The invention provides a multi-agent reinforcement learning traffic signal control method based on strategy multiplexing, and belongs to the technical field of traffic signal control. The method comprises the following steps: constructing a universal EDLight model of the intelligent agent; the general road network model is trained based on an existing TOD scene, and a learned environment model is stored; calculating the similarity between the unknown TOD target environment and the pre-training task environment; and based on the similarity, selecting a pre-training model of a similar environment to guide a target agent model to make a decision, and updating a target domain model based on probability sampling to realize autonomous decision. The optimal strategy is selected for the unknown target network model, so that the agent reinforcement learning model can migrate in the road network and across the road network. The problem that an existing model is insufficient in traffic signal control migration capacity is solved.

    本发明提供一种基于策略复用的多智能体强化学习交通信号控制方法,属于交通信号控制技术领域。通过步骤:构建智能体通用EDLight模型;基于现有TOD场景对通用路网模型进行训练,并存储已学习环境模型;计算未知TOD目标环境和预训练任务环境的相似度;基于所述相似度,选择相似环境的预训练模型来指导目标智能体模型进行决策,并基于概率采样更新目标域模型,实现自主决策。实现了为未知目标网络模型选择最优策略,从而实现了智能体强化学习模型能够在路网中和跨路网迁移。解决了现有模型对交通信号控制迁移能力不足的问题。


    Access

    Download


    Export, share and cite



    Title :

    Multi-agent reinforcement learning traffic signal control method based on strategy multiplexing


    Additional title:

    一种基于策略复用的多智能体强化学习交通信号控制方法


    Contributors:
    ZHANG CHENGWEI (author) / LI YIHONG (author) / ZHOU KAILING (author) / LIU WANTING (author)

    Publication date :

    2024-12-10


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    Chinese


    Classification :

    IPC:    G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS



    Adaptive traffic signal control method based on multi-agent reinforcement learning

    ZHANG CHENGWEI / JIN SHAN / ZHENG KANGJIE | European Patent Office | 2021

    Free access

    Traffic light signal control method based on multi-agent reinforcement learning

    ZHAO SHENGJIE / DENG HAO / CHEN ZHI | European Patent Office | 2022

    Free access

    Traffic signal control method based on multi-agent deep reinforcement learning

    LIN FENG / HE SHUAI / SHAO LANG et al. | European Patent Office | 2025

    Free access


    Reinforcement learning-based multi-agent system for network traffic signal control

    Arel, I. / Liu, C. / Urbanik, T. et al. | Tema Archive | 2010