Reinforcement Learning for Ridesharing: A Survey. (arXiv:2105.01099v1 [cs.LG])

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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