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Algorithms for Reinforcement Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning)
Seller: World of Books (was SecondSale), Montgomery, IL, U.S.A.
Seller rating 5 out of 5 stars
Condition: Good. Item in good condition. Textbooks may not include supplemental items i.e. CDs, access codes etc.
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Algorithms for Reinforcement Learning (Paperback)
Language: English
Published by Springer International Publishing AG, Cham, 2010
ISBN 10: 303100423X ISBN 13: 9783031004230
Paperback. Condition: 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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Algorithms for Reinforcement Learning
Language: English
Published by Springer International Publishing AG, CH, 2010
ISBN 10: 303100423X ISBN 13: 9783031004230
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
Seller rating 5 out of 5 stars
First Edition
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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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Algorithms for Reinforcement Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Algorithmic Learning Theory (Paperback)
Language: English
Published by Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Berlin, 2011
ISBN 10: 3642244114 ISBN 13: 9783642244117
Paperback. Condition: new. Paperback. This book constitutes the refereed proceedings of the 22nd International Conference on Algorithmic Learning Theory, ALT 2011, held in Espoo, Finland, in October 2011, co-located with the 14th International Conference on Discovery Science, DS 2011. The 28 revised full papers presented together with the abstracts of 5 invited talks were carefully reviewed and selected from numerous submissions. The papers are divided into topical sections of papers on inductive inference, regression, bandit problems, online learning, kernel and margin-based methods, intelligent agents and other learning models. This book constitutes the refereed proceedings of the 22nd International Conference on Algorithmic Learning Theory, ALT 2011, held in Espoo, Finland, in October 2011, co-located with the 14th International Conference on Discovery Science, DS 2011. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Bandit Algorithms
Language: English
Published by Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
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Algorithmic Learning Theory
Language: English
Published by Springer-Verlag Berlin and Heidelberg GmbH and Co. KG, DE, 2011
ISBN 10: 3642244114 ISBN 13: 9783642244117
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
Seller rating 5 out of 5 stars
Paperback. Condition: New. 2011th.
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Bandit Algorithms
Language: English
Published by Cambridge University Press CUP, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Condition: New.
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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)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Bandit Algorithms
Language: English
Published by Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
£ 51.83
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Bandit Algorithms
Language: English
Published by Cambridge University Press, GB, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
Seller rating 5 out of 5 stars
Hardback. Condition: 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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Algorithms for Reinforcement Learning (Paperback)
Language: English
Published by Springer International Publishing AG, Cham, 2010
ISBN 10: 303100423X ISBN 13: 9783031004230
Paperback. Condition: 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 Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Algorithms for Reinforcement Learning
Language: English
Published by Springer International Publishing, 2010
ISBN 10: 303100423X ISBN 13: 9783031004230
Taschenbuch. Condition: 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
Language: English
Published by Springer-Verlag New York Inc, 2011
ISBN 10: 3642244114 ISBN 13: 9783642244117
Paperback. Condition: Brand New. 2011 edition. 466 pages. 9.50x6.25x1.00 inches. In Stock.
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Taschenbuch. Condition: Neu. Algorithms for Reinforcement Learning | Csaba Szepesvári | Taschenbuch | Synthesis Lectures on Artificial Intelligence and Machine Learning | xiii | Englisch | 2010 | Springer | EAN 9783031004230 | 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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Algorithms for Reinforcement Learning
Language: English
Published by Springer International Publishing AG, CH, 2010
ISBN 10: 303100423X ISBN 13: 9783031004230
First Edition
Paperback. Condition: New. 1st.
-
Condition: 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.
-
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.
-
Condition: New. Brand New Original US Edition. Customer service! Satisfaction Guaranteed.
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Algorithmic learning theory 22nd international conference ; proceedings.
Language: English
Published by Berlin ; Heidelberg : Springer, 2011
ISBN 10: 3642244114 ISBN 13: 9783642244117
Softcover/Paperback, Condition: 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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Algorithmic Learning Theory (Paperback)
Language: English
Published by Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Berlin, 2011
ISBN 10: 3642244114 ISBN 13: 9783642244117
Paperback. Condition: new. Paperback. This book constitutes the refereed proceedings of the 22nd International Conference on Algorithmic Learning Theory, ALT 2011, held in Espoo, Finland, in October 2011, co-located with the 14th International Conference on Discovery Science, DS 2011. The 28 revised full papers presented together with the abstracts of 5 invited talks were carefully reviewed and selected from numerous submissions. The papers are divided into topical sections of papers on inductive inference, regression, bandit problems, online learning, kernel and margin-based methods, intelligent agents and other learning models. This book constitutes the refereed proceedings of the 22nd International Conference on Algorithmic Learning Theory, ALT 2011, held in Espoo, Finland, in October 2011, co-located with the 14th International Conference on Discovery Science, DS 2011. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
-
Bandit Algorithms
Language: English
Published by Cambridge University Press Jul 2020, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Buch. Condition: Neu. Neuware.
