Reinforcement learning : an introduction / Richard S. Sutton and Andrew G. Barto.
Material type:
TextSeries: Adaptive computation and machine learning seriesPublication details: England: The MIT Press 2020Edition: Second editionDescription: xxii, 526 pages : illustrations (some color) ; 24 cmISBN: - 9780262039246
- 006.31 23 S9671r
| Item type | Current library | Call number | Vol info | Copy number | Status | Barcode | |
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Library, Independent University, Bangladesh (IUB) General Stacks | 006.31 S9671r (Browse shelf(Opens below)) | 2020 | 01 | Available | 029882 | |
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Library, Independent University, Bangladesh (IUB) Reference Stacks | 006.31 S9671r (Browse shelf(Opens below)) | 2020 | 02 | Not For Loan | 029883 |
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| 006.31 P954u Understanding deep learning / | 006.31 R2151m Machine learning in data science using python/ | 006.31 R639p The principles of deep learning theory : an effective theory approach to understanding neural networks / | 006.31 S9671r Reinforcement learning : an introduction / | 006.31 T775g Grokking deep learning / | 006.310151 K689m Math for deep learning : what you need to know to understand neural networks / | 006.31015192 M9781p Probabilistic machine learning : advanced topics / |
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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