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Reinforcement learning: an introduction Richard S. Sutton and Andrew G. Barto.

By:

Sutton, Richard S [author.]

Contributor(s):

Barto, Andrew G [author.]

Material type: Text

TextSeries: Adaptive computation and machine learning seriesPublication details: Cambridge, Massachusetts : The MIT Press, [2018]Edition: Second editionDescription: xxii, 526 pages : illustrations (some color) ; 24 cmContent type:

text

Media type:

unmediated

Carrier type:

volume

ISBN:

9780262039246 (hardcover : alk. paper)

Subject(s):

Reinforcement learning

DDC classification:

006.31 22

LOC classification:

Q325.6 .R45 2018

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.

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Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode
Books	Tan Tao University General Stacks	Reference	006.31 (Browse shelf(Opens below))	Available		A-2022-0019
Books	Tan Tao University General Stacks	Reference	006.31 (Browse shelf(Opens below))	Available		A-2022-0018

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Previous	006.3 Artificial intelligence : a modern approach	006.3 Continuous Heuristics: Prelinguistic Basis of Intelligence (Ellis Horwood Series in Artificial Intelligence)	006.31 Reinforcement learning: an introduction	006.31 Reinforcement learning: an introduction	006.31 Deep learning	006.31 Deep learning	006.31 Introduction to deep learning	Next

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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