Reinforcement learning : an introduction / Richard S. Sutton and Andrew G. Barto.
By: Sutton, Richard S [author.]
.
Contributor(s): Barto, Andrew G [author.].
Material type: 
TextSeries: Adaptive computation and machine learning series.Publisher: Cambridge, Massachusetts : The MIT Press, [2018]Edition: Second edition.Description: xxii, 526 pages : illustrations (some color) ; 24 cm.Content type: text Media type: unmediated Carrier type: volumeISBN: 9780262039246 (hardcover : alk. paper).Subject(s): Reinforcement learningDDC classification: 006.3/1 Summary: "Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms."-- Provided by publisher.
| Item type | Home library | Call number | Status | Date due | Barcode | Item holds |
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KCST Library | 006.31 Su Re (Browse shelf) | Available | 1000000010 |
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| 006.3 Ru Ar Artificial intelligence : | 006.31 Ba Ba Bayesian reasoning and machine learning / | 006.31 Go De Deep learning / | 006.31 Su Re Reinforcement learning : | 006.31 Th Ma Machine learning for absolute beginners : | 006.312 On Do Doing data science : | 006.312 Va Py Python data science handbook : |
Includes bibliographical references (pages 481-518) and index.
"Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms."-- Provided by publisher.
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