  Mobility on demand (MoD) systems show great promise in realizing flexible and
efficient urban transportation. However, significant technical challenges arise
from operational decision making associated with MoD vehicle dispatch and fleet
rebalancing. For this reason, operators tend to employ simplified algorithms
that have been demonstrated to work well in a particular setting. To help
bridge the gap between novel and existing methods, we propose a modular
framework for fleet rebalancing based on model-free reinforcement learning (RL)
that can leverage an existing dispatch method to minimize system cost. In
particular, by treating dispatch as part of the environment dynamics, a
centralized agent can learn to intermittently direct the dispatcher to
reposition free vehicles and mitigate against fleet imbalance. We formulate RL
state and action spaces as distributions over a grid partitioning of the
operating area, making the framework scalable and avoiding the complexities
associated with multiagent RL. Numerical experiments, using real-world trip and
network data, demonstrate that this approach has several distinct advantages
over baseline methods including: improved system cost; high degree of
adaptability to the selected dispatch method; and the ability to perform
scale-invariant transfer learning between problem instances with similar
vehicle and request distributions.
