Reinforcement learning (RL) has recently been applied to solve railway timetabling problems. In this paper, we provide a detailed overview of the state-of-the-art research for RL in railway timetabling. Specifically, we categorize RL into basic RL and deep reinforcement learning (DRL), and further divide the research of railway timetabling into scheduling and rescheduling for exhaustively review and discussion. The present research on RL in railway timetabling is still in the primary stage and the scale of problems that can be solved is still limited. However, the applications of RL shows great promise and excitement, with significant potential for addressing various challenges in railway planning and management in the future.


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    Title :

    A Literature Review of Reinforcement Learning in Railway Timetabling


    Additional title:

    Lecture Notes in Civil Engineering


    Contributors:
    Meng, Lingyun (editor) / Qian, Yongsheng (editor) / Bai, Yun (editor) / Lv, Bin (editor) / Tang, Yuanjie (editor) / Wang, Yan (author) / Fan, Jiaming (author) / Han, Ruihao (author) / Chen, Angyang (author) / He, Junyuan (author)

    Conference:

    International Conference on Traffic and Transportation Studies ; 2024 ; Lanzhou, China August 23, 2024 - August 25, 2024



    Publication date :

    2024-11-14


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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