  We propose a novel approach to optimize fleet management by combining
multi-agent reinforcement learning with graph neural network. To provide
ride-hailing service, one needs to optimize dynamic resources and demands over
spatial domain. While the spatial structure was previously approximated with a
regular grid, our approach represents the road network with a graph, which
better reflects the underlying geometric structure. Dynamic resource allocation
is formulated as multi-agent reinforcement learning, whose action-value
function (Q function) is approximated with graph neural networks. We use
stochastic policy update rule over the graph with deep Q-networks (DQN), and
achieve superior results over the greedy policy update. We design a realistic
simulator that emulates the empirical taxi call data, and confirm the
effectiveness of the proposed model under various conditions.
