We propose a novel approach to optimize fleet management by combining multi-agent reinforcement learning with graph neural network. To provide ride-hailing service, one needs to optimize dynamic resources and demands over spatial domain. While the spatial structure was previously approximated with a regular grid, our approach represents the road network with a graph, which better reflects the underlying geometric structure. Dynamic resource allocation is formulated as multi-agent reinforcement learning, whose action-value function (Q function) is approximated with graph neural networks. We use stochastic policy update rule over the graph with deep Q-networks (DQN), and achieve superior results over the greedy policy update. We design a realistic simulator that emulates the empirical taxi call data, and confirm the effectiveness of the proposed model under various conditions.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Optimizing Large-Scale Fleet Management on a Road Network using Multi-Agent Deep Reinforcement Learning with Graph Neural Network


    Beteiligte:
    Kim, Juhyeon (Autor:in) / Kim, Kihyun (Autor:in)


    Erscheinungsdatum :

    19.09.2021


    Format / Umfang :

    2074973 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Multi-Agent Mix Hierarchical Deep Reinforcement Learning for Large-Scale Fleet Management

    Huang, Xiaohui / Ling, Jiahao / Yang, Xiaofei et al. | IEEE | 2023


    Multi-Agent Deep Reinforcement Learning for Large-Scale Traffic Signal Control

    Chu, Tianshu / Wang, Jie / Codeca, Lara et al. | IEEE | 2020


    Multi-agent intelligent signal lamp road network control method based on deep reinforcement learning strategy

    LIU XUELI / YIN BAOCAI / GAO WEN | Europäisches Patentamt | 2021

    Freier Zugriff


    Optimizing Traffic Lights with Multi-agent Deep Reinforcement Learning and V2X communication

    Hussain, Azhar / Wang, Tong / Jiahua, Cao | ArXiv | 2020

    Freier Zugriff