标题： 基于多智能体图强化学习的多无人机辅助物联网按需无线能量传输 显示英文标题

标题： Multi-Agent Graph Reinforcement Learning based On-Demand Wireless Energy Transfer in Multi-UAV-aided IoT Network

摘要： 本文提出了一种新的按需无线能量传输（WET）方案，该方案适用于多架无人机（UAV）。与现有研究仅追求物联网（IoT）设备的总能量或最小能量收获最大化的做法不同，现有研究几乎没有考虑IoT设备自身的能量需求，我们提出了一种新的度量标准，称为能量饥饿水平（HoE），该度量标准基于IoT设备所需能量与其从无人机获得的能量之间的能量差距，反映了每个IoT设备随时间变化的能量需求。为了最小化未满足能量需求的IoT设备的整体HoE，我们在无人机的实际移动性和能量约束下，最优地确定所有无人机随时间的轨迹和WET决策。尽管所提出的問題具有很高的复杂性，但通过挖掘无人机之间在协作WET中的自我注意力，我们提出了基于多智能体图强化学习（MAGRL）的方法。通过MAGRL模型的离线训练，其中中央控制器的全局训练指导每个无人机代理的局部训练，每个无人机随后根据训练良好的局部神经网络分布式地确定其轨迹和WET。仿真结果表明，所提出的基于MAGRL的方法在满足IoT设备的能量需求方面优于各种基准。 显示英文摘要

摘要： This paper proposes a new on-demand wireless energy transfer (WET) scheme of multiple unmanned aerial vehicles (UAVs). Unlike the existing studies that simply pursuing the total or the minimum harvested energy maximization at the Internet of Things (IoT) devices, where the IoT devices' own energy requirements are barely considered, we propose a new metric called the hungry-level of energy (HoE), which reflects the time-varying energy demand of each IoT device based on the energy gap between its required energy and the harvested energy from the UAVs. With the purpose to minimize the overall HoE of the IoT devices whose energy requirements are not satisfied, we optimally determine all the UAVs' trajectories and WET decisions over time, under the practical mobility and energy constraints of the UAVs. Although the proposed problem is of high complexity to solve, by excavating the UAVs' self-attentions for their collaborative WET, we propose the multiagent graph reinforcement learning (MAGRL) based approach. Through the offline training of the MAGRL model, where the global training at the central controller guides the local training at each UAV agent, each UAV then distributively determines its trajectory and WET based on the well-trained local neural networks. Simulation results show that the proposed MAGRL-based approach outperforms various benchmarks for meeting the IoT devices' energy requirements.