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Algorithms for Reinforcement Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning)
Editore: Morgan and Claypool Publishers, 2010
ISBN 10: 1608454924 ISBN 13: 9781608454921
Lingua: Inglese
Da: WeBuyBooks, Rossendale, LANCS, Regno Unito
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EUR 10,20
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Aggiungi al carrelloCondizione: Like New. Most items will be dispatched the same or the next working day. An apparently unread copy in perfect condition. Dust cover is intact with no nicks or tears. Spine has no signs of creasing. Pages are clean and not marred by notes or folds of any kind.
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Algorithms for Reinforcement Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning)
Editore: Morgan and Claypool Publishers, 2010
ISBN 10: 1608454924 ISBN 13: 9781608454921
Lingua: Inglese
EUR 33,00
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Aggiungi al carrelloCondizione: good. Befriedigend/Good: Durchschnittlich erhaltenes Buch bzw. Schutzumschlag mit Gebrauchsspuren, aber vollständigen Seiten. / Describes the average WORN book or dust jacket that has all the pages present.
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Condizione: Sehr gut. Zustand: Sehr gut | Seiten: 451 | Sprache: Englisch | Produktart: Bücher.
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Algorithms for Reinforcement Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning)
Da: California Books, Miami, FL, U.S.A.
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EUR 36,87
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Algorithms for Reinforcement Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning)
Da: Ria Christie Collections, Uxbridge, Regno Unito
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EUR 35,01
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Algorithms for Reinforcement Learning
Editore: Springer Nature Switzerland, Springer International Publishing Jul 2010, 2010
ISBN 10: 303100423X ISBN 13: 9783031004230
Lingua: Inglese
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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EUR 32,09
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Neuware -Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms' merits and limitations. Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering. In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations. Table of Contents: Markov Decision Processes / Value Prediction Problems / Control / For Further ExplorationSpringer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 104 pp. Englisch.
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Algorithms for Reinforcement Learning
Editore: Springer International Publishing, 2010
ISBN 10: 303100423X ISBN 13: 9783031004230
Lingua: Inglese
EUR 32,09
Convertire valutaEUR 14,99 per la spedizione da Germania a ItaliaQuantità: 1 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms' merits and limitations. Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering. In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations. Table of Contents: Markov Decision Processes / Value Prediction Problems / Control / For Further Exploration.
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Algorithmic learning theory 22nd international conference ; proceedings.
Editore: Berlin ; Heidelberg : Springer, 2011
ISBN 10: 3642244114 ISBN 13: 9783642244117
Lingua: Inglese
Da: BBB-Internetbuchantiquariat, Bremen, Germania
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EUR 39,30
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Aggiungi al carrelloSoftcover/Paperback, Condizione: Sehr gut. 451 Seiten Zustand: sehr gut; Ungelesen; Fußschnitt leicht angeschmutzt; T-AA1357 9783642244117 Wenn das Buch einen Schutzumschlag hat, ist das ausdrücklich erwähnt. Rechnung mit ausgewiesener Mwst. Sprache: Englisch Gewicht in Gramm: 745.
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Algorithms for Reinforcement Learning
Da: Books Puddle, New York, NY, U.S.A.
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EUR 45,99
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Algorithms for Reinforcement Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning)
Editore: Morgan and Claypool Publishers, 2010
ISBN 10: 1608454924 ISBN 13: 9781608454921
Lingua: Inglese
Da: SecondSale, Montgomery, IL, U.S.A.
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EUR 22,86
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Aggiungi al carrelloCondizione: Very Good. Item in very good condition! Textbooks may not include supplemental items i.e. CDs, access codes etc.
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Bandit Algorithms
Editore: Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Lingua: Inglese
Da: California Books, Miami, FL, U.S.A.
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EUR 58,45
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Bandit Algorithms
Editore: Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Lingua: Inglese
Da: Ria Christie Collections, Uxbridge, Regno Unito
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EUR 60,70
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Algorithmic Learning Theory: 22nd International Conference, ALT 2011, Espoo, Finland, October 5-7, 2011, Proceedings (Lecture Notes in Computer Science, 6925)
Da: Ria Christie Collections, Uxbridge, Regno Unito
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EUR 61,95
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EUR 62,28
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Aggiungi al carrelloCondizione: New. Decision-making in the face of uncertainty is a challenge in machine learning, and the multi-armed bandit model is a common framework to address it. This comprehensive introduction is an excellent reference for established researchers and a resource for gra.
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Algorithms for Reinforcement Learning (Paperback)
Editore: Springer International Publishing AG, Cham, 2010
ISBN 10: 303100423X ISBN 13: 9783031004230
Lingua: Inglese
Da: CitiRetail, Stevenage, Regno Unito
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EUR 40,22
Convertire valutaEUR 35,51 per la spedizione da Regno Unito a ItaliaQuantità: 1 disponibili
Aggiungi al carrelloPaperback. Condizione: new. Paperback. Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms' merits and limitations. Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering. In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations. Table of Contents: Markov Decision Processes / Value Prediction Problems / Control / For Further Exploration Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Algorithmic Learning Theory: 22nd International Conference, ALT 2011, Espoo, Finland, October 5-7, 2011, Proceedings (Lecture Notes in Computer Science)
Da: Chiron Media, Wallingford, Regno Unito
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EUR 58,91
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Aggiungi al carrelloPaperback. Condizione: New.
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Bandit Algorithms
Editore: Cambridge University Press CUP, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Lingua: Inglese
Da: Books Puddle, New York, NY, U.S.A.
Valutazione del venditore 4 su 5 stelle
EUR 85,57
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Algorithmic Learning Theory: 22nd International Conference, Alt 2011, Espoo, Finland, October 5-7, 2011, Proceedings
Editore: Springer-Verlag New York Inc, 2011
ISBN 10: 3642244114 ISBN 13: 9783642244117
Lingua: Inglese
Da: Revaluation Books, Exeter, Regno Unito
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EUR 82,49
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Aggiungi al carrelloPaperback. Condizione: Brand New. 2011 edition. 466 pages. 9.50x6.25x1.00 inches. In Stock.
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Algorithms for Reinforcement Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning)
Da: Lucky's Textbooks, Dallas, TX, U.S.A.
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EUR 32,73
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Algorithms for Reinforcement Learning (Paperback)
Editore: Springer International Publishing AG, Cham, 2010
ISBN 10: 303100423X ISBN 13: 9783031004230
Lingua: Inglese
Da: Grand Eagle Retail, Fairfield, OH, U.S.A.
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EUR 41,73
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Aggiungi al carrelloPaperback. Condizione: new. Paperback. Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms' merits and limitations. Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering. In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations. Table of Contents: Markov Decision Processes / Value Prediction Problems / Control / For Further Exploration Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Bandit Algorithms
Editore: Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Lingua: Inglese
Da: Lucky's Textbooks, Dallas, TX, U.S.A.
Valutazione del venditore 5 su 5 stelle
EUR 52,46
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Algorithmic Learning Theory: 22nd International Conference, ALT 2011, Espoo, Finland, October 5-7, 2011, Proceedings (Lecture Notes in Computer Science, 6925)
Da: Lucky's Textbooks, Dallas, TX, U.S.A.
Valutazione del venditore 5 su 5 stelle
EUR 53,96
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Algorithms for Reinforcement Learning
Editore: Springer, Berlin|Springer International Publishing|Morgan & Claypool|Springer, 2010
ISBN 10: 303100423X ISBN 13: 9783031004230
Lingua: Inglese
EUR 31,03
Convertire valutaEUR 9,70 per la spedizione da Germania a ItaliaQuantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only.
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Algorithms for Reinforcement Learning
Editore: Springer International Publishing Jul 2010, 2010
ISBN 10: 303100423X ISBN 13: 9783031004230
Lingua: Inglese
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
Valutazione del venditore 5 su 5 stelle
EUR 32,09
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms' merits and limitations. Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering. In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations. Table of Contents: Markov Decision Processes / Value Prediction Problems / Control / For Further Exploration 104 pp. Englisch.
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Algorithms for Reinforcement Learning
Da: Majestic Books, Hounslow, Regno Unito
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EUR 45,24
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Algorithms for Reinforcement Learning
Da: Biblios, Frankfurt am main, HESSE, Germania
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EUR 47,77
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Algorithmic Learning Theory
Editore: Springer Berlin Heidelberg, 2011
ISBN 10: 3642244114 ISBN 13: 9783642244117
Lingua: Inglese
EUR 48,74
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Aggiungi al carrelloKartoniert / Broschiert. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Fast track conference proceedings Unique visibility State of the art researchThis book constitutes the refereed proceedings of the 22nd International Conference on Algorithmic Learning Theory, ALT 2011, held in Espoo, Finland, in Oc.
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Bandit Algorithms
Editore: Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Lingua: Inglese
Da: Majestic Books, Hounslow, Regno Unito
Valutazione del venditore 5 su 5 stelle
EUR 85,60
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Bandit Algorithms
Editore: Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Lingua: Inglese
Da: Biblios, Frankfurt am main, HESSE, Germania
Valutazione del venditore 5 su 5 stelle
EUR 88,29
Convertire valutaEUR 7,95 per la spedizione da Germania a ItaliaQuantità: 4 disponibili
Aggiungi al carrelloCondizione: New. PRINT ON DEMAND.
