Resource Allocation in Vehicular Networks Based on Federated Multi-Agent Reinforcement Learning

Abstract

In this paper, we propose a distributed resource allocation scheme based on federated multi-agent deep reinforcement learning (Fed-MARL) to address the channel allocation and power control problem in vehicular networks. We tackle the formulated resource optimization problem by taking advantage of deep reinforcement learning and federated learning, to satisfy the different quality-of-service requirements for vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) links. Specifically, we propose to enhance traditional reinforcement learning methods, including both the deep Q network and proximal policy optimization, with federated learning, to obtain two efficient Fed-MARL-based resource allocation algorithms for vehicular networks. Simulation results show that our proposed resource allocation schemes exhibit superiority in both the total capacity of V2I links and the payload delivery rate of V2V links simultaneously, compared to other baselines without federated learning assistance.