In this paper, we present a comprehensive, in-depth survey of the literature on reinforcement learning approaches to ridesharing problems. Papers on the topics of rideshare matching, vehicle repositioning, ride-pooling, and dynamic pricing are covered. Popular data sets and open simulation environments are also introduced. Subsequently, we discuss a number of challenges and opportunities for reinforcement learning research on this important domain.


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    Title :

    Reinforcement Learning for Ridesharing: A Survey


    Contributors:


    Publication date :

    2021-09-19


    Size :

    311581 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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