Abstract Nowadays, reinforcement learning is widely used to design intelligent control algorithms, which has gradually become one of the popular methods of signal control. We propose a new traffic signal control method, which applies parallel reinforcement learning methods to build a traffic signal control agent in the traffic micro-simulator. This method uses covariance adaptive matrix evolution strategy (CMA-ES) algorithm to train our system on computer cluster, with over 300 iterations and 500 populations in each iteration. In this paper, we provide preliminary results on how the parallel reinforcement learning methods perform in traffic signal control system.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Traffic Signal Timing via Parallel Reinforcement Learning


    Beteiligte:
    Zhao, Qian (Autor:in) / Xu, Cheng (Autor:in) / jin, Sheng (Autor:in)


    Erscheinungsdatum :

    2019-01-01


    Format / Umfang :

    11 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Adaptive Optimization of Traffic Signal Timing via Deep Reinforcement Learning

    Zibo Ma / Tongchao Cui / Wenxing Deng et al. | DOAJ | 2021

    Freier Zugriff

    Regional traffic signal timing method and system based on reinforcement learning

    WANG HAIQUAN / FEI YUNFAN | Europäisches Patentamt | 2023

    Freier Zugriff

    METHOD AND APPARATUS FOR CONTROLLING TRAFFIC SIGNAL TIMING BASED ON REINFORCEMENT LEARNING

    LIM YU JIN / JOO HYUN JIN | Europäisches Patentamt | 2021

    Freier Zugriff


    Traffic signal lamp timing method, device and equipment based on deep reinforcement learning

    ZHOU JIANHONG / HUANG YUMIN / WANG YUNXIANG et al. | Europäisches Patentamt | 2023

    Freier Zugriff