Algorithms for Reinforcement Learning

Author:   Csaba Szepesvári
Publisher:   Springer International Publishing AG
ISBN:  

9783031004230


Pages:   89
Publication Date:   07 July 2010
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Algorithms for Reinforcement Learning


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Author:   Csaba Szepesvári
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
Weight:   0.214kg
ISBN:  

9783031004230


ISBN 10:   303100423
Pages:   89
Publication Date:   07 July 2010
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.
Language:   English

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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.

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