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Softcover. Condizione: As New. Leichte Kratzer / Abnutzungen / Druckstellen. Inthesummerof2008, reinforcementlearningresearchersfromaroundtheworld gathered in the north of France for a week of talks and discussions on reinfor- ment learning, on how it could be made more e? cient, applied to a broader range of applications, and utilized at more abstract and symbolic levels. As a participant in this 8th European Workshop on Reinforcement Learning, I was struck by both the quality and quantity of the presentations. There were four full days of short talks, over 50 in all, far more than there have been at any p- vious meeting on reinforcement learning in Europe, or indeed, anywhere else in the world. There was an air of excitement as substantial progress was reported in many areas including Computer Go, robotics, and ? tted methods. Overall, the work reported seemed to me to be an excellent, broad, and representative sample of cutting-edge reinforcement learning research. Some of the best of it is collected and published in this volume. The workshopandthe paperscollectedhere provideevidence thatthe ? eldof reinforcement learning remains vigorous and varied. It is appropriate to re? ect on some of the reasons for this. One is that the ? eld remains focused on a pr- lem -- sequential decision making -- without prejudice as to solution methods. Another is the existence of a common terminology and body of theory.

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Taschenbuch. Condizione: Neu. Abstraction in Reinforcement Learning | Using Option Discovery and State Similarity | Sertan Girgin | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2009 | VDM Verlag Dr. Müller | EAN 9783639136524 | Verantwortliche Person für die EU: OmniScriptum GmbH & Co. KG, Bahnhofstr. 28, 66111 Saarbrücken, info[at]akademikerverlag[dot]de | Anbieter: preigu.

Condizione: Sehr gut. Zustand: Sehr gut | Seiten: 304 | Sprache: Englisch | Produktart: Bücher | Inthesummerof2008,reinforcementlearningresearchersfromaroundtheworld gathered in the north of France for a week of talks and discussions on reinfor- ment learning, on how it could be made more e?cient, applied to a broader range of applications, and utilized at more abstract and symbolic levels. As a participant in this 8th European Workshop on Reinforcement Learning, I was struck by both the quality and quantity of the presentations. There were four full days of short talks, over 50 in all, far more than there have been at any p- vious meeting on reinforcement learning in Europe, or indeed, anywhere else in the world. There was an air of excitement as substantial progress was reported in many areas including Computer Go, robotics, and ?tted methods. Overall, the work reported seemed to me to be an excellent, broad, and representative sample of cutting-edge reinforcement learning research. Some of the best of it is collected and published in this volume. The workshopandthe paperscollectedhere provideevidence thatthe ?eldof reinforcement learning remains vigorous and varied. It is appropriate to re?ect on some of the reasons for this. One is that the ?eld remains focused on a pr- lem ¿ sequential decision making ¿ without prejudice as to solution methods. Another is the existence of a common terminology and body of theory.

Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Inthesummerof2008,reinforcementlearningresearchersfromaroundtheworl d gathered in the north of France for a week of talks and discussions on reinfor- ment learning, on how it could be made more e cient, applied to a broader range of applications, and utilized at more abstract and symbolic levels. As a participant in this 8th European Workshop on Reinforcement Learning, I was struck by both the quality and quantity of the presentations. There were four full days of short talks, over 50 in all, far more than there have been at any p- vious meeting on reinforcement learning in Europe, or indeed, anywhere else in the world. There was an air of excitement as substantial progress was reported in many areas including Computer Go, robotics, and tted methods. Overall, the work reported seemed to me to be an excellent, broad, and representative sample of cutting-edge reinforcement learning research. Some of the best of it is collected and published in this volume. The workshopandthe paperscollectedhere provideevidence thatthe eldof reinforcement learning remains vigorous and varied. It is appropriate to re ect on some of the reasons for this. One is that the eld remains focused on a pr- lem - sequential decision making - without prejudice as to solution methods. Another is the existence of a common terminology and body of theory.

Taschenbuch. Condizione: Neu. Recent Advances in Reinforcement Learning | 8th European Workshop, EWRL 2008, Villeneuve d'Ascq, France, June 30-July 3, 2008, Revised and Selected Papers | Sertan Girgin (u. a.) | Taschenbuch | xii | Englisch | 2008 | Springer | EAN 9783540897217 | 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. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Inthesummerof2008,reinforcementlearningresearchersfromaroundtheworld gathered in the north of France for a week of talks and discussions on reinfor- ment learning, on how it could be made more e cient, applied to a broader range of applications, and utilized at more abstract and symbolic levels. As a participant in this 8th European Workshop on Reinforcement Learning, I was struck by both the quality and quantity of the presentations. There were four full days of short talks, over 50 in all, far more than there have been at any p- vious meeting on reinforcement learning in Europe, or indeed, anywhere else in the world. There was an air of excitement as substantial progress was reported in many areas including Computer Go, robotics, and tted methods. Overall, the work reported seemed to me to be an excellent, broad, and representative sample of cutting-edge reinforcement learning research. Some of the best of it is collected and published in this volume. The workshopandthe paperscollectedhere provideevidence thatthe eldof reinforcement learning remains vigorous and varied. It is appropriate to re ect on some of the reasons for this. One is that the eld remains focused on a pr- lem - sequential decision making - without prejudice as to solution methods. Another is the existence of a common terminology and body of theory. 304 pp. Englisch.

Kartoniert / Broschiert. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Girgin SertanSertan Girgin received his Ph.D. degree in Computer Engineeringnfrom Middle East Technical University, Turkey in 2007 and holds andouble major in Mathematics. His research interests includenReinforcement Learning, Distri.

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Inthesummerof2008,reinforcementlearningresearchersfromaroundtheworld gathered in the north of France for a week of talks and discussions on reinfor- ment learning, on how it could be made more e?cient, applied to a broader range of applications, and utilize.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Reinforcement learning is the problem faced by anagent that must learn behavior throughtrial-and-error interactions with a dynamicenvironment. Usually, the problem to be solvedcontains subtasks that repeat at different regions ofthe state space. Without any guidancean agent has to learn the solutions of all subtaskinstances independently, which in turn degrades theperformance of the learning process. In this work, wepropose two novel approaches for building theconnections between different regions of the searchspace. The first approach efficiently discoversabstractions in the form of conditionally terminatingsequences and represents these abstractions compactlyas a single tree structure; this structure is thenused to determine the actions to be executed by theagent. In the second approach, a similarity functionbetween states is defined based on the number ofcommon action sequences; by using this similarityfunction, updates on the action-value function of astate are re ected to all similar states that allowsexperience acquired during learning be applied to abroader context. The effectiveness of both approachesis demonstrated empirically over various domains.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Inthesummerof2008,reinforcementlearningresearchersfromaroundtheworld gathered in the north of France for a week of talks and discussions on reinfor- ment learning, on how it could be made more e cient, applied to a broader range of applications, and utilized at more abstract and symbolic levels. As a participant in this 8th European Workshop on Reinforcement Learning, I was struck by both the quality and quantity of the presentations. There were four full days of short talks, over 50 in all, far more than there have been at any p- vious meeting on reinforcement learning in Europe, or indeed, anywhere else in the world. There was an air of excitement as substantial progress was reported in many areas including Computer Go, robotics, and tted methods. Overall, the work reported seemed to me to be an excellent, broad, and representative sample of cutting-edge reinforcement learning research. Some of the best of it is collected and published in this volume. The workshopandthe paperscollectedhere provideevidence thatthe eldof reinforcement learning remains vigorous and varied. It is appropriate to re ect on some of the reasons for this. One is that the eld remains focused on a pr- lem ¿ sequential decision making ¿ without prejudice as to solution methods. Another is the existence of a common terminology and body of theory.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 304 pp. Englisch.