  Order dispatching and driver repositioning (also known as fleet management)
in the face of spatially and temporally varying supply and demand are central
to a ride-sharing platform marketplace. Hand-crafting heuristic solutions that
account for the dynamics in these resource allocation problems is difficult,
and may be better handled by an end-to-end machine learning method. Previous
works have explored machine learning methods to the problem from a high-level
perspective, where the learning method is responsible for either repositioning
the drivers or dispatching orders, and as a further simplification, the drivers
are considered independent agents maximizing their own reward functions. In
this paper we present a deep reinforcement learning approach for tackling the
full fleet management and dispatching problems. In addition to treating the
drivers as individual agents, we consider the problem from a system-centric
perspective, where a central fleet management agent is responsible for
decision-making for all drivers.
