Algorithms For Stochastic Finite Memory Control Of Partially Observable Systems
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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.
Algorithms For Stochastic Finite Memory Control Of Partially Observable Systems
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Author :
language : en
Publisher:
Release Date : 2005
Algorithms For Stochastic Finite Memory Control Of Partially Observable Systems written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with 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.
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.
Algorithmic Decision Theory
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Author : Patrice Perny
language : en
Publisher: Springer
Release Date : 2013-10-28
Algorithmic Decision Theory written by Patrice Perny and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-10-28 with Computers categories.
This book constitutes the thoroughly refereed conference proceedings of the Third International Conference on Algorithmic Decision Theory, ADT 2013, held in November 2013 in Bruxelles, Belgium. The 33 revised full papers presented were carefully selected from more than 70 submissions, covering preferences in reasoning and decision making, uncertainty and robustness in decision making, multi-criteria decision analysis and optimization, collective decision making, learning and knowledge extraction for decision support.
Symbolic And Quantitative Approaches To Reasoning With Uncertainty
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Author : Salem Benferhat
language : en
Publisher: Springer
Release Date : 2003-06-30
Symbolic And Quantitative Approaches To Reasoning With Uncertainty written by Salem Benferhat and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-06-30 with Computers categories.
This book constitutes the refereed proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU 2001, held in Toulouse, France in September 2001. The 68 revised full papers presented together with three invited papers were carefully reviewed and selected from over a hundred submissions. The book offers topical sections on decision theory, partially observable Markov decision processes, decision-making, coherent probabilities, Bayesian networks, learning causal networks, graphical representation of uncertainty, imprecise probabilities, belief functions, fuzzy sets and rough sets, possibility theory, merging, belief revision and preferences, inconsistency handling, default logic, logic programming, etc.
A Concise Introduction To Decentralized Pomdps
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Author : Frans A. Oliehoek
language : en
Publisher: Springer
Release Date : 2016-06-03
A Concise Introduction To Decentralized Pomdps written by Frans A. Oliehoek and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-06-03 with Computers categories.
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.
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.
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.
Optimal Bang Bang Control Of Partially Observable Stochastic Systems
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Author : Yakoov Yavin
language : en
Publisher:
Release Date : 1980
Optimal Bang Bang Control Of Partially Observable Stochastic Systems written by Yakoov Yavin and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1980 with categories.
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.