Real-world Ride-hailing Vehicle Repositioning using Deep Reinforcement Learning
Abstract
We present a new practical framework based on deep reinforcement learning and decision-time planning for real-world vehicle repositioning on ride-hailing (a type of mobility-on-demand, MoD) platforms. Our approach learns the spatiotemporal state-value function using a batch training algorithm with deep value networks. The optimal repositioning action is generated on-demand through value-based policy search, which combines planning and bootstrapping with the value networks. For the large-fleet problems, we develop several algorithmic features that we incorporate into our framework and that we demonstrate to induce coordination among the algorithmically-guided vehicles. We benchmark our algorithm with baselines in a ride-hailing simulation environment to demonstrate its superiority in improving income efficiency meausred by income-per-hour. We have also designed and run a real-world experiment program with regular drivers on a major ride-hailing platform. We have observed significantly positive results on key metrics comparing our method with experienced drivers who performed idle-time repositioning based on their own expertise.
Cite
@article{arxiv.2103.04555,
title = {Real-world Ride-hailing Vehicle Repositioning using Deep Reinforcement Learning},
author = {Yan Jiao and Xiaocheng Tang and Zhiwei Qin and Shuaiji Li and Fan Zhang and Hongtu Zhu and Jieping Ye},
journal= {arXiv preprint arXiv:2103.04555},
year = {2021}
}