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

• URL: http://arxiv.org/abs/2202.08972v1
• Date: Tue, 15 Feb 2022 17:14:58 GMT
• Title: Deep Reinforcement Learning Based Multi-Access Edge Computing Schedule for Internet of Vehicle
• Authors: Xiaoyu Dai, Kaoru Ota, Mianxiong Dong
• Abstract summary: We propose a UAVs-assisted approach to help provide a better wireless network service retaining the maximum Quality of Experience (QoE) of the Internet of Vehicles (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.
• Score: 16.619839349229437
• License: http://creativecommons.org/licenses/by/4.0/
• 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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