Download or read book Experimental Design for Partially Observed Markov Decision Processes written by Leifur Thorbergsson and published by . This book was released on 2014 with total page 95 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis considers the question of how to most effectively conduct experiments in Partially Observed Markov Decision Processes so as to provide data that is most informative about a parameter of interest. Methods from Markov decision processes, especially dynamic programming, are introduced and then used in algorithms to maximize a relevant Fisher Information. These algorithms are then applied to two POMDP examples. The methods developed can also be applied to stochastic dynamical systems, by suitable discretization, and we consequently show what control policies look like in the Morris-Lecar Neuron model and the Rosenzweig MacArthur Model, and simulation results are presented. We discuss how parameter dependence within these methods can be dealt with by the use of priors, and develop tools to update control policies online. This is demonstrated in another stochastic dynamical system describing growth dynamics of DNA template in a PCR model.

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: This book covers formulation, algorithms, and structural results of partially observed Markov decision processes, whilst linking theory to real-world applications in controlled sensing. Computations are kept to a minimum, enabling students and researchers in engineering, operations research, and economics to understand the methods and determine the structure of their optimal solution.

Download or read book Partially Observable Markov Decision Processes with Applications written by Dale J. Hockstra and published by . This book was released on 1973 with total page 127 pages. Available in PDF, EPUB and Kindle. Book excerpt: The study examines a class of partially observable sequential decision models motivated by the process of machine maintenance and corrective action or medical diagnosis and treatment. Emphasis is placed on the dynamics of the state, i.e., the possibility that the machine (disease) state changes during the decision process. This is incorporated in the form of a Markov chain. It is also assumed that the state is only indirectly observable via outputs probabilistically related to the state. The end result is a model which is a discrete time Markov decision process with a continuous state space, a finite action space, and a special transition structure. (Modified author abstract).

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.

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?

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.

Download or read book Ambiguous Partially Observable Markov Decision Processes written by Soroush Saghafian and published by . This book was released on 2018 with total page 35 pages. Available in PDF, EPUB and Kindle. Book excerpt: Markov Decision Processes (MDPs) have been widely used as invaluable tools in dynamic decision-making, which is a central concern for economic agents operating at both the micro and macro levels. Often the decision maker's information about the state is incomplete; hence, the generalization to Partially Observable MDPs (POMDPs). Unfortunately, POMDPs may require a large state and/or action space, creating the well-known "curse of dimensionality." However, recent computational contributions and blindingly fast computers have helped to dispel this curse. This paper introduces and addresses a second curse termed "curse of ambiguity," which refers to the fact that the exact transition probabilities are often hard to quantify, and are rather ambiguous. For instance, for a monetary authority concerned with dynamically setting the inflation rate so as to control the unemployment, the dynamics of unemployment rate under any given inflation rate is often ambiguous. Similarly, in worker-job matching, the dynamics of worker-job match/proficiency level is typically ambiguous. This paper addresses the "curse of ambiguity" by developing a generalization of POMDPs termed Ambiguous POMDPs (APOMDPs), which not only allows the decision maker to take into account imperfect state information, but also tackles the inevitable ambiguity with respect to the correct probabilistic model of transitions. Importantly, this paper extends various structural results from POMDPs to APOMDPs. These results enable the decision maker to make robust decisions. Robustness is achieved by using a-maximin expected utility (a-MEU), which (a) differentiates between ambiguity and ambiguity attitude, (b) avoids the over conservativeness of traditional maximin approaches, and (c) is found to be suitable in laboratory experiments in various choice behaviors including those in portfolio selection. The structural results provided also help to handle the "curse of dimensionality," since they significantly simplify the search for an optimal policy. The analysis identifies a performance guarantee for the proposed approach by developing a bound for its maximum reward loss due to model ambiguity.

Download or read book Encyclopedia of Research Design written by Neil J. Salkind and published by SAGE. This book was released on 2010-06-22 with total page 1779 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Comprising more than 500 entries, the Encyclopedia of Research Design explains how to make decisions about research design, undertake research projects in an ethical manner, interpret and draw valid inferences from data, and evaluate experiment design strategies and results. Two additional features carry this encyclopedia far above other works in the field: bibliographic entries devoted to significant articles in the history of research design and reviews of contemporary tools, such as software and statistical procedures, used to analyze results. It covers the spectrum of research design strategies, from material presented in introductory classes to topics necessary in graduate research; it addresses cross- and multidisciplinary research needs, with many examples drawn from the social and behavioral sciences, neurosciences, and biomedical and life sciences; it provides summaries of advantages and disadvantages of often-used strategies; and it uses hundreds of sample tables, figures, and equations based on real-life cases."--Publisher's description.

Download or read book Handbook of Markov Decision Processes written by Eugene A. Feinberg and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 560 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Discrete time Partially Observed Markov Decision Processes written by Shun-pin Hsu and published by . This book was released on 2002 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Markov Decision Processes with Their Applications written by Qiying Hu and published by Springer. This book was released on 2010-11-19 with total page 0 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.

Download or read book On Planning Prediction and Knowledge Transfer in Fully and Partially Observable Markov Decision Processes written by Pablo Samuel Castro and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation addresses the problem of sequential decision making under uncertainty in large systems. The formalisms used to study this problem are fully and partially observable Markov Decision Processes (MDPs and POMDPs, respectively). The first contribution of this dissertation is a theoretical analysis of the behavior of POMDPs when only subsets of the observation set are used. One of these subsets is used to update the agent's state estimate, while the other subset contains observations the agent is interested in predicting and/or optimizing. The behaviors are formalized as three types of equivalence relations. The first groups states based on their values under optimal or general policies; the second groups states according to their ability to predict observations sequences; the third type isbased on bisimulation, which is a well known equivalence relation borrowed from concurrency theory.Bisimulation relations can be generalized to bisimulation metrics ...

Download or read book The Complexity of Planning with Partially observable Markov Decision Processes written by Martin Mundhenk and published by . This book was released on 2000 with total page 70 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book A Concise Introduction to Decentralized POMDPs written by Frans A. Oliehoek and published by Springer. This book was released on 2016-06-03 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces multiagent planning under uncertainty as formalized by decentralized partially observable Markov decision processes (Dec-POMDPs). The intended audience is researchers and graduate students working in the fields of artificial intelligence related to sequential decision making: reinforcement learning, decision-theoretic planning for single agents, classical multiagent planning, decentralized control, and operations research.

Download or read book A Note on Exact Solution of Partially Observed Markov Decision Processes written by William S. Lovejoy and published by . This book was released on 1988 with total page 5 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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.

Download or read book Design of Experiments for Reinforcement Learning written by Christopher Gatti and published by Springer. This book was released on 2014-11-22 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis takes an empirical approach to understanding of the behavior and interactions between the two main components of reinforcement learning: the learning algorithm and the functional representation of learned knowledge. The author approaches these entities using design of experiments not commonly employed to study machine learning methods. The results outlined in this work provide insight as to what enables and what has an effect on successful reinforcement learning implementations so that this learning method can be applied to more challenging problems.