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03159cam a2200529Ii 4500
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000000534105
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20210114163614
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190402s2018 maua ob 001 0 eng
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019
▼a 1175918416
-
020
▼a 9780262352703
▼q (electronic bk.) -
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▼a 0262352702
▼q (electronic bk.) -
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▼z 9780262039246
▼q (hardcover
▼q alkaline paper) -
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▼z 0262039249
▼q (hardcover
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▼a 2517937
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▼a (OCoLC)1091191532
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▼a INA
▼b eng
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▼d UKAHL
▼d OCLCQ
▼d N$T
▼d EBLCP
▼d 248023 -
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▼a Q325.6
▼b .R45 2018 -
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▼a 006.3/1
▼2 23 -
100
▼a Sutton, Richard S.
-
245
▼a Reinforcement learning:
▼b an introduction /:
▼c Richard S. Sutton and Andrew G. Barto. -
250
▼a Second edition.
-
260
▼a Cambridge, Massachusetts:
▼b The MIT Press,
▼c [2018]. -
300
▼a 1 online resource (xxii, 526 pages).
-
336
▼a text
▼b txt
▼2 rdacontent -
337
▼a computer
▼b c
▼2 rdamedia -
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▼a online resource
▼b cr
▼2 rdacarrier -
490
▼a Adaptive computation and machine learning
-
504
▼a Includes bibliographical references and index.
-
505
▼g 1.
▼t Introduction --
▼g I.
▼t Tabular Solution Methods:
▼g 2.
▼t Multi-armed Bandits --
▼g 3.
▼t Finite Markov Decision processes --
▼g 4.
▼t Dynamic programming --
▼g 5.
▼t Monte Carlo methods --
▼g 6.
▼t Temporal-difference learning --
▼g 7.
▼t n-step Bootstrapping --
▼g 8.
▼t Planning and learning with tabular methods--
▼g I.
▼t Approximate Solution Methods:
▼g 9.
▼t On-policy Prediction with Approximation--
▼g 10.
▼t On-policy Control with Approximation--
▼g 11.
▼t O↵-policy Methods with Approximation --
▼g 12.
▼t Eligibility Traces--
▼g 13.
▼t Policy Gradient Methods--
▼g III.
▼t Looking Deeper:
▼g 14.
▼t Psychology --
▼g 15.
▼t Neuroscience --
▼g 16.
▼t Applications and Case Studies --
▼g 17.
▼t Frontiers -
520
▼a "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."--
▼c Provided by publisher. -
590
▼a OCLC control number change
-
650
▼a Reinforcement learning.
-
650
▼a Reinforcement learning.
▼2 fast
▼0 (OCoLC)fst01732553 -
655
▼a Electronic books.
-
700
▼a Barto, Andrew G.
-
776
▼i Print version:
▼a Sutton, Richard S.
▼t Reinforcement learning.
▼b Second edition.
▼d Cambridge, Massachusetts : The MIT Press, [2018],
▼z 0262039249,
▼z 9780262039246
▼w (DLC) 2018023826
▼w (OCoLC)1043175824 -
830
▼a Adaptive computation and machine learning.
-
856
▼3 EBSCOhost
▼u http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2517937 -
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▼a Askews and Holts Library Services
▼b ASKH
▼n AH37519960 -
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▼a YBP Library Services
▼b YANK
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▼a ProQuest Ebook Central
▼b EBLB
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▼a 강리원
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▼a eBook
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Detailed Information
Reinforcement learning : an introduction / / Second edition
QR Code| Data Type : | eBook |
|---|---|
| ISBN : | 9780262352703 |
| ISBN : | 0262352702 |
| ISBN : | |
| ISBN : | |
| Personal Author : | Sutton, Richard S. |
| Title/Author : | Reinforcement learning: an introduction /: Richard S. Sutton and Andrew G. Barto. |
| Edition : | Second edition. |
| Imprint : | Cambridge, Massachusetts: The MIT Press, [2018]. |
| Format : | 1 online resource (xxii, 526 pages). |
| Total Title Note : | Adaptive computation and machine learning |
| Note : | Includes bibliographical references and index. |
| Content Note : | 1. Introduction -- I. Tabular Solution Methods: 2. Multi-armed Bandits -- 3. Finite Markov Decision processes -- 4. Dynamic programming -- 5. Monte Carlo methods -- 6. Temporal-difference learning -- 7. n-step Bootstrapping -- 8. Planning and learning with tabular methods-- I. Approximate Solution Methods: 9. On-policy Prediction with Approximation-- 10. On-policy Control with Approximation-- 11. O↵-policy Methods with Approximation -- 12. Eligibility Traces-- 13. Policy Gradient Methods-- III. Looking Deeper: 14. Psychology -- 15. Neuroscience -- 16. Applications and Case Studies -- 17. Frontiers |
| 요약 : | "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. |
| General Subject Name : | Reinforcement learning. -- |
| General Subject Name : | Reinforcement learning. -- |
| Personal Author : | Barto, Andrew G. |
| 기타형태 저록 : | Print version: Sutton, Richard S. Reinforcement learning. Second edition. Cambridge, Massachusetts : The MIT Press, [2018], 0262039249, 9780262039246 |
| Language | English |
| URL : |
|---|
예약
- 1. 예약현황은 홈페이지 로그인 후 예약 페이지에 확인 가능합니다.
- 2. 도착 통보된 예약자료 대출을 원하지 않는 경우에는 예약 현황에서 취소할 수 있습니다.
- 3. 기타 문의사항은 도서관에 문의 바랍니다.
무인예약대출
- 1. 무인예약대출 현황은 홈페이지 로그인 후 무인예약대출 페이지에 확인 가능합니다.
- 2. 무인예약대출자료 대출을 원하지 않는 경우에는 무인예약대출 페이지에서 신청 또는 접수상태인 경우만 취소할 수 있습니다.
- 3. 희망대출일은 신청일로부터 최대 1주일 까지 가능합니다.
- 4. 희망대출일을 선택하지 않은 경우 대출대기 통보 후 1주일까지 기기에서 대출가능합니다.
- 5. 기타 문의사항은 도서관에 문의 바랍니다.
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