  Large-scale online ride-sharing platforms have substantially transformed our
lives by reallocating transportation resources to alleviate traffic congestion
and promote transportation efficiency. An efficient fleet management strategy
not only can significantly improve the utilization of transportation resources
but also increase the revenue and customer satisfaction. It is a challenging
task to design an effective fleet management strategy that can adapt to an
environment involving complex dynamics between demand and supply. Existing
studies usually work on a simplified problem setting that can hardly capture
the complicated stochastic demand-supply variations in high-dimensional space.
In this paper we propose to tackle the large-scale fleet management problem
using reinforcement learning, and propose a contextual multi-agent
reinforcement learning framework including three concrete algorithms to achieve
coordination among a large number of agents adaptive to different contexts. We
show significant improvements of the proposed framework over state-of-the-art
approaches through extensive empirical studies.
