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

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