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Reinforcement learning : an introduction /

By: Contributor(s): Material type: TextSeries: Adaptive computation and machine learning seriesPublisher: Cambridge, Massachusetts : The MIT Press, [2018]Copyright date: ©2018Edition: Second editionDescription: 1 online resource (xxii, 526 pages) : illustrationsContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9780262039246
Subject(s): Genre/Form: Online resources:
Contents:
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.
List(s) this item appears in: Open-Access Books
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eBook Online Engineering - CCAS Link to resource Available E1000065
Total holds: 0

Includes bibliographical references and index.

Preface to the Second Edition -- Preface to the First Edition -- Summary of Notation -- 1. Introduction -- I. Tabular Solution Methods -- II. Approximate Solution Methods -- III. Looking Deeper.

Available on-campus and off-campus.

"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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