Cargando…
Statistical reinforcement learning : modern machine learning approaches /
Reinforcement learning is a mathematical framework for developing computer agents that can learn an optimal behavior by relating generic reward signals with its past actions. With numerous successful applications in business intelligence, plant control, and gaming, the RL framework is ideal for deci...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Boca Raton, FL :
CRC Press,
[2015]
|
| Colección: | Chapman & Hall/CRC machine learning & pattern recognition series.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
| LEADER | 00000cam a2200000 i 4500 | ||
|---|---|---|---|
| 001 | OR_ocn908635287 | ||
| 003 | OCoLC | ||
| 005 | 20231017213018.0 | ||
| 006 | m o d | ||
| 007 | cr unu|||||||| | ||
| 008 | 150508s2015 flua ob 000 0 eng d | ||
| 040 | |a UMI |b eng |e rda |e pn |c UMI |d DEBBG |d EBLCP |d CN3GA |d E7B |d YDXCP |d CRCPR |d COO |d DEBSZ |d OCLCF |d IDB |d NRC |d MERUC |d OCLCQ |d VT2 |d CEF |d OCLCQ |d WYU |d UKAHL |d OCLCQ |d IEUOL |d OCLCO |d OCL |d OCLCQ |d SFB | ||
| 019 | |a 905983819 |a 1008947985 |a 1066586448 |a 1156379381 |a 1192342244 |a 1240520211 |a 1390933245 |a 1391465646 | ||
| 020 | |a 9781439856901 | ||
| 020 | |a 1439856907 | ||
| 020 | |a 9781466549319 | ||
| 020 | |a 1466549319 | ||
| 020 | |a 1439856893 | ||
| 020 | |a 9781439856895 | ||
| 020 | |z 9781439856895 | ||
| 020 | |a 9780429105364 |q (electronic bk.) | ||
| 020 | |a 0429105363 |q (electronic bk.) | ||
| 024 | 8 | |a 99978429971 | |
| 029 | 1 | |a AU@ |b 000056090761 | |
| 029 | 1 | |a AU@ |b 000060914160 | |
| 029 | 1 | |a AU@ |b 000062348741 | |
| 029 | 1 | |a AU@ |b 000065156715 | |
| 029 | 1 | |a AU@ |b 000067074197 | |
| 029 | 1 | |a CHBIS |b 010876938 | |
| 029 | 1 | |a CHVBK |b 480377480 | |
| 029 | 1 | |a DEBBG |b BV042683333 | |
| 029 | 1 | |a DEBBG |b BV043611331 | |
| 029 | 1 | |a DEBSZ |b 434904481 | |
| 029 | 1 | |a DEBSZ |b 445549386 | |
| 029 | 1 | |a DEBSZ |b 446585386 | |
| 029 | 1 | |a DEBSZ |b 463020677 | |
| 029 | 1 | |a GBVCP |b 820671584 | |
| 029 | 1 | |a GBVCP |b 835874281 | |
| 029 | 1 | |a ZWZ |b 189426624 | |
| 035 | |a (OCoLC)908635287 |z (OCoLC)905983819 |z (OCoLC)1008947985 |z (OCoLC)1066586448 |z (OCoLC)1156379381 |z (OCoLC)1192342244 |z (OCoLC)1240520211 |z (OCoLC)1390933245 |z (OCoLC)1391465646 | ||
| 037 | |a CL0500000589 |b Safari Books Online | ||
| 050 | 4 | |a Q325.6 | |
| 082 | 0 | 4 | |a 006.3/1 |a 006.31 |
| 049 | |a UAMI | ||
| 100 | 1 | |a Sugiyama, Masashi, |d 1974- |e author. | |
| 245 | 1 | 0 | |a Statistical reinforcement learning : |b modern machine learning approaches / |c Masashi Sugiyama. |
| 246 | 3 | 0 | |a Modern machine learning approaches |
| 264 | 1 | |a Boca Raton, FL : |b CRC Press, |c [2015] | |
| 264 | 4 | |c ©2015 | |
| 300 | |a 1 online resource (xiii, 189 pages) : |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 | 1 | |a Chapman & Hall/CRC machine learning & pattern recognition series | |
| 588 | 0 | |a Print version record. | |
| 504 | |a Includes bibliographical references (pages 183-189). | ||
| 505 | 0 | |a Cover; Contents; Foreword; Preface; Author; Part I: Introduction; Chapter 1: Introduction to Reinforcement Learning; Part II: Model-Free Policy Iteration; Chapter 2: Policy Iteration with Value Function Approximation; Chapter 3: Basis Design for Value Function Approximation; Chapter 4: Sample Reuse in Policy Iteration; Chapter 5: Active Learning in Policy Iteration; Chapter 6: Robust Policy Iteration; Part III: Model-Free Policy Search; Chapter 7: Direct Policy Search by Gradient Ascent; Chapter 8: Direct Policy Search by Expectation-Maximization; Chapter 9: Policy-Prior Search. | |
| 505 | 8 | |a Part IV: Model-Based Reinforcement LearningChapter 10: Transition Model Estimation; Chapter 11: Dimensionality Reduction for Transition Model Estimation; References. | |
| 520 | |a Reinforcement learning is a mathematical framework for developing computer agents that can learn an optimal behavior by relating generic reward signals with its past actions. With numerous successful applications in business intelligence, plant control, and gaming, the RL framework is ideal for decision making in unknown environments with large amounts of data. Supplying an up-to-date and accessible introduction to the field, Statistical Reinforcement Learning: Modern Machine Learning Approaches presents fundamental concepts and practical algorithms of statistical reinforcement learning from th. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Reinforcement learning. | |
| 650 | 0 | |a Machine learning |x Mathematical models. | |
| 650 | 6 | |a Apprentissage par renforcement (Intelligence artificielle) | |
| 650 | 6 | |a Apprentissage automatique |x Modèles mathématiques. | |
| 650 | 7 | |a Machine learning |x Mathematical models. |2 fast |0 (OCoLC)fst01004800 | |
| 650 | 7 | |a Reinforcement learning. |2 fast |0 (OCoLC)fst01732553 | |
| 655 | 2 | |a Statistics | |
| 655 | 7 | |a Statistics. |2 fast |0 (OCoLC)fst01423727 | |
| 655 | 7 | |a Statistics. |2 lcgft | |
| 655 | 7 | |a Statistiques. |2 rvmgf | |
| 776 | 0 | 8 | |a Sugiyama, Masashi, 1974- |t Statistical reinforcement learning. |d Boca Raton, Florida : Chapman & Hall/CRC, 2013 |z 9781439856895 |w (OCoLC)859182486 |
| 830 | 0 | |a Chapman & Hall/CRC machine learning & pattern recognition series. | |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781439856895/?ar |z Texto completo (Requiere registro previo con correo institucional) |
| 938 | |a Askews and Holts Library Services |b ASKH |n AH27196341 | ||
| 938 | |a CRC Press |b CRCP |n CAH0KE12645PDF | ||
| 938 | |a EBL - Ebook Library |b EBLB |n EBL1759550 | ||
| 938 | |a ebrary |b EBRY |n ebr11032989 | ||
| 938 | |a YBP Library Services |b YANK |n 12015257 | ||
| 994 | |a 92 |b IZTAP | ||