论文标题
在无线电动通信网络中，用于分布式资源管理的多代理深入学习学习
Multi-Agent Deep Reinforcement Learning for Distributed Resource Management in Wirelessly Powered Communication Networks
论文作者
论文摘要
本文研究了基于多机构无线电动通信网络（WPCN）的基于多代理的深入增强学习（MADRL）资源分配方法，其中多个混合访问点（H-aps）无线充电能量限制用户从中收集数据。我们设计了分布式增强学习策略，其中H-APS单独确定时间和功率分配变量。与需要在中央单元收集的全球信息的传统集中优化算法不同，拟议的MADRL技术将HAP模拟为HAP作为代理，仅基于其本地可观察到的状态。数值结果验证了所提出的方法可以实现集中算法的可比性能。
This paper studies multi-agent deep reinforcement learning (MADRL) based resource allocation methods for multi-cell wireless powered communication networks (WPCNs) where multiple hybrid access points (H-APs) wirelessly charge energy-limited users to collect data from them. We design a distributed reinforcement learning strategy where H-APs individually determine time and power allocation variables. Unlike traditional centralized optimization algorithms which require global information collected at a central unit, the proposed MADRL technique models an H-AP as an agent producing its action based only on its own locally observable states. Numerical results verify that the proposed approach can achieve comparable performance of the centralized algorithms.
