Sutton, Richard S
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자료유형 | E-BOOK |
---|---|
서명/저자사항 | Reinforcement learning : an introduction/ Richard S. Sutton and Andrew G. Barto. |
개인저자 | Sutton, Richard S. Barto, Andrew G, |
판사항 | Second edition. |
발행사항 | Cambridge, Massachusetts: The MIT Press, [2018]. |
형태사항 | 1 online resource (xxii, 526 pages). |
총서사항 | Adaptive computation and machine learning |
기타형태 저록 | Print version: Sutton, Richard S. Reinforcement learning. Second edition. Cambridge, Massachusetts : The MIT Press, [2018] 0262039249 9780262039246 |
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. Oâµ-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."-- |
해제 | Provided by publisher. |
일반주제명 | Reinforcement learning. Reinforcement learning. |
언어 | 영어 |
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