论文标题
与合作无人机的分散计算卸载：多代理深度强化学习观点
Decentralized Computation Offloading With Cooperative UAVs: Multi-Agent Deep Reinforcement Learning Perspective
论文作者
论文摘要
有限的计算资源（IOT）节点在处理输入数据中产生了延迟的延迟。这触发了新的研究机会，即在边缘服务器处理物联网设备的密集计算的任务卸载系统。在现有基站部署计算服务器可能不足以支持在恶劣环境中运行的物联网节点。这要求将移动边缘服务器安装在提供按需移动边缘计算（MEC）服务的无人机（UAV）上。无人机的时变卸载需求和移动性需要对所有时间实例的优化变量进行联合设计。因此，在线决策机制对于无人机的MEC网络至关重要。本文概述了最近的深入强化学习（DRL）方法，其中以在线方式做出有关无人机和物联网节点的决策。具体而言，从DRL的角度解决了任务卸载，资源分配和无人机移动性的联合优化。对于分散的实施，提出了一种多代理DRL方法，其中多个智能无人机合作地确定其计算和通信政策而无需中央协调。数值结果表明，所提出的分散学习策略优于现有的DRL解决方案。所提出的框架阐明了分散的DRL技术在设计自组织的IoT网络时的生存能力。
Limited computing resources of internet-of-things (IoT) nodes incur prohibitive latency in processing input data. This triggers new research opportunities toward task offloading systems where edge servers handle intensive computations of IoT devices. Deploying the computing servers at existing base stations may not be sufficient to support IoT nodes operating in a harsh environment. This requests mobile edge servers to be mounted on unmanned aerial vehicles (UAVs) that provide on-demand mobile edge computing (MEC) services. Time-varying offloading demands and mobility of UAVs need a joint design of the optimization variables for all time instances. Therefore, an online decision mechanism is essential for UAV-aided MEC networks. This article presents an overview of recent deep reinforcement learning (DRL) approaches where decisions about UAVs and IoT nodes are taken in an online manner. Specifically, joint optimization over task offloading, resource allocation, and UAV mobility is addressed from the DRL perspective. For the decentralized implementation, a multi-agent DRL method is proposed where multiple intelligent UAVs cooperatively determine their computations and communication policies without central coordination. Numerical results demonstrate that the proposed decentralized learning strategy is superior to existing DRL solutions. The proposed framework sheds light on the viability of the decentralized DRL techniques in designing self-organizing IoT networks.
