processes With Uncertain Transition Matrices Or And Probabilistic Observation Of States written by Jayantilal K. Satia and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1968 with Markov processes categories.




Markov Chains And Decision Processes For Engineers And Managers


Markov Chains And Decision Processes For Engineers And Managers
DOWNLOAD
Author : Theodore J. Sheskin
language : en
Publisher: CRC Press
Release Date : 2016-04-19

Markov Chains And Decision Processes For Engineers And Managers written by Theodore J. Sheskin and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-04-19 with Mathematics categories.


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



Handbook Of Markov Decision Processes


Handbook Of Markov Decision Processes
DOWNLOAD
Author : Eugene A. Feinberg
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06

Handbook Of Markov Decision Processes written by Eugene A. Feinberg and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Business & Economics categories.


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.



Simulation Based Algorithms For Markov Decision Processes


Simulation Based Algorithms For Markov Decision Processes
DOWNLOAD
Author : Hyeong Soo Chang
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-02-26

Simulation Based Algorithms For Markov Decision Processes written by Hyeong Soo Chang and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-02-26 with Technology & Engineering categories.


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. Many real-world problems modeled by MDPs have huge state and/or action spaces, giving an opening to the curse of dimensionality and so making practical solution of the resulting models intractable. In other cases, the system of interest is too complex to allow explicit specification of some of the MDP model parameters, but simulation samples are readily available (e.g., for random transitions and costs). For these settings, various sampling and population-based algorithms have been developed to overcome the difficulties of computing an optimal solution in terms of a policy and/or value function. Specific approaches include adaptive sampling, evolutionary policy iteration, evolutionary random policy search, and model reference adaptive search. This substantially enlarged new edition reflects the latest developments in novel algorithms and their underpinning theories, and presents an updated account of the topics that have emerged since the publication of the first edition. Includes: innovative material on MDPs, both in constrained settings and with uncertain transition properties; game-theoretic method for solving MDPs; theories for developing roll-out based algorithms; and details of approximation stochastic annealing, a population-based on-line simulation-based algorithm. The self-contained approach of this book will appeal not only to researchers in MDPs, stochastic modeling, and control, and simulation but will be a valuable source of tuition and reference for students of control and operations research.



Constrained Markov Decision Processes


Constrained Markov Decision Processes
DOWNLOAD
Author : Eitan Altman
language : en
Publisher: CRC Press
Release Date : 1999-03-30

Constrained Markov Decision Processes written by Eitan Altman and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999-03-30 with Mathematics categories.


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.



Markovian Decision Processes With Uncertain Transition Probabilities


Markovian Decision Processes With Uncertain Transition Probabilities
DOWNLOAD
Author : John M. Cozzolino
language : en
Publisher:
Release Date : 1965

Markovian Decision Processes With Uncertain Transition Probabilities written by John M. Cozzolino and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1965 with categories.


A dynamic programming formulation for the Markovian decision process when transition probabilities are unknown is proposed. This formulation is used to solve simple problems, but is shown to be too difficult to apply to more complex systems. Various approximate methods are then proposed and discussed. A simple approximating algorithm is finally presented. (Author).



Examples In Markov Decision Processes


Examples In Markov Decision Processes
DOWNLOAD
Author : A. B. Piunovskiy
language : en
Publisher: World Scientific
Release Date : 2013

Examples In Markov Decision Processes written by A. B. Piunovskiy and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with Mathematics categories.


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.



Markov Decision Processes In Practice


Markov Decision Processes In Practice
DOWNLOAD
Author : Richard J. Boucherie
language : en
Publisher: Springer
Release Date : 2017-03-10

Markov Decision Processes In Practice written by Richard J. Boucherie and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-03-10 with Business & Economics categories.


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.