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Simulation Based Algorithms Markov Decision by Chang Hyeong (45 results)
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Condition: New. pp. xviii + 189.
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Condition: New. pp. xviii + 189 1st Edition.
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Condition: New. pp. xviii + 189.
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Condition: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.
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Simulation-based Algorithms for Markov Decision Processes.
Language: English
Published by Berlin/Berlin, Springer London/Springer Berlin., 2007
ISBN 10: 1846286891 ISBN 13: 9781846286896
Seller: Universitätsbuchhandlung Herta Hold GmbH, Berlin, Germany
Association Member: GIAQ ILAB VDA
Seller rating 4 out of 5 stars
1. Ed. 235 mm x 155 mm. XVI, 184 S. 38 schw.-w. Ill., 10 schw.-w. Tab., 38 schw.-w. graph. Darst. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Communications and Control Engineering. Sprache: Englisch.
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Condition: New. Brand New Original US Edition. Customer service! Satisfaction Guaranteed.
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Condition: New. Brand New Original US Edition. Customer service! Satisfaction Guaranteed.
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Condition: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.
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Simulation-Based Algorithms for Markov Decision Processes (Communications and Control Engineering)
Book 26 of 65: Communications and Control EngineeringSeller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
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Simulation-Based Algorithms for Markov Decision Processes (Communications and Control Engineering)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
£ 96.88
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Simulation-based Algorithms for Markov Decision Processes
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Condition: New.
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Simulation-based Algorithms for Markov Decision Processes (Communications and Control Engineering)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
£ 101.50
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Condition: New. pp. 246 2nd ed. 2013 edition NO-PA16APR2015-KAP.
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Hardcover. Condition: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
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Condition: New. pp. 208.
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Hardcover. Condition: Brand New. 2nd edition. 246 pages. 9.25x6.25x0.75 inches. In Stock.
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Taschenbuch. Condition: Neu. Simulation-Based Algorithms for Markov Decision Processes | Hyeong Soo Chang (u. a.) | Taschenbuch | Communications and Control Engineering | xvii | Englisch | 2015 | Springer | EAN 9781447159902 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Taschenbuch. Condition: Neu. Simulation-based Algorithms for Markov Decision Processes | Hyeong Soo Chang (u. a.) | Taschenbuch | Communications and Control Engineering | Einband - flex.(Paperback) | Englisch | 2010 | Springer | EAN 9781849966436 | Verantwortliche Person für die EU: Springer Nature Customer Service Center GmbH, Europaplatz 3, 69115 Heidelberg, productsafety[at]springernature[dot]com | Anbieter: preigu.
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Simulation-Based Algorithms for Markov Decision Processes
Book 26 of 65: Communications and Control EngineeringLanguage: English
Published by Springer London, Springer London, 2013
ISBN 10: 144715021X ISBN 13: 9781447150213
Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Often, real-world problems modeled by Markov decision processes (MDPs) are difficult to solve in practise because of the curse of dimensionality. In others, explicit specification of the MDP model parameters is not feasible, but simulation samples are available. For these settings, various sampling and population-based numerical algorithms for computing an optimal solution in terms of a policy and/or value function have been developed recently. Here, this state-of-the-art research is brought together in a way that makes it accessible to researchers of varying interests and backgrounds. Many specific algorithms, illustrative numerical examples and rigorous theoretical convergence results are provided. The algorithms differ from the successful computational methods for solving MDPs based on neuro-dynamic programming or reinforcement learning. The algorithms can be combined with approximate dynamic programming methods that reduce the size of the state space and ameliorate the effects of dimensionality.
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Simulation-based Algorithms for Markov Decision Processes
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
£ 144.99
£ 15 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: As New. Unread book in perfect condition.
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Paperback. Condition: Like New. Like New. book.
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Condition: As New. Unread book in perfect condition.
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Hardcover. Condition: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
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Condition: new. Questo è un articolo print on demand.
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Simulation-Based Algorithms for Markov Decision Processes
Book 26 of 65: Communications and Control EngineeringLanguage: English
Published by Springer London Mrz 2013, 2013
ISBN 10: 144715021X ISBN 13: 9781447150213
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 248 pp. Englisch.
