Reinforcement learning : an introduction Richard S. Sutton and Andrew G. Barto.
Material type: TextSeries: Adaptive computation and machine learning seriesPublication details: Cambridge: The MIT Press, 2020.Edition: Second editionDescription: xxii, 526 pages : illustrations (some color) ; 24 cmISBN:- 9780262039246 (hardcover : alk. paper)
- 006.31 So7R2 23
- Q325.6 .R45 2018
Item type | Current library | Collection | Call number | Status | Notes | Date due | Barcode | |
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Books | Central Library, IISER Bhopal Reference Section | Reference | 006.31 So7R2 (Browse shelf(Opens below)) | Not For Loan | Reserve | 10700 | ||
Books | Central Library, IISER Bhopal General Section | 006.31 So7R2 (Browse shelf(Opens below)) | Checked out to Pavan Rajak (2321004) | 18/10/2024 | 10701 |
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006.31 M954M Machine learning : | 006.31 R149T TensorFlow for deep learning : | 006.31 Sch81M2 Machine learning with quantum computer | 006.31 So7R2 Reinforcement learning : | 006.312 Aa4P2 Process mining : | 006.312 B220T Text mining with MATLAB | 006.312 R27P Process mining in action: |
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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