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Employing the Mean-Field Approximation Technique in Multiagent Reinforcement Learning to Control Signalized Intersections in an Urban Network

dc.contributor.authorTrinh, Hung Tuan
dc.contributor.authorBae, Sang-Hoon
dc.contributor.authorNguyen, Tuan Anh
dc.contributor.authorTran, Duy Quang
dc.contributor.authorNguyen, Van Bac
dc.date.accessioned2026-08-20T20:55:30Z
dc.date.available2026-08-20T20:55:30Z
dc.date.copyright2026/02/23
dc.date.issued2026
dc.identifier.otherJTEPBS.TEENG-9184.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4313679
dc.description.abstractAbstractTraffic signal control is a difficult task to ensure the performance of traffic networks in major cities around the world. Multiagent reinforcement learning (MARL) is a promising approach for traffic light management. As the number of agents ...
dc.publisherAmerican Society of Civil Engineers
dc.titleEmploying the Mean-Field Approximation Technique in Multiagent Reinforcement Learning to Control Signalized Intersections in an Urban Network
dc.typeJournal Article
dc.journal.volume152
dc.journal.issue5
dc.journal.titleJournal of Transportation Engineering, Part A: Systems
dc.identifier.doi10.1061/JTEPBS.TEENG-9184
dc.journal.fristpage04026019-1
dc.journal.lastpage04026019-13
dc.page13
dc.treeJournal of Transportation Engineering, Part A: Systems:;2026:;Volume ( 152 ):;issue: 005
dc.contenttypeFulltext


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