A decentralized intersection management system through collaborative negotiation between smart signals

Graves, Russell; Nelson, Zachariah  E.; Chakraborty, Subhadeep

doi:10.1080/15472450.2021.2016405

Cited by 6 publications

(1 citation statement)

References 32 publications

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“…They then analyzed the degree of correlation between connected neighbors, weight observations, and rewards based on this degree of correlation [18]. Graves et al described a decentralized road traffic light control structure that performed cooperative traffic light control through repeated negotiations with neighboring agents [19]. Haddad et al proposed an approach to integrate cooperative activities among agents so that they could share their decisions and observations [20].…”

Section: Related Researchmentioning

confidence: 99%

Learning Thresholds to Select Cooperative Partners by Applying Deep Reinforcement Learning in Distributed Traffic Signal Control

Matsuta¹,

Kodama²,

Harada³

EPiC Series in Computing

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One method to reduce vehicle congestion in a road traffic network is to appropriately control traffic signals. One control scheme for traffic signals is a distributed control scheme in which individual traffic signals cooperate locally with other geographically close traffic signals. Deep reinforcement learning has been actively studied to appropriately control traffic signals. In distributed control, it is important to select appropriate cooperative partners. In this study, we propose a method for selecting appropriate cooperative partners using deep reinforcement learning to the distributed traffic signal control.

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Section: Related Researchmentioning

confidence: 99%