Algorithms for Reinforcement Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning)

Szepesvári, Csaba

ISBN 10: 303100423X ISBN 13: 9783031004230
Verlag: Springer, 2010
Neu Softcover

Verkäufer Ria Christie Collections, Uxbridge, Vereinigtes Königreich Verkäuferbewertung 5 von 5 Sternen 5 Sterne, Erfahren Sie mehr über Verkäufer-Bewertungen

AbeBooks-Verkäufer seit 25. März 2015


Beschreibung

Beschreibung:

In English. Bestandsnummer des Verkäufers ria9783031004230_new

Diesen Artikel melden

Inhaltsangabe:

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

Über die Autorin bzw. den Autor: Csaba Szepesvári received his PhD in 1999 from "Jozsef Attila" University, Szeged, Hungary. He is currently an Associate Professor at the Department of Computing Science of the University of Alberta and a principal investigator of the Alberta Ingenuity Center for Machine Learning. Previously, he held a senior researcher position at the Computer and Automation Research Institute of the Hungarian Academy of Sciences, where he headed the Machine Learning Group. Before that, he spent 5 years in the software industry. In 1998, he became the Research Director of Mindmaker, Ltd., working on natural language processing and speech products, while from 2000, he became the Vice President of Research at the Silicon Valley company Mindmaker Inc. He is the coauthor of a book on nonlinear approximate adaptive controllers, published over 80 journal and conference papers and serves as the Associate Editor of IEEE Transactions on Adaptive Control and AI Communications, is on the board of editors of theJournal of Machine Learning Research and the Machine Learning Journal, and is a regular member of the program committee at various machine learning and AI conferences. His areas of expertise include statistical learning theory, reinforcement learning and nonlinear adaptive control.

„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.

Bibliografische Details

Titel: Algorithms for Reinforcement Learning (...
Verlag: Springer
Erscheinungsdatum: 2010
Einband: Softcover
Zustand: New

Beste Suchergebnisse beim ZVAB

Foto des Verkäufers

Csaba Szepesvári
ISBN 10: 303100423X ISBN 13: 9783031004230
Neu Taschenbuch

Anbieter: preigu, Osnabrück, Deutschland

Verkäuferbewertung 5 von 5 Sternen 5 Sterne, Erfahren Sie mehr über Verkäufer-Bewertungen

Taschenbuch. Zustand: Neu. Algorithms for Reinforcement Learning | Csaba Szepesvári | Taschenbuch | xiii | Englisch | 2010 | Springer Nature Switzerland | EAN 9783031004230 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. Artikel-Nr. 121974924

Verkäufer kontaktieren

Neu kaufen

EUR 31,45
EUR 70,00 shipping
Versand von Deutschland nach USA

Anzahl: 5 verfügbar

In den Warenkorb

Foto des Verkäufers

Csaba Szepesvári
ISBN 10: 303100423X ISBN 13: 9783031004230
Neu Taschenbuch

Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland

Verkäuferbewertung 5 von 5 Sternen 5 Sterne, Erfahren Sie mehr über Verkäufer-Bewertungen

Taschenbuch. Zustand: 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. Artikel-Nr. 9783031004230

Verkäufer kontaktieren

Neu kaufen

EUR 32,09
EUR 60,00 shipping
Versand von Deutschland nach USA

Anzahl: 2 verfügbar

In den Warenkorb

Foto des Verkäufers

Csaba Szepesvári
ISBN 10: 303100423X ISBN 13: 9783031004230
Neu Taschenbuch

Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland

Verkäuferbewertung 5 von 5 Sternen 5 Sterne, Erfahren Sie mehr über Verkäufer-Bewertungen

Taschenbuch. Zustand: 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. Artikel-Nr. 9783031004230

Verkäufer kontaktieren

Neu kaufen

EUR 32,09
EUR 61,06 shipping
Versand von Deutschland nach USA

Anzahl: 1 verfügbar

In den Warenkorb

Beispielbild für diese ISBN

Szepesvári, Csaba
Verlag: Springer, 2010
ISBN 10: 303100423X ISBN 13: 9783031004230
Neu Softcover

Anbieter: Romtrade Corp., STERLING HEIGHTS, MI, USA

Verkäuferbewertung 5 von 5 Sternen 5 Sterne, Erfahren Sie mehr über Verkäufer-Bewertungen

Zustand: 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. Artikel-Nr. ABNR-309951

Verkäufer kontaktieren

Neu kaufen

EUR 41,12
Versand gratis
Versand innerhalb von USA

Anzahl: 2 verfügbar

In den Warenkorb