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Algorithms for Reinforcement Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning)
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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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Algorithms for Reinforcement Learning
Da: Books Puddle, New York, NY, U.S.A.
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Bandit Algorithms
Lingua: Inglese
Editore: Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Da: Goodwill Books, Hillsboro, OR, U.S.A.
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Algorithms for Reinforcement Learning
Lingua: Inglese
Editore: Springer International Publishing AG, CH, 2010
ISBN 10: 303100423X ISBN 13: 9783031004230
Da: Rarewaves.com USA, London, LONDO, Regno Unito
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Algorithms for Reinforcement Learning
Da: Basi6 International, Irving, TX, U.S.A.
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Algorithms for Reinforcement Learning
Da: Romtrade Corp., STERLING HEIGHTS, MI, U.S.A.
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Condizione: 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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Condizione: New. Brand New Original US Edition. Customer service! Satisfaction Guaranteed.
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EUR 47,00
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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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Bandit Algorithms
Lingua: Inglese
Editore: Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Da: Lucky's Textbooks, Dallas, TX, U.S.A.
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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.
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Bandit Algorithms
Lingua: Inglese
Editore: Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Da: California Books, Miami, FL, U.S.A.
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Bandit Algorithms
Lingua: Inglese
Editore: Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Da: Ria Christie Collections, Uxbridge, Regno Unito
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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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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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Bandit Algorithms
Lingua: Inglese
Editore: Cambridge University Press, GB, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Da: Rarewaves.com USA, London, LONDO, Regno Unito
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Aggiungi al carrelloHardback. Condizione: New. Decision-making in the face of uncertainty is a significant challenge in machine learning, and the multi-armed bandit model is a commonly used framework to address it. This comprehensive and rigorous introduction to the multi-armed bandit problem examines all the major settings, including stochastic, adversarial, and Bayesian frameworks. A focus on both mathematical intuition and carefully worked proofs makes this an excellent reference for established researchers and a helpful resource for graduate students in computer science, engineering, statistics, applied mathematics and economics. Linear bandits receive special attention as one of the most useful models in applications, while other chapters are dedicated to combinatorial bandits, ranking, non-stationary problems, Thompson sampling and pure exploration. The book ends with a peek into the world beyond bandits with an introduction to partial monitoring and learning in Markov decision processes.
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Bandit Algorithms
Lingua: Inglese
Editore: Cambridge University Press CUP, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Da: Books Puddle, New York, NY, U.S.A.
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Algorithms for Reinforcement Learning
Lingua: Inglese
Editore: Springer Nature Switzerland, Springer International Publishing Jul 2010, 2010
ISBN 10: 303100423X ISBN 13: 9783031004230
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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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
Lingua: Inglese
Editore: Springer International Publishing, 2010
ISBN 10: 303100423X ISBN 13: 9783031004230
EUR 32,09
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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, Alt 2011, Espoo, Finland, October 5-7, 2011, Proceedings
Lingua: Inglese
Editore: Springer-Verlag New York Inc, 2011
ISBN 10: 3642244114 ISBN 13: 9783642244117
Da: Revaluation Books, Exeter, Regno Unito
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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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Algorithmic learning theory 22nd international conference ; proceedings.
Lingua: Inglese
Editore: Berlin ; Heidelberg : Springer, 2011
ISBN 10: 3642244114 ISBN 13: 9783642244117
Da: BBB-Internetbuchantiquariat, Bremen, Germania
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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
Lingua: Inglese
Editore: Springer International Publishing AG, CH, 2010
ISBN 10: 303100423X ISBN 13: 9783031004230
Da: Rarewaves.com UK, London, Regno Unito
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Aggiungi al carrelloPaperback. Condizione: New. 1st. 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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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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Aggiungi al carrelloCondizione: Sehr gut. Zustand: Sehr gut | Seiten: 451 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.
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Bandit Algorithms
Lingua: Inglese
Editore: Cambridge University Press, GB, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Da: Rarewaves.com UK, London, Regno Unito
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Aggiungi al carrelloHardback. Condizione: New. Decision-making in the face of uncertainty is a significant challenge in machine learning, and the multi-armed bandit model is a commonly used framework to address it. This comprehensive and rigorous introduction to the multi-armed bandit problem examines all the major settings, including stochastic, adversarial, and Bayesian frameworks. A focus on both mathematical intuition and carefully worked proofs makes this an excellent reference for established researchers and a helpful resource for graduate students in computer science, engineering, statistics, applied mathematics and economics. Linear bandits receive special attention as one of the most useful models in applications, while other chapters are dedicated to combinatorial bandits, ranking, non-stationary problems, Thompson sampling and pure exploration. The book ends with a peek into the world beyond bandits with an introduction to partial monitoring and learning in Markov decision processes.
-
Algorithms for Reinforcement Learning
Da: Majestic Books, Hounslow, Regno Unito
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Algorithms for Reinforcement Learning
Da: Biblios, Frankfurt am main, HESSE, Germania
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