  Autonomous mobility-on-demand (AMoD) systems represent a rapidly developing
mode of transportation wherein travel requests are dynamically handled by a
coordinated fleet of robotic, self-driving vehicles. Given a graph
representation of the transportation network - one where, for example, nodes
represent areas of the city, and edges the connectivity between them - we argue
that the AMoD control problem is naturally cast as a node-wise decision-making
problem. In this paper, we propose a deep reinforcement learning framework to
control the rebalancing of AMoD systems through graph neural networks.
Crucially, we demonstrate that graph neural networks enable reinforcement
learning agents to recover behavior policies that are significantly more
transferable, generalizable, and scalable than policies learned through other
approaches. Empirically, we show how the learned policies exhibit promising
zero-shot transfer capabilities when faced with critical portability tasks such
as inter-city generalization, service area expansion, and adaptation to
potentially complex urban topologies.
