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Algorithms for Reinforcement Learning - 12 Angebote vergleichen
Bester Preis: € 27,28 (vom 30.06.2022)Algorithms for Reinforcement Learning (Hardback) (2010)
ISBN: 9781681732138 bzw. 1681732130, in Englisch, Morgan Claypool, gebundenes Buch, neu, Nachdruck.
Von Händler/Antiquariat, The Book Depository US [58762574], London, United Kingdom.
Language: English . Brand New Book ***** Print on Demand *****. 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.
Algorithms for Reinforcement Learning
ISBN: 9783031004230 bzw. 303100423X, vermutlich in Englisch, Morgan & Claypool / Springer / Springer International Publishing / Springer, Berlin, Taschenbuch, neu.
Von Händler/Antiquariat, buecher.de GmbH & Co. KG, [1].
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 2010. xiii, 89 S. XIII, 89 p. 235 mm Versandfertig in 6-10 Tagen, Softcover, Neuware, Offene Rechnung (Vorkasse vorbehalten).
Algorithms for Reinforcement Learning (2010)
ISBN: 9781681732138 bzw. 1681732130, in Englisch, Morgan & Claypool, gebundenes Buch, neu.
Von Händler/Antiquariat, Amazon.com.
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. Hardcover, Label: Morgan & Claypool, Morgan & Claypool, Product group: Book, Published: 2010-06-25, Studio: Morgan & Claypool, Sales rank: 1125052.
Algorithms for Reinforcement Learning (2010)
ISBN: 9783031004230 bzw. 303100423X, vermutlich in Englisch, Springer International Publishing, Taschenbuch, neu.
Von Händler/Antiquariat, AHA-BUCH GmbH [51283250], Einbeck, Germany.
Druck auf Anfrage 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, Books.
Algorithms for Reinforcement Learning
ISBN: 9783031015519 bzw. 3031015517, in Englisch, neu, E-Book, elektronischer Download.
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.
Algorithms for Reinforcement Learning (2010)
ISBN: 9781681732138 bzw. 1681732130, in Englisch, MORGAN and CLAYPOOL, neu, Nachdruck.
New Book.Shipped from US within 10 to 14 business days.THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Algorithms for Reinforcement Learning (2010)
ISBN: 9781681732138 bzw. 1681732130, in Englisch, MORGAN and CLAYPOOL, neu, Nachdruck.
New Book. Delivered from our US warehouse in 10 to 14 business days. THIS BOOK IS PRINTED ON DEMAND.Established seller since 2000.
Algorithms for Reinforcement Learning (2010)
ISBN: 9783031004230 bzw. 303100423X, vermutlich in Englisch, Springer, Berlin|Springer International Publishing|Morgan & Claypool|Springer, Taschenbuch, neu, Nachdruck.
Von Händler/Antiquariat, moluna [73551232], Greven, Germany.
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. Books.
Algorithms for Reinforcement Learning (2010)
ISBN: 9783031004230 bzw. 303100423X, vermutlich in Englisch, Springer, Taschenbuch, gebraucht, guter Zustand.
Unread book in perfect condition. Books.
Algorithms for Reinforcement Learning
ISBN: 303100423X bzw. 9783031004230, vermutlich in Englisch, Springer International Publishing, Taschenbuch, neu.