| LDR | | 03159cmm u2200529Ii 4500 |
| 001 | | 000000317863 |
| 003 | | OCoLC |
| 005 | | 20230525183550 |
| 006 | | m d | |
| 007 | | cr ||||||||||| |
| 008 | | 190402s2018 maua ob 001 0 eng |
| 019 | |
▼a 1175918416 |
| 020 | |
▼a 9780262352703
▼q (electronic bk.) |
| 020 | |
▼a 0262352702
▼q (electronic bk.) |
| 020 | |
▼z 9780262039246
▼q (hardcover
▼q alkaline paper) |
| 020 | |
▼z 0262039249
▼q (hardcover
▼q alkaline paper) |
| 035 | |
▼a 2517937
▼b (N$T) |
| 035 | |
▼a (OCoLC)1091191532
▼z (OCoLC)1175918416 |
| 040 | |
▼a INA
▼b eng
▼e rda
▼e pn
▼c INA
▼d YDX
▼d UKAHL
▼d OCLCQ
▼d N$T
▼d EBLCP
▼d 248032 |
| 049 | |
▼a MAIN |
| 050 | 4 |
▼a Q325.6
▼b .R45 2018 |
| 082 | 04 |
▼a 006.3/1
▼2 23 |
| 100 | 1 |
▼a Sutton, Richard S. |
| 245 | 10 |
▼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 |
| 338 | |
▼a online resource
▼b cr
▼2 rdacarrier |
| 490 | 1 |
▼a Adaptive computation and machine learning |
| 504 | |
▼a Includes bibliographical references and index. |
| 505 | 00 |
▼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 | 0 |
▼a Reinforcement learning. |
| 650 | 7 |
▼a Reinforcement learning.
▼2 fast
▼0 (OCoLC)fst01732553 |
| 655 | 4 |
▼a Electronic books. |
| 700 | 1 |
▼a Barto, Andrew G. |
| 776 | 08 |
▼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 | 0 |
▼a Adaptive computation and machine learning. |
| 856 | 40 |
▼3 EBSCOhost
▼u http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2517937 |
| 938 | |
▼a Askews and Holts Library Services
▼b ASKH
▼n AH37519960 |
| 938 | |
▼a YBP Library Services
▼b YANK
▼n 301368137 |
| 938 | |
▼a ProQuest Ebook Central
▼b EBLB
▼n EBL6260249 |
| 938 | |
▼a EBSCOhost
▼b EBSC
▼n 2517937 |
| 990 | |
▼a 관리자 |
| 994 | |
▼a 92
▼b N$T |