Content Caching-Assisted Vehicular Edge Computing Using Multi-Agent Graph Attention Reinforcement Learning

TL;DR

This paper proposes a multi-agent graph attention reinforcement learning framework for content caching in vehicular edge computing, aiming to improve caching efficiency and reduce task latency in dynamic network environments.

Contribution

It introduces a novel MGARL-based caching scheme that leverages graph attention convolution to enhance cooperation among agents in VEC systems.

Findings

Improves caching resource utilization

Reduces long-term task computing latency

Outperforms baseline schemes in simulations

Abstract

In order to avoid repeated task offloading and realize the reuse of popular task computing results, we construct a novel content caching-assisted vehicular edge computing (VEC) framework. In the face of irregular network topology and unknown environmental dynamics, we further propose a multi-agent graph attention reinforcement learning (MGARL) based edge caching scheme, which utilizes the graph attention convolution kernel to integrate the neighboring nodes' features of each agent and further enhance the cooperation among agents. Our simulation results show that our proposed scheme is capable of improving the utilization of caching resources while reducing the long-term task computing latency compared to the baselines.