  In this paper, we present a comprehensive, in-depth survey of the literature
on reinforcement learning approaches to decision optimization problems in a
typical ridesharing system. Papers on the topics of rideshare matching, vehicle
repositioning, ride-pooling, routing, and dynamic pricing are covered. Most of
the literature has appeared in the last few years, and several core challenges
are to continue to be tackled: model complexity, agent coordination, and joint
optimization of multiple levers. Hence, we also introduce popular data sets and
open simulation environments to facilitate further research and development.
Subsequently, we discuss a number of challenges and opportunities for
reinforcement learning research on this important domain.
