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.