Twin-Timescale 3C Resource Allocation for Semantic-Aware Vehicular Edge Computing Using Multi-Agent Graph Reinforcement Learning

Abstract

Semantic-aware edge computing has exhibited tremendous potential for reducing communication-computing-caching (3C) resource costs in vehicular networks through task-oriented semantic extraction. However, environmental dynamics and uncertainties across heterogeneous timescales pose critical challenges for 3C resource allocation in semantic-aware vehicular edge computing (VEC) networks. To this end, this paper investigates a joint semantic content caching and semantic task offloading problem in twin-timescale semantic-aware VEC scenarios, aiming to maximize the long-term utility tradeoff between task execution latency and semantic cache hit ratio. First, twin-timescale Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs) are established, where semantic content caching is optimized on a large timescale, while semantic offloading and bandwidth allocation policies are learned on a small timescale. Subsequently, a novel twin-timescale 3C resource allocation solution based on multi-agent graph reinforcement learning method is proposed. Specifically, a Graphical Partial Reward Decoupling-aided Multi-Agent Proximal Policy Optimization (GPRD-MAPPO) algorithm is proposed, which incorporates graph attention networks (GAT) and credit assignment mechanism to decouple the irrelevant agents in cooperative learning by dynamically identifying inter-agent graphical dependencies. Our simulation results verify the superiority of the proposed solution in reducing task execution latency and improving semantic cache hit ratio over the benchmarks in varying numbers of vehicles and diverse task characteristics.