Reinforcement learning: an introduction (Record no. 199869)
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| fixed length control field | 02283nam a22002417a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20260602164926.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 260330b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9780262039246 |
| 041 ## - LANGUAGE CODE | |
| Language code of text/sound track or separate title | eng |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.31 SUT-R |
| 100 ## - MAIN ENTRY--AUTHOR NAME | |
| Personal name | Sutton, Richard S. |
| 245 ## - TITLE STATEMENT | |
| Title | Reinforcement learning: an introduction |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 2nd |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication | Massachusetts |
| Name of publisher | The MIT Press, Cambridge, Massachusetts |
| Year of publication | 2020 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | xxii, 526p. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence.<br/>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. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics.<br/><br/>Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Artificial intelligence |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Machine learning |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Barto, Andrew G. |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Books and Monographs |
| Full call number | Accession Number | Koha item type | Lost status | Damaged status | Permanent Location | Current Location | Date acquired | Shelving location | Source of acquisition |
|---|---|---|---|---|---|---|---|---|---|
| 006.31 SUT-R | 102635 | Books and Monographs | Central Library, NIT Jalandhar | Central Library, NIT Jalandhar | 30.03.2026 | ||||
| 006.31 SUT-R | 102867 | Books and Monographs | Central Library, NIT Jalandhar | Central Library, NIT Jalandhar | 20.05.2026 | General Stacks | Mumbai, TV Enterprises | ||
| 006.31 SUT-R | 102868 | Books and Monographs | Central Library, NIT Jalandhar | Central Library, NIT Jalandhar | 20.05.2026 | General Stacks | Mumbai, TV Enterprises | ||
| 006.31 SUT-R | 102869 | Books and Monographs | Central Library, NIT Jalandhar | Central Library, NIT Jalandhar | 20.05.2026 | General Stacks | Mumbai, TV Enterprises |