Markovian Decision Processes With Uncertain Transition Probabilities
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Markovian Decision Processes With Uncertain Transition Probabilities Or Rewards
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Author :
language : en
Publisher:
Release Date : 1959
Markovian Decision Processes With Uncertain Transition Probabilities Or Rewards written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1959 with categories.
Markovian Decision Processes With Uncertain Transition Probabilities
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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).
Markovian Decision Processes With Uncertain Transition Probabilities Of Rewards
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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
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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.
Robust Markov Decision Processes With Uncertain Transition Matrices
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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.
Constrained Markov Decision Processes
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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.
Examples In Markov Decision Processes
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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.
Decision Rules In Markovian Decision Processes With Incompletely Known Transition Probabilities
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Author : Jaap Wessels
language : en
Publisher:
Release Date : 1968
Decision Rules In Markovian Decision Processes With Incompletely Known Transition Probabilities written by Jaap Wessels 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.
Simulation Based Algorithms For Markov Decision Processes
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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.
Markov Decision Processes With Their Applications
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Author : Qiying Hu
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-09-14
Markov Decision Processes With Their Applications written by Qiying Hu 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 2007-09-14 with Business & Economics categories.
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.