A survey and critique of multiagent deep reinforcement learning

Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has led to a dramatic increase in the number of applications and methods. Recent works have explored learning beyond single-agent scenarios and have considered multiagent learning (MAL) scenarios. Initial results...

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Veröffentlicht in:	Autonomous agents and multi-agent systems Jg. 33; H. 6; S. 750 - 797
Hauptverfasser:	Hernandez-Leal, Pablo, Kartal, Bilal, Taylor, Matthew E.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.11.2019 Springer Nature B.V
Schlagworte:	Artificial Intelligence Computer Science Computer Systems Organization and Communication Networks Domains Machine learning Multiagent systems New Horizons in Multiagent Learning Software Engineering/Programming and Operating Systems User Interfaces and Human Computer Interaction Multiagent learning Deep reinforcement learning Multiagent systems Survey Multiagent reinforcement learning
ISSN:	1387-2532, 1573-7454
Online-Zugang:	Volltext
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