Continual Meta-Reinforcement Learning for UAV-Aided Vehicular Wireless Networks

TL;DR

This paper introduces a continual meta-reinforcement learning approach to optimize UAV trajectories in vehicular networks, enabling faster adaptation to changing traffic conditions compared to traditional methods.

Contribution

The paper proposes using continual meta-RL with CoMPS to transfer knowledge across traffic configurations, improving efficiency in UAV trajectory optimization.

Findings

Significant efficiency gains over conventional RL

Effective transfer of information across traffic scenarios

Reduced time for policy optimization

Abstract

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.