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Reinforcement learning : an introduction /
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| 자료유형 | E-Book |
|---|---|
| 개인저자 | Sutton, Richard S. Barto, Andrew G. |
| 서명/저자사항 | Reinforcement learning :an introduction /Richard S. Sutton and Andrew G. Barto. |
| 판사항 | Second edition. |
| 발행사항 | Cambridge, Massachusetts : The MIT Press, [2018] |
| 형태사항 | 1 online resource (xxii, 526 pages) |
| 총서사항 | Adaptive computation and machine learning |
| 소장본 주기 | OCLC control number change |
| ISBN | 9780262352703 0262352702 |
| 서지주기 | Includes bibliographical references and index. |
| 내용주기 | 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."-- |
| 일반주제명 | Reinforcement learning. Reinforcement learning. |
| 언어 | 영어 |
| 기타형태 저록 | Print version:Sutton, Richard S.Reinforcement learning.Second edition.Cambridge, Massachusetts : The MIT Press, [2018]02620392499780262039246 |
| 대출바로가기 | http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2517937 |
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| No. | 등록번호 | 청구기호 | 소장처 | 도서상태 | 반납예정일 | 예약 | 서비스 | 매체정보 |
|---|---|---|---|---|---|---|---|---|
| 1 | WE00018746 | 006.3/1 | 가야대학교/전자책서버(컴퓨터서버)/ | 대출가능 |
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