  Autonomous Mobility-on-Demand (AMoD) systems represent an attractive
alternative to existing transportation paradigms, currently challenged by
urbanization and increasing travel needs. By centrally controlling a fleet of
self-driving vehicles, these systems provide mobility service to customers and
are currently starting to be deployed in a number of cities around the world.
Current learning-based approaches for controlling AMoD systems are limited to
the single-city scenario, whereby the service operator is allowed to take an
unlimited amount of operational decisions within the same transportation
system. However, real-world system operators can hardly afford to fully
re-train AMoD controllers for every city they operate in, as this could result
in a high number of poor-quality decisions during training, making the
single-city strategy a potentially impractical solution. To address these
limitations, we propose to formalize the multi-city AMoD problem through the
lens of meta-reinforcement learning (meta-RL) and devise an actor-critic
algorithm based on recurrent graph neural networks. In our approach, AMoD
controllers are explicitly trained such that a small amount of experience
within a new city will produce good system performance. Empirically, we show
how control policies learned through meta-RL are able to achieve near-optimal
performance on unseen cities by learning rapidly adaptable policies, thus
making them more robust not only to novel environments, but also to
distribution shifts common in real-world operations, such as special events,
unexpected congestion, and dynamic pricing schemes.
