DRLE: Decentralized Reinforcement Learning at the Edge for Traffic Light Control in the IoV

Pengyuan Zhou, Xianfu Chen, Zhi Liu, Tristan Braud, Pan Hui, Jussi Kangasharju

    Research output: Contribution to journalArticleScientificpeer-review

    Abstract

    The Internet of Vehicles (IoV) enables real-time data exchange among vehicles and roadside units and thus provides a promising solution to alleviate traffic jams in the urban area. Meanwhile, better traffic management via efficient traffic light control can benefit the IoV as well by enabling a better communication environment and decreasing the network load. As such, IoV and efficient traffic light control can formulate a virtuous cycle. Edge computing, an emerging technology to provide low-latency computation capabilities at the edge of the network, can further improve the performance of this cycle. However, while the collected information is valuable, an efficient solution for better utilization and faster feedback has yet to be developed for edge-empowered IoV. To this end, we propose a Decentralized Reinforcement Learning at the Edge for traffic light control in the IoV (DRLE). DRLE exploits the ubiquity of the IoV to accelerate traffic data collection and interpretation towards better traffic light control and congestion alleviation. Operating within the coverage of the edge servers, DRLE aggregates data from neighboring edge servers for city-scale traffic light control. DRLE decomposes the highly complex problem of large area control into a decentralized multi-agent problem. We prove its global optima with concrete mathematical reasoning and demonstrate its superiority over several state-of-the-art algorithms via extensive evaluations.

    Original languageEnglish
    Pages (from-to)2262-2273
    Number of pages12
    JournalIEEE Transactions on Intelligent Transportation Systems
    Volume22
    Issue number4
    DOIs
    Publication statusPublished - Apr 2021
    MoE publication typeA1 Journal article-refereed

    Keywords

    • Edge computing
    • Internet of Vehicles
    • multi-agent deep reinforcement learning
    • traffic light control

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