  As intelligent transportation systems been implemented broadly and unmanned
arial vehicles (UAVs) can assist terrestrial base stations acting as
multi-access edge computing (MEC) to provide a better wireless network
communication for Internet of Vehicles (IoVs), we propose a UAVs-assisted
approach to help provide a better wireless network service retaining the
maximum Quality of Experience(QoE) of the IoVs on the lane. In the paper, we
present a Multi-Agent Graph Convolutional Deep Reinforcement Learning
(M-AGCDRL) algorithm which combines local observations of each agent with a
low-resolution global map as input to learn a policy for each agent. The agents
can share their information with others in graph attention networks, resulting
in an effective joint policy. Simulation results show that the M-AGCDRL method
enables a better QoE of IoTs and achieves good performance.
