A Stochastic Point Based Algorithm For Partially Observable Markov Decision Processes
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A Stochastic Point Based Algorithm For Partially Observable Markov Decision Processes
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Author : Ludovic Tobin
language : fr
Publisher:
Release Date : 2008
A Stochastic Point Based Algorithm For Partially Observable Markov Decision Processes written by Ludovic Tobin and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with categories.
Exploiting Structure To Efficiently Solve Large Scale Partially Observable Markov Decision Processes Microform
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Author : Pascal Poupart
language : en
Publisher: Library and Archives Canada = Bibliothèque et Archives Canada
Release Date : 2005
Exploiting Structure To Efficiently Solve Large Scale Partially Observable Markov Decision Processes Microform written by Pascal Poupart and has been published by Library and Archives Canada = Bibliothèque et Archives Canada this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with categories.
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.
Reinforcement Learning
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Author : Marco Wiering
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-03-05
Reinforcement Learning written by Marco Wiering 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 2012-03-05 with Technology & Engineering categories.
Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncertain environments and a computational methodology for finding optimal behaviors for challenging problems in control, optimization and adaptive behavior of intelligent agents. As a field, reinforcement learning has progressed tremendously in the past decade. The main goal of this book is to present an up-to-date series of survey articles on the main contemporary sub-fields of reinforcement learning. This includes surveys on partially observable environments, hierarchical task decompositions, relational knowledge representation and predictive state representations. Furthermore, topics such as transfer, evolutionary methods and continuous spaces in reinforcement learning are surveyed. In addition, several chapters review reinforcement learning methods in robotics, in games, and in computational neuroscience. In total seventeen different subfields are presented by mostly young experts in those areas, and together they truly represent a state-of-the-art of current reinforcement learning research. Marco Wiering works at the artificial intelligence department of the University of Groningen in the Netherlands. He has published extensively on various reinforcement learning topics. Martijn van Otterlo works in the cognitive artificial intelligence group at the Radboud University Nijmegen in The Netherlands. He has mainly focused on expressive knowledge representation in reinforcement learning settings.
Markov Decision Processes In Artificial Intelligence
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Author : Olivier Sigaud
language : en
Publisher: John Wiley & Sons
Release Date : 2013-03-04
Markov Decision Processes In Artificial Intelligence written by Olivier Sigaud and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-04 with Technology & Engineering categories.
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.
Machine Learning Ecml 2005
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Author : João Gama
language : en
Publisher: Springer Science & Business Media
Release Date : 2005-09-22
Machine Learning Ecml 2005 written by João Gama 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 2005-09-22 with Computers categories.
This book constitutes the refereed proceedings of the 16th European Conference on Machine Learning, ECML 2005, jointly held with PKDD 2005 in Porto, Portugal, in October 2005. The 40 revised full papers and 32 revised short papers presented together with abstracts of 6 invited talks were carefully reviewed and selected from 335 papers submitted to ECML and 30 papers submitted to both, ECML and PKDD. The papers present a wealth of new results in the area and address all current issues in machine learning.
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.
Algorithms For Stochastic Finite Memory Control Of Partially Observable Systems
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Author : Gaurav Marwah
language : en
Publisher:
Release Date : 2005
Algorithms For Stochastic Finite Memory Control Of Partially Observable Systems written by Gaurav Marwah and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Algorithms categories.
A partially observable Markov decision process (POMDP) is a mathematical framework for planning and control problems in which actions have stochastic effects and observations provide uncertain state information. It is widely used for research in decision-theoretic planning and reinforcement learning. To cope with partial observability, a policy (or plan) must use memory, and previous work has shown that a finite-state controller provides a good policy representation. This thesis considers a previously-developed bounded policy iteration algorithm for POMDPs that finds policies that take the form of stochastic finite-state controllers. Two new improvements of this algorithm are developed. First improvement provides a simplification of the basic linear program, which is used to find improved controllers. This results in a considerable speed-up in efficiency of the original algorithm. Secondly, a branch and bound algorithm for adding the best possible node to the controller is presented, which provides an error bound and a test for global optimality. Experimental results show that these enhancements significantly improve the algorithm's performance.
Partially Observed Markov Decision Processes
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Author : Vikram Krishnamurthy
language : en
Publisher: Cambridge University Press
Release Date : 2016-03-21
Partially Observed Markov Decision Processes written by Vikram Krishnamurthy and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-03-21 with Mathematics categories.
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.
Optimization For Stochastic Partially Observed Systems Using A Sampling Based Approach To Learn Switched Policies
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Author : Salvatore J. Candido
language : en
Publisher:
Release Date : 2011
Optimization For Stochastic Partially Observed Systems Using A Sampling Based Approach To Learn Switched Policies written by Salvatore J. Candido and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with categories.
We propose a new method for learning policies for large, partially observable Markov decision processes (POMDPs) that require long time horizons for planning. Computing optimal policies for POMDPs is an intractable problem and, in practice, dimensionality renders exact solutions essentially unreachable for even small real-world systems of interest. For this reason, we restrict the policies we learn to the class of switched belief-feedback policies in a manner that allows us to introduce domain expert knowledge into the planning process. This approach has worked well for the systems on which we have tested our approach, and we conjecture that it will be useful for many real-world systems of interest. Our approach is based on a method like value iteration to learn a switching law. Because the POMDP problem is intractable, we use a Monte Carlo approximation to evaluate system behavior and optimize a switching law based on sampling. We explicitly analyze the sensitivity of expected cost (performance) with respect to perturbations introduced by our approximations, and use that analysis to avoid approximation errors that are potentially disastrous when using the computed policy. We demonstrate results on discrete POMDP problems from the literature and a resource allocation problem modeled after a team of robots attempting to extinguish a forest fire. We then utilize our approach to build two algorithms that solve the minimum uncertainty robot navigation problem. We demonstrate that our approach can improve on techniques in the literature in terms of solution quality by demonstrating our results in simulation. Our second approach utilizes information-theoretic heuristics to drive the sampling-based learning procedure. We conjecture that this is a useful direction towards an efficient, general stochastic motion planning algorithm.
Decision Processes In Dynamic Probabilistic Systems
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Author : A.V. Gheorghe
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Decision Processes In Dynamic Probabilistic Systems written by A.V. Gheorghe 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 Mathematics categories.
'Et moi - ... - si j'avait su comment en revenir. One service mathematics has rendered the je n'y serais point aile: human race. It has put common sense back where it belongs. on the topmost shelf next Jules Verne (0 the dusty canister labelled 'discarded non sense'. The series is divergent; therefore we may be able to do something with it. Eric T. Bell O. Heaviside Mathematics is a tool for thought. A highly necessary tool in a world where both feedback and non linearities abound. Similarly, all kinds of parts of mathematics serve as tools for other parts and for other sciences. Applying a simple rewriting rule to the quote on the right above one finds such statements as: 'One service topology has rendered mathematical physics .. .'; 'One service logic has rendered com puter science .. .'; 'One service category theory has rendered mathematics .. .'. All arguably true. And all statements obtainable this way form part of the raison d'etre of this series.