Algorithms for Reinforcement Learning

Häftad, Engelska, 2010

Del i serien Synthesis Lectures on Artificial Intelligence and Machine Learning

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2010-07-07
Mått:191 x 235 x 10 mm
Vikt:210 g
Format:Häftad
Språk:Engelska
Serie:Synthesis Lectures on Artificial Intelligence and Machine Learning
Antal sidor:89
Förlag:Springer International Publishing AG
ISBN:9783031004230
Originaltitel:Algorithms for Reinforcement Learning

Utforska kategorier

Systemvetenskap och AI inom Data och IT
Tillämpad matematik inom Naturvetenskap och teknik
Artificiell intelligens inom Data och IT

Mer om författaren

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.