通过无人机集群实现大规模物联网中的信息年龄最小化：一种多智能体强化学习方法
Age Minimization in Massive IoT via UAV Swarm: A Multi-agent Reinforcement Learning Approach
AI总结：
针对无人机集群服务大规模物联网带来的高维决策问题，本文采用多智能体深度强化学习最小化全网信息年龄，并证明协作与部分协作方案优于集中式方法。
摘要：
在许多大规模物联网通信场景中，物联网设备需要来自动态单元的覆盖，这些单元能够移动到靠近物联网设备的位置，并降低上行链路能耗。一种稳健的解决方案是部署大量无人机（无人机集群），为物联网网络提供覆盖和更好的视距（LoS）链路。然而，研究这些具有大量服务单元的大规模物联网场景会产生高复杂度的高维问题。本文应用多智能体深度强化学习来解决因部署无人机集群从物联网设备收集新鲜信息而产生的高维问题。目标是最小化物联网网络中的整体信息年龄。结果表明，协作式和部分协作式多智能体深度强化学习方法都能够优于高复杂度的集中式深度强化学习方法，后者在大规模网络中束手无策。
英文摘要：
In many massive IoT communication scenarios, the IoT devices require coverage from dynamic units that can move close to the IoT devices and reduce the uplink energy consumption. A robust solution is to deploy a large number of UAVs (UAV swarm) to provide coverage and a better line of sight (LoS) for the IoT network. However, the study of these massive IoT scenarios with a massive number of serving units leads to high dimensional problems with high complexity. In this paper, we apply multi-agent deep reinforcement learning to address the high-dimensional problem that results from deploying a swarm of UAVs to collect fresh information from IoT devices. The target is to minimize the overall age of information in the IoT network. The results reveal that both cooperative and partially cooperative multi-agent deep reinforcement learning approaches are able to outperform the high-complexity centralized deep reinforcement learning approach, which stands helpless in large-scale networks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
