This paper presents a multi-agent reinforcement learning approach for autonomous vehicle highway merging control. A decentralized partially observable Markov decision process is formulated, where each autonomous vehicle acts independently based on local observations. The scenario considered in this paper assumes randomly spawning vehicles and fluctuating traffic flows and a self-attention network is used to handle varying numbers of agents (vehicles). The proposed method is validated in SUMO traffic simulator, which provides a realistic highway simulation environment. Results demonstrate the approach can enable safe, efficient coordination for merging maneuvers, successfully handling dynamic number of agents. Future work will continue to enhance multi-agent reinforcement learning for autonomous vehicle coordination in complex traffic environments by reducing the training time.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Highway Merging Control Using Multi - Agent Reinforcement Learning


    Beteiligte:
    Irshayyid, Ali (Autor:in) / Chen, Jun (Autor:in)


    Erscheinungsdatum :

    13.04.2024


    Format / Umfang :

    276543 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Deep Multi-Agent Reinforcement Learning for Highway On-Ramp Merging in Mixed Traffic

    Chen, Dong / Hajidavalloo, Mohammad R. / Li, Zhaojian et al. | IEEE | 2023




    Learning Highway Ramp Merging Via Reinforcement Learning with Temporally-Extended Actions

    Triest, Samuel / Villaflor, Adam / Dolan, John M. | IEEE | 2020


    LEARNING HIGHWAY RAMP MERGING VIA REINFORCEMENT LEARNING WITH TEMPORALLY-EXTENDED ACTIONS

    Triest, Samuel / Villaflor, Adam / Dolan, John M. | British Library Conference Proceedings | 2020