  An unmanned aerial vehicle (UAV)-aided mobile edge computing (MEC) framework
is proposed, where several UAVs having different trajectories fly over the
target area and support the user equipments (UEs) on the ground. We aim to
jointly optimize the geographical fairness among all the UEs, the fairness of
each UAV' UE-load and the overall energy consumption of UEs. The above
optimization problem includes both integer and continues variables and it is
challenging to solve. To address the above problem, a multi-agent deep
reinforcement learning based trajectory control algorithm is proposed for
managing the trajectory of each UAV independently, where the popular
Multi-Agent Deep Deterministic Policy Gradient (MADDPG) method is applied.
Given the UAVs' trajectories, a low-complexity approach is introduced for
optimizing the offloading decisions of UEs. We show that our proposed solution
has considerable performance over other traditional algorithms, both in terms
of the fairness for serving UEs, fairness of UE-load at each UAV and energy
consumption for all the UEs.
