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.
Reinforcement Learning for Ridesharing: A Survey
19.09.2021
311581 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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