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arXiv · 2202.08972

Deep Reinforcement Learning Based Multi-Access Edge Computing Schedule for Internet of Vehicle

Xiaoyu Dai·Kaoru Ota·Mianxiong Dong
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Abstract

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.

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Xiaoyu Dai, Kaoru Ota, Mianxiong Dong. 2022-02-15. Deep Reinforcement Learning Based Multi-Access Edge Computing Schedule for Internet of Vehicle. https://arxiv.org/abs/2202.08972

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