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.
@article{arxiv.2105.01099,
title = {Reinforcement Learning for Ridesharing: An Extended Survey},
author = {Zhiwei Qin and Hongtu Zhu and Jieping Ye},
journal= {arXiv preprint arXiv:2105.01099},
year = {2022}
}