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.
@article{arxiv.2202.08972,
title = {Deep Reinforcement Learning Based Multi-Access Edge Computing Schedule for Internet of Vehicle},
author = {Xiaoyu Dai and Kaoru Ota and Mianxiong Dong},
journal= {arXiv preprint arXiv:2202.08972},
year = {2022}
}