Deep Reinforcement Learning Based Multi-Access Edge Computing Schedule for Internet of Vehicle
This addresses network optimization for intelligent transportation systems, though it appears incremental as it combines existing techniques (multi-agent RL, graph networks) for a specific application.
The paper tackles the problem of optimizing wireless network service for Internet of Vehicles using UAV-assisted multi-access edge computing, proposing a multi-agent graph convolutional deep reinforcement learning algorithm that achieves better Quality of Experience for IoTs in simulations.
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.