Multi-agent machine learning: a reinforcement approach
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Hoboken, NJ
John Wiley & Sons
[2014]
|
| Schlagworte: | |
| Online-Zugang: | FRO01 UBG01 UBY01 Volltext |
| Beschreibung: | Includes bibliographical references and index "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"-- "Provide an in-depth coverage of multi-player, differential games and Gam theory"-- |
| Beschreibung: | 1 Online-Ressource |
| ISBN: | 9781118884485 1118884485 9781118884478 1118884477 9781118884614 1118884612 9781322094762 1322094764 111836208X |
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Datensatz im Suchindex
| _version_ | 1804175978305421312 |
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| any_adam_object | |
| author | Schwartz, Howard M. |
| author_facet | Schwartz, Howard M. |
| author_role | aut |
| author_sort | Schwartz, Howard M. |
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| dewey-raw | 519.3 |
| dewey-search | 519.3 |
| dewey-sort | 3519.3 |
| dewey-tens | 510 - Mathematics |
| discipline | Informatik Mathematik |
| format | Electronic eBook |
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| id | DE-604.BV043396847 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T07:24:52Z |
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| isbn | 9781118884485 1118884485 9781118884478 1118884477 9781118884614 1118884612 9781322094762 1322094764 111836208X |
| language | English |
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| publishDate | 2014 |
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| publisher | John Wiley & Sons |
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| spelling | Schwartz, Howard M. Verfasser aut Multi-agent machine learning a reinforcement approach Howard M. Schwartz Hoboken, NJ John Wiley & Sons [2014] 1 Online-Ressource txt rdacontent c rdamedia cr rdacarrier Includes bibliographical references and index "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"-- "Provide an in-depth coverage of multi-player, differential games and Gam theory"-- TECHNOLOGY & ENGINEERING / Electronics / General bisacsh Differential games fast Machine learning fast Reinforcement learning fast Swarm intelligence fast Reinforcement learning Differential games Swarm intelligence Machine learning Schwarmintelligenz (DE-588)4793676-9 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Bestärkendes Lernen Künstliche Intelligenz (DE-588)4825546-4 gnd rswk-swf Mehragentensystem (DE-588)4389058-1 gnd rswk-swf Electronic books Bestärkendes Lernen Künstliche Intelligenz (DE-588)4825546-4 s Schwarmintelligenz (DE-588)4793676-9 s Maschinelles Lernen (DE-588)4193754-5 s DE-604 Mehragentensystem (DE-588)4389058-1 s 1\p DE-604 Erscheint auch als Druck-Ausgabe, Hardcover 978-1-118-36208-2 https://onlinelibrary.wiley.com/doi/book/10.1002/9781118884614 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Schwartz, Howard M. Multi-agent machine learning a reinforcement approach TECHNOLOGY & ENGINEERING / Electronics / General bisacsh Differential games fast Machine learning fast Reinforcement learning fast Swarm intelligence fast Reinforcement learning Differential games Swarm intelligence Machine learning Schwarmintelligenz (DE-588)4793676-9 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Bestärkendes Lernen Künstliche Intelligenz (DE-588)4825546-4 gnd Mehragentensystem (DE-588)4389058-1 gnd |
| subject_GND | (DE-588)4793676-9 (DE-588)4193754-5 (DE-588)4825546-4 (DE-588)4389058-1 |
| title | Multi-agent machine learning a reinforcement approach |
| title_auth | Multi-agent machine learning a reinforcement approach |
| title_exact_search | Multi-agent machine learning a reinforcement approach |
| title_full | Multi-agent machine learning a reinforcement approach Howard M. Schwartz |
| title_fullStr | Multi-agent machine learning a reinforcement approach Howard M. Schwartz |
| title_full_unstemmed | Multi-agent machine learning a reinforcement approach Howard M. Schwartz |
| title_short | Multi-agent machine learning |
| title_sort | multi agent machine learning a reinforcement approach |
| title_sub | a reinforcement approach |
| topic | TECHNOLOGY & ENGINEERING / Electronics / General bisacsh Differential games fast Machine learning fast Reinforcement learning fast Swarm intelligence fast Reinforcement learning Differential games Swarm intelligence Machine learning Schwarmintelligenz (DE-588)4793676-9 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Bestärkendes Lernen Künstliche Intelligenz (DE-588)4825546-4 gnd Mehragentensystem (DE-588)4389058-1 gnd |
| topic_facet | TECHNOLOGY & ENGINEERING / Electronics / General Differential games Machine learning Reinforcement learning Swarm intelligence Schwarmintelligenz Maschinelles Lernen Bestärkendes Lernen Künstliche Intelligenz Mehragentensystem |
| url | https://onlinelibrary.wiley.com/doi/book/10.1002/9781118884614 |
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