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

By: Contributor(s): Material type: TextTextSeries: Adaptive computation and machine learning seriesPublication details: MIT Press 2020Edition: Second editionDescription: xxii, 526 pages : illustrations (some color)ISBN:
  • 9780262039246 (hardcover : alk. paper)
Subject(s): DDC classification:
  • 006.31 SUT
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."--
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Holdings
Item type Current library Home library Collection Call number Status Date due Barcode
Book Book Dept. of Computer Science Reference Dept. of Computer Science 006.31 SUT (Browse shelf(Opens below)) Available DCS4870
Book Book Dept. of Futures Studies Reference Dept. of Futures Studies Reference R 006.31 SUT;1 (Browse shelf(Opens below)) Available DFS4500
Book Book Dept. of Futures Studies Processing Center Dept. of Futures Studies 006.31 SUT (Browse shelf(Opens below)) Available DFS4357

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."--

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