  Unmanned aerial vehicles (UAVs) are a highly promising technology with
diverse applications in wireless networks. One of their primary uses is the
collection of time-sensitive data from Internet of Things (IoT) devices. In
UAV-assisted networks, the Age-of-Information (AoI) serves as a fundamental
metric for quantifying data timeliness and freshness. In this work, we are
interested in a generalized AoI formulation, where each packet's age is
weighted based on its generation time. Our objective is to find the optimal
UAVs' trajectories and the subsets of selected devices such that the weighted
AoI is minimized. To address this challenge, we formulate the problem as a
Mixed-Integer Nonlinear Programming (MINLP), incorporating time and quality of
service constraints. To efficiently tackle this complex problem and minimize
communication overhead among UAVs, we propose a distributed approach. This
approach enables drones to make independent decisions based on locally acquired
data. Specifically, we reformulate our problem such that our objective function
is easily decomposed into individual rewards. The reformulated problem is
solved using a distributed implementation of Multi-Agent Reinforcement Learning
(MARL). Our empirical results show that the proposed decentralized approach
achieves results that are nearly equivalent to a centralized implementation
with a notable reduction in communication overhead.
