Applications of Multi-Agent Deep Reinforcement Learning: Models and Algorithms

Recent advancements in deep reinforcement learning (DRL) have led to its application in multi-agent scenarios to solve complex real-world problems, such as network resource allocation and sharing, network routing, and traffic signal controls. Multi-agent DRL (MADRL) enables multiple agents to intera...

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Veröffentlicht in:	Applied Sciences Jg. 11; H. 22; S. 10870
Hauptverfasser:	Ibrahim, Abdikarim Mohamed, Yau, Kok-Lim Alvin, Chong, Yung-Wey, Wu, Celimuge
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 17.11.2021
Schlagworte:	Algorithms applied reinforcement learning Biology (General) Chemistry Cooperative learning Decision making Deep learning deep Q-network Engineering (General). Civil engineering (General) Knowledge Markov analysis multi-agent deep reinforcement learning multi-agent reinforcement learning Physics QC1-999 QD1-999 QH301-705.5 reinforcement learning T TA1-2040 Taxonomy Technology Wireless networks
ISSN:	2076-3417, 2076-3417
Online-Zugang:	Volltext
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