Multi-Agent Deep Reinforcement Learning for Urban Traffic Light Control in Vehicular Networks

As urban traffic condition is diverse and complicated, applying reinforcement learning to reduce traffic congestion becomes one of the hot and promising topics. Especially, how to coordinate the traffic light controllers of multiple intersections is a key challenge for multi-agent reinforcement lear...

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Veröffentlicht in:	IEEE transactions on vehicular technology Jg. 69; H. 8; S. 8243 - 8256
Hauptverfasser:	Wu, Tong, Zhou, Pan, Liu, Kai, Yuan, Yali, Wang, Xiumin, Huang, Huawei, Wu, Dapeng Oliver
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.08.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Ad hoc networks Algorithms Buses (vehicles) Computer science Decision making deep deterministic policy gradient algorithm Deep learning deep reinforcement learning Heuristic algorithms Internet of Things Intersections markov decision process Multiagent systems Pedestrians Process control Reinforcement learning Traffic congestion Traffic control Traffic light control Traffic signals Training Vehicle dynamics Vehicles vehicular network
ISSN:	0018-9545, 1939-9359
Online-Zugang:	Volltext
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