  The advent of Urban Air Mobility (UAM) presents the scope for a
transformative shift in the domain of urban transportation. However, its
widespread adoption and economic viability depends in part on the ability to
optimally schedule the fleet of aircraft across vertiports in a UAM network,
under uncertainties attributed to airspace congestion, changing weather
conditions, and varying demands. This paper presents a comprehensive
optimization formulation of the fleet scheduling problem, while also
identifying the need for alternate solution approaches, since directly solving
the resulting integer nonlinear programming problem is computationally
prohibitive for daily fleet scheduling. Previous work has shown the
effectiveness of using (graph) reinforcement learning (RL) approaches to train
real-time executable policy models for fleet scheduling. However, such policies
can often be brittle on out-of-distribution scenarios or edge cases. Moreover,
training performance also deteriorates as the complexity (e.g., number of
constraints) of the problem increases. To address these issues, this paper
presents an imitation learning approach where the RL-based policy exploits
expert demonstrations yielded by solving the exact optimization using a Genetic
Algorithm. The policy model comprises Graph Neural Network (GNN) based encoders
that embed the space of vertiports and aircraft, Transformer networks to encode
demand, passenger fare, and transport cost profiles, and a Multi-head attention
(MHA) based decoder. Expert demonstrations are used through the Generative
Adversarial Imitation Learning (GAIL) algorithm. Interfaced with a UAM
simulation environment involving 8 vertiports and 40 aircrafts, in terms of the
daily profits earned reward, the new imitative approach achieves better mean
performance and remarkable improvement in the case of unseen worst-case
scenarios, compared to pure RL results.
