Multi-agent reinforcement learning (MARL) based methods for adaptive traffic signal control (ATSC) have shown promising potentials to solve the heavy traffic problems. The existing MARL methods adopt centralized or distributed strategies. The former only models the environment as an agent and suffers from the exponential growth of action and state space. The latter extends the independent reinforcement learning methods, such as DQN, to multiple interactions directly or propagates information, such as state and policy, without taking their qualities into account. In this paper, we propose a multi-agent transfer reinforcement learning method to enhance the performance of MARL for ATSC, which is termed as multi-agent transfer soft actor-critic with the multi-view encoder (MT-SAC). The MT-SAC combines centralized and distributed strategies. In MT-SAC, we propose a multi-view state encoder and a transfer learning paradigm with guidance. The encoder processes input states from multiple perspectives and uses an attention mechanism to weigh the neighborhood information. While the paradigm enables the agents to handle different conditions for improving generalization abilities by transfer learning. Experimental studies on different scale road networks show that the MT-SAC outperforms the state-of-the-art algorithms and makes the traffic signal controllers more collaborative and robust.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Multi-Agent Transfer Reinforcement Learning With Multi-View Encoder for Adaptive Traffic Signal Control


    Contributors:
    Ge, Hongwei (author) / Gao, Dongwan (author) / Sun, Liang (author) / Hou, Yaqing (author) / Yu, Chao (author) / Wang, Yuxin (author) / Tan, Guozhen (author)


    Publication date :

    2022-08-01


    Size :

    15488080 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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

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

    Free access

    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 for integrated and networked adaptive traffic signal control

    EL-TANTAWY SAMAH / ABDULHAI BAHER | European Patent Office | 2017

    Free access

    MULTI-AGENT REINFORCEMENT LEARNING FOR INTEGRATED AND NETWORKED ADAPTIVE TRAFFIC SIGNAL CONTROL

    EL-TANTAWY SAMAH / ABDULHAI BAHER | European Patent Office | 2018

    Free access