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Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning
Li,Zhenning1; Yu,Hao2; Zhang,Guohui3; Dong,Shangjia4; Xu,Cheng Zhong1
Source PublicationTransportation Research Part C: Emerging Technologies

Inefficient traffic control may cause numerous problems such as traffic congestion and energy waste. This paper proposes a novel multi-agent reinforcement learning method, named KS-DDPG (Knowledge Sharing Deep Deterministic Policy Gradient) to achieve optimal control by enhancing the cooperation between traffic signals. By introducing the knowledge-sharing enabled communication protocol, each agent can access to the collective representation of the traffic environment collected by all agents. The proposed method is evaluated through two experiments respectively using synthetic and real-world datasets. The comparison with state-of-the-art reinforcement learning-based and conventional transportation methods demonstrate the proposed KS-DDPG has significant efficiency in controlling large-scale transportation networks and coping with fluctuations in traffic flow. In addition, the introduced communication mechanism has also been proven to speed up the convergence of the model without significantly increasing the computational burden.

KeywordAdaptive Traffic Signal Control Deep Learning Knowledge Sharing Multi-agent Reinforcement Learning Transportation Network
URLView the original
Indexed BySCIE
WOS Research AreaTransportation
WOS SubjectTransportation Science & Technology
WOS IDWOS:000636374400006
Scopus ID2-s2.0-85101941829
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Cited Times [WOS]:17   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionFaculty of Science and Technology
Corresponding AuthorYu,Hao
Affiliation1.State Key Laboratory of Internet of Things for Smart City,University of Macau,Macao
2.School of Transportation,Southeast University,China
3.Department of Civil and Environmental Engineering,University of Hawaii at Manoa,United States
4.Department of Civil and Environmental Engineering,University of Delaware,United States
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Li,Zhenning,Yu,Hao,Zhang,Guohui,et al. Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning[J]. Transportation Research Part C: Emerging Technologies,2021,125.
APA Li,Zhenning,Yu,Hao,Zhang,Guohui,Dong,Shangjia,&Xu,Cheng Zhong.(2021).Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning.Transportation Research Part C: Emerging Technologies,125.
MLA Li,Zhenning,et al."Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning".Transportation Research Part C: Emerging Technologies 125(2021).
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