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Book Bounded parameter Partially Observable Markov Decision Processes

Download or read book Bounded parameter Partially Observable Markov Decision Processes written by 倪耀東 and published by . This book was released on 2009 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Parameter Estimation for Partially Observable Markov Decision Processes

Download or read book Parameter Estimation for Partially Observable Markov Decision Processes written by Srinivas S. Kambhamettu and published by . This book was released on 2000 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Markov Decision Processes with Their Applications

Download or read book Markov Decision Processes with Their Applications written by Qiying Hu and published by Springer Science & Business Media. This book was released on 2007-09-14 with total page 305 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Book Exploiting Structure to Efficiently Solve Large Scale Partially Observable Markov Decision Processes microform

Download or read book Exploiting Structure to Efficiently Solve Large Scale Partially Observable Markov Decision Processes microform written by Pascal Poupart and published by Library and Archives Canada = Bibliothèque et Archives Canada. This book was released on 2005 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: Partially observable Markov decision processes (POMDPs) provide a natural and principled framework to model a wide range of sequential decision making problems under uncertainty. To date, the use of POMDPs in real-world problems has been limited by the poor scalability of existing solution algorithms, which can only solve problems with up to ten thousand states. In fact, the complexity of finding an optimal policy for a finite-horizon discrete POMDP is PSPACE-complete. In practice, two important sources of intractability plague most solution algorithms: Large policy spaces and large state spaces. In practice, it is critical to simultaneously mitigate the impact of complex policy representations and large state spaces. Hence, this thesis describes three approaches that combine techniques capable of dealing with each source of intractability: VDC with BPI, VDC with Perseus (a randomized point-based value iteration algorithm by Spaan and Vlassis [136]), and state abstraction with Perseus. The scalability of those approaches is demonstrated on two problems with more than 33 million states: synthetic network management and a real-world system designed to assist elderly persons with cognitive deficiencies to carry out simple daily tasks such as hand-washing. This represents an important step towards the deployment of POMDP techniques in ever larger, real-world, sequential decision making problems. On the other hand, for many real-world POMDPs it is possible to define effective policies with simple rules of thumb. This suggests that we may be able to find small policies that are near optimal. This thesis first presents a Bounded Policy Iteration (BPI) algorithm to robustly find a good policy represented by a small finite state controller. Real-world POMDPs also tend to exhibit structural properties that can be exploited to mitigate the effect of large state spaces. To that effect, a value-directed compression (VDC) technique is also presented to reduce POMDP models to lower dimensional representations.

Book Constrained Markov Decision Processes

Download or read book Constrained Markov Decision Processes written by Eitan Altman and published by CRC Press. This book was released on 1999-03-30 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Book Partially Observable Markov Decision Process

Download or read book Partially Observable Markov Decision Process written by Gerard Blokdyk and published by Createspace Independent Publishing Platform. This book was released on 2018-05-29 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt: Which customers cant participate in our Partially observable Markov decision process domain because they lack skills, wealth, or convenient access to existing solutions? Can we add value to the current Partially observable Markov decision process decision-making process (largely qualitative) by incorporating uncertainty modeling (more quantitative)? Who are the people involved in developing and implementing Partially observable Markov decision process? How does Partially observable Markov decision process integrate with other business initiatives? Does the Partially observable Markov decision process performance meet the customer's requirements? This premium Partially observable Markov decision process self-assessment will make you the assured Partially observable Markov decision process domain master by revealing just what you need to know to be fluent and ready for any Partially observable Markov decision process challenge. How do I reduce the effort in the Partially observable Markov decision process work to be done to get problems solved? How can I ensure that plans of action include every Partially observable Markov decision process task and that every Partially observable Markov decision process outcome is in place? How will I save time investigating strategic and tactical options and ensuring Partially observable Markov decision process costs are low? How can I deliver tailored Partially observable Markov decision process advice instantly with structured going-forward plans? There's no better guide through these mind-expanding questions than acclaimed best-selling author Gerard Blokdyk. Blokdyk ensures all Partially observable Markov decision process essentials are covered, from every angle: the Partially observable Markov decision process self-assessment shows succinctly and clearly that what needs to be clarified to organize the required activities and processes so that Partially observable Markov decision process outcomes are achieved. Contains extensive criteria grounded in past and current successful projects and activities by experienced Partially observable Markov decision process practitioners. Their mastery, combined with the easy elegance of the self-assessment, provides its superior value to you in knowing how to ensure the outcome of any efforts in Partially observable Markov decision process are maximized with professional results. Your purchase includes access details to the Partially observable Markov decision process self-assessment dashboard download which gives you your dynamically prioritized projects-ready tool and shows you exactly what to do next. Your exclusive instant access details can be found in your book.

Book Examples in Markov Decision Processes

Download or read book Examples in Markov Decision Processes written by A. B. Piunovskiy and published by World Scientific. This book was released on 2013 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Book Robust Partially Observable Markov Decision Processes

Download or read book Robust Partially Observable Markov Decision Processes written by Mohammad Rasouli and published by . This book was released on 2018 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt: In a variety of applications, decisions needs to be made dynamically after receiving imperfect observations about the state of an underlying system. Partially Observable Markov Decision Processes (POMDPs) are widely used in such applications. To use a POMDP, however, a decision-maker must have access to reliable estimations of core state and observation transition probabilities under each possible state and action pair. This is often challenging mainly due to lack of ample data, especially when some actions are not taken frequently enough in practice. This significantly limits the application of POMDPs in real-world settings. In healthcare, for example, medical tests are typically subject to false-positive and false-negative errors, and hence, the decision-maker has imperfect information about the health state of a patient. Furthermore, since some treatment options have not been recommended or explored in the past, data cannot be used to reliably estimate all the required transition probabilities regarding the health state of the patient. We introduce an extension of POMDPs, termed Robust POMDPs (RPOMDPs), which allows dynamic decision-making when there is ambiguity regarding transition probabilities. This extension enables making robust decisions by reducing the reliance on a single probabilistic model of transitions, while still allowing for imperfect state observations. We develop dynamic programming equations for solving RPOMDPs, provide a sufficient statistic and an information state, discuss ways in which their computational complexity can be reduced, and connect them to stochastic zero-sum games with imperfect private monitoring.

Book Algorithms for Partially Observable Markov Decision Processes

Download or read book Algorithms for Partially Observable Markov Decision Processes written by Hsien-Te Cheng and published by . This book was released on 1988 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Partially Observed Markov Decision Processes

Download or read book Partially Observed Markov Decision Processes written by Vikram Krishnamurthy and published by Cambridge University Press. This book was released on 2016-03-21 with total page 491 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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?

Book Learning in Partially Observable Markov Decision Processes

Download or read book Learning in Partially Observable Markov Decision Processes written by Mohit Sachan and published by . This book was released on 2012 with total page 94 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learning in Partially Observable Markov Decision process (POMDP) is motivated by the essential need to address a number of realistic problems. A number of methods exist for learning in POMDPs, but learning with limited amount of information about the model of POMDP remains a highly anticipated feature. Learning with minimal information is desirable in complex systems as methods requiring complete information among decision makers are impractical in complex systems due to increase of problem dimensionality. In this thesis we address the problem of decentralized control of POMDPs with unknown transition probabilities and reward. We suggest learning in POMDP using a tree based approach. States of the POMDP are guessed using this tree. Each node in the tree has an automaton in it and acts as a decentralized decision maker for the POMDP. The start state of POMDP is known as the landmark state. Each automaton in the tree uses a simple learning scheme to update its action choice and requires minimal information. The principal result derived is that, without proper knowledge of transition probabilities and rewards, the automata tree of decision makers will converge to a set of actions that maximizes the long term expected reward per unit time obtained by the system. The analysis is based on learning in sequential stochastic games and properties of ergodic Markov chains. Simulation results are presented to compare the long term rewards of the system under different decision control algorithms.

Book Finite Memory Policies for Partially Observable Markov Decision Processes microform

Download or read book Finite Memory Policies for Partially Observable Markov Decision Processes microform written by Lusena, Christopher and published by Ann Arbor, Mich. : University Microfilms International. This book was released on 2001 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book A Stochastic Point Based Algorithm for Partially Observable Markov Decision Processes

Download or read book A Stochastic Point Based Algorithm for Partially Observable Markov Decision Processes written by Ludovic Tobin and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: