| 000 | 02463nam a22002537a 4500 | ||
|---|---|---|---|
| 005 | 20260312151213.0 | ||
| 008 | 260312b |||||||| |||| 00| 0 eng d | ||
| 020 | _a9780262039246 | ||
| 040 | _cAL | ||
| 041 | _aeng | ||
| 082 |
_a006.3 _bSUTR |
||
| 100 |
_aRichard S Sutton _9258640 |
||
| 245 |
_aReinforcement learning _b: an introduction |
||
| 260 |
_aCambridge _bMIT Press _c2020 |
||
| 300 |
_axxii,526p _bHB _c23.5X18cm |
||
| 365 |
_2Computer Science and Engineering _aABDI/0854/26 _b$90 ₹8221.50 _c$ _d$120 ₹ 10962.00 _eABDI/0854/26 _f19-02-2026 |
||
| 440 |
_aAdaptive computation and machine learning series _9258641 |
||
| 520 | _aThe significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. 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. 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 |
_aMulti armed Bandits _9258642 |
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| 650 |
_aMonte Carlo Methods _9258643 |
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| 650 |
_aFrontiers _9258644 |
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| 700 |
_aBarto, Andrew G _9258645 |
||
| 942 |
_2ddc _cBK |
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| 999 |
_c241021 _d241021 |
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