论文标题
无人机车辆无线网络的持续荟萃方面学习
Continual Meta-Reinforcement Learning for UAV-Aided Vehicular Wireless Networks
论文作者
论文摘要
无人驾驶基站（UABSS）可以部署在车辆无线网络中，以支持应用程序（通过车辆到设施（V2X）服务）的应用。此类系统中的一个关键问题是设计算法，该算法可以有效地优化UAB的轨迹，以最大程度地提高覆盖范围。在现有的解决方案中，通常通过常规加固学习（RL）从头开始进行此类优化。在本文中，我们建议使用连续的元RL作为一种将信息从先前经验丰富的流量配置转移到新条件的手段，以减少优化UABS策略所需的时间。通过持续的元策略搜索（COMP）策略，与常规RL相比，我们表现出显着的效率提高，以及幼稚的转移学习方法。
Unmanned aerial base stations (UABSs) can be deployed in vehicular wireless networks to support applications such as extended sensing via vehicle-to-everything (V2X) services. A key problem in such systems is designing algorithms that can efficiently optimize the trajectory of the UABS in order to maximize coverage. In existing solutions, such optimization is carried out from scratch for any new traffic configuration, often by means of conventional reinforcement learning (RL). In this paper, we propose the use of continual meta-RL as a means to transfer information from previously experienced traffic configurations to new conditions, with the goal of reducing the time needed to optimize the UABS's policy. Adopting the Continual Meta Policy Search (CoMPS) strategy, we demonstrate significant efficiency gains as compared to conventional RL, as well as to naive transfer learning methods.
