Reinforcement learning : an introduction /
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
- computer
- online resource
- 9780262039246
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eBook
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Online | Engineering - CCAS | Link to resource | Available | E1000065 |
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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