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Reinforcement Learning : An Introduction /
"In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The o...
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| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cambridge, Mass. :
MIT Press,
1998.
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| Colección: | Book collections on Project MUSE.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 006 | m o d | ||
| 007 | cr||||||||nn|n | ||
| 008 | 970626s1998 mau o 00 0 eng d | ||
| 010 | |z 97026416 | ||
| 020 | |a 9780262257053 | ||
| 020 | |z 026225705X | ||
| 020 | |z 9780262193986 | ||
| 035 | |a (OCoLC)1053169863 | ||
| 040 | |a MdBmJHUP |c MdBmJHUP | ||
| 100 | 1 | |a Sutton, Richard S. | |
| 245 | 1 | 0 | |a Reinforcement Learning : |b An Introduction / |c Richard S. Sutton and Andrew G. Barto. |
| 264 | 1 | |a Cambridge, Mass. : |b MIT Press, |c 1998. | |
| 264 | 3 | |a Baltimore, Md. : |b Project MUSE, |c 2018 | |
| 264 | 4 | |c ©1998. | |
| 300 | |a 1 online resource: |b illustrations ; | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 0 | |a Adaptive computation and machine learning | |
| 505 | 0 | 0 | |t Contents -- |t Series Foreword -- |t Preface -- |g I. |t The Problem -- |g 1. |t Introduction -- |g 2. |t Evaluative Feedback -- |g 3. |t The Reinforcement Learning Problem -- |g II. |t Elementary Solution Methods -- |g 4. |t Dynamic Programming -- |g 5. |t Monte Carlo Methods -- |g 6. |t Temporal-Difference Learning -- |g III. |t A Unified View -- |g 7. |t Eligibility Traces -- |g 8. |t Generalization and Function Approximation -- |g 9. |t Planning and Learning -- |g 10. |t Dimensions of Reinforcement Learning -- |g 11. |t Case Studies -- |t References -- |t Summary of Notation -- |t Index. |
| 506 | 0 | |a Open Access |f Unrestricted online access |2 star | |
| 520 | 1 | |a "In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability."--Jacket. | |
| 588 | |a Description based on print version record. | ||
| 650 | 7 | |a Reinforcement learning. |2 nli | |
| 650 | 0 | 7 | |a Intel·ligencia artificial. |2 lemac |
| 650 | 1 | 7 | |a Leren. |2 gtt |
| 650 | 1 | 7 | |a Reinforcement. |2 gtt |
| 650 | 1 | 7 | |a Kunstmatige intelligentie. |2 gtt |
| 650 | 7 | |a Reinforcement learning. |2 fast |0 (OCoLC)fst01732553 | |
| 650 | 7 | |a Reinforcement learning (Machine learning) |2 blmlsh | |
| 650 | 7 | |a artificial intelligence. |2 aat | |
| 650 | 6 | |a Recherche operationnelle. | |
| 650 | 6 | |a Reconnaissance des formes (Informatique) | |
| 650 | 6 | |a Intelligence artificielle. | |
| 650 | 6 | |a Apprentissage par renforcement (Intelligence artificielle) | |
| 650 | 2 | 2 | |a Operations Research |
| 650 | 2 | 2 | |a Pattern Recognition, Automated |
| 650 | 1 | 2 | |a Artificial Intelligence |
| 650 | 0 | |a Operations research. | |
| 650 | 0 | |a Pattern recognition systems. | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Reinforcement learning. | |
| 655 | 7 | |a Electronic books. |2 local | |
| 700 | 1 | |a Barto, Andrew G. | |
| 710 | 2 | |a Project Muse. |e distributor | |
| 830 | 0 | |a Book collections on Project MUSE. | |
| 856 | 4 | 0 | |z Texto completo |u https://projectmuse.uam.elogim.com/book/60836/ |