  This paper investigates the autonomous control of massive unmanned aerial
vehicles (UAVs) for mission-critical applications (e.g., dispatching many UAVs
from a source to a destination for firefighting). Achieving their fast travel
and low motion energy without inter-UAV collision under wind perturbation is a
daunting control task, which incurs huge communication energy for exchanging
UAV states in real time. We tackle this problem by exploiting a mean-field game
(MFG) theoretic control method that requires the UAV state exchanges only once
at the initial source. Afterwards, each UAV can control its acceleration by
locally solving two partial differential equations (PDEs), known as the
Hamilton-Jacobi-Bellman (HJB) and Fokker-Planck-Kolmogorov (FPK) equations.
This approach, however, brings about huge computation energy for solving the
PDEs, particularly under multi-dimensional UAV states. We address this issue by
utilizing a machine learning (ML) method where two separate ML models
approximate the solutions of the HJB and FPK equations. These ML models are
trained and exploited using an online gradient descent method with low
computational complexity. Numerical evaluations validate that the proposed ML
aided MFG theoretic algorithm, referred to as MFG learning control, is
effective in collision avoidance with low communication energy and acceptable
computation energy.
