Reinforcement learning (RL), given its adaptability and generality, has great potential to optimize online traffic signal control strategies. Although studies have proposed various RL-based signal controllers and validated them offline, very few examine the robustness of the trained RL-based controllers when deployed in a dynamic traffic environment. This paper proposed a multi-agent reinforcement learning algorithm for traffic signal control and developed a general multi-agent optimization simulation tool to evaluate different signal control methods. A transfer learning technique is applied to test the robustness of the proposed algorithm and traditional control approaches under different traffic scenarios, including stochastic traffic flow, varying traffic volume, and uncertain sensor data. The experimental results show that the proposed RL-based control method is robust under stochastic traffic flow and variation traffic demand patterns, and it outperforms the fixed-time and vehicle-actuated methods. However, it is unstable in the case of highly noisy sensor data. Also, the trained RL-based controller can continuously learn online and improve its performance by interacting with the dynamic traffic environment, especially when the traffic is congested, and the sensor has noisy observations.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Multi-Agent Reinforcement Learning for Traffic Signal Control: Algorithms and Robustness Analysis


    Contributors:
    Wu, Chunliang (author) / Ma, Zhenliang (author) / Kim, Inhi (author)


    Publication date :

    2020-09-20


    Size :

    654335 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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

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

    Free access

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

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


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

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

    Free access

    Multi-agent reinforcement learning method for fair adaptive traffic signal control

    ZHANG CHENGWEI / FANG WANQING / ZHAO XINTIAN | European Patent Office | 2023

    Free access