  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.
