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03495nam a2200769 4500 |
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ocn881065009 |
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OCoLC |
| 005 |
20170124071737.4 |
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m o d |
| 007 |
cr ||||||||||| |
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140604s2014 nju ob 001 0 eng |
| 010 |
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|a 2014021985
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| 040 |
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|a DLC
|b eng
|e rda
|c DLC
|d YDX
|d N$T
|d EBLCP
|d IDEBK
|d OCLCF
|d YDXCP
|d E7B
|d CDX
|d RECBK
|d COO
|d DG1
|d DEBSZ
|d B24X7
|d DEBBG
|d OCLCQ
|d EBLB
|d GrThAP
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| 019 |
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|a 961611919
|a 962663050
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|a 9781118884485 (ePub)
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|a 1118884485 (ePub)
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|a 9781118884478 (Adobe PDF)
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|a 1118884477 (Adobe PDF)
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|z 9781118362082 (hardback)
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|a 9781118884614
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| 020 |
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|a 1118884612
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|a 9781322094762
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| 035 |
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|a (OCoLC)881065009
|z (OCoLC)961611919
|z (OCoLC)962663050
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| 042 |
|
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|a pcc
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| 050 |
0 |
0 |
|a Q325.6
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| 072 |
|
7 |
|a MAT
|x 003000
|2 bisacsh
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0 |
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|a 519.3
|2 23
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| 084 |
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|a TEC008000
|2 bisacsh
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| 049 |
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|a MAIN
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| 100 |
1 |
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|a Schwartz, Howard M.,
|e editor.
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| 245 |
1 |
0 |
|a Multi-agent machine learning :
|b a reinforcement approach /
|c Howard M. Schwartz.
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| 264 |
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1 |
|a Hoboken, NJ :
|b John Wiley & Sons,
|c [2014]
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| 300 |
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|a 1 online resource.
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| 336 |
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|a text
|2 rdacontent
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| 337 |
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|a computer
|2 rdamedia
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| 338 |
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|a online resource
|2 rdacarrier
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| 504 |
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|a Includes bibliographical references and index.
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| 520 |
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|a "Multi-Agent Machine Learning: A Reinforcement Learning Approach is a framework to understanding different methods and approaches in multi-agent machine learning. It also provides cohesive coverage of the latest advances in multi-agent differential games and presents applications in game theory and robotics. Framework for understanding a variety of methods and approaches in multi-agent machine learning. Discusses methods of reinforcement learning such as a number of forms of multi-agent Q-learning Applicable to research professors and graduate students studying electrical and computer engineering, computer science, and mechanical and aerospace engineering"--
|c Provided by publisher.
|
| 520 |
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|a "Provide an in-depth coverage of multi-player, differential games and Gam theory"--
|c Provided by publisher.
|
| 588 |
|
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|a Description based on print version record and CIP data provided by publisher.
|
| 650 |
|
0 |
|a Reinforcement learning.
|
| 650 |
|
0 |
|a Differential games.
|
| 650 |
|
0 |
|a Swarm intelligence.
|
| 650 |
|
0 |
|a Machine learning.
|
| 650 |
|
7 |
|a TECHNOLOGY & ENGINEERING / Electronics / General.
|2 bisacsh
|
| 650 |
|
7 |
|a Differential games.
|2 fast
|0 (OCoLC)fst00893492
|
| 650 |
|
7 |
|a Machine learning.
|2 fast
|0 (OCoLC)fst01004795
|
| 650 |
|
7 |
|a Reinforcement learning.
|2 fast
|0 (OCoLC)fst01732553
|
| 650 |
|
7 |
|a Swarm intelligence.
|2 fast
|0 (OCoLC)fst01139953
|
| 655 |
|
4 |
|a Electronic books.
|
| 655 |
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0 |
|a Electronic books.
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| 776 |
0 |
8 |
|i Print version:
|a Schwartz, Howard M., editor.
|t Multi-agent machine learning
|d Hoboken, NJ : John Wiley & Sons, [2014]
|z 9781118362082
|w (DLC) 2014016950
|
| 856 |
4 |
0 |
|u https://doi.org/10.1002/9781118884614
|z Full Text via HEAL-Link
|
| 994 |
|
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|a 92
|b DG1
|
