Multi-access edge computing (MEC) is a promising solution to the computation-intensive, low-latency rendering tasks of the metaverse. However, how to optimally allocate limited communication and computation resources at the edge to a large number of users in the metaverse is quite challenging. In this paper, we propose an adaptive edge resource allocation method based on multi-agent soft actor-critic with graph convolutional networks (SAC-GCN). Specifically, SAC-GCN models the multi-user metaverse environment as a graph where each agent is denoted by a node. Each agent learns the interplay between agents by graph convolutional networks with self-attention mechanism to further determine the resource usage for one user in the metaverse. The effectiveness of SAC-GCN is demonstrated through the analysis of user experience, balance of resource allocation, and resource utilization rate by taking a virtual city park metaverse as an example. Experimental results indicate that SAC-GCN outperforms other resource allocation methods in improving overall user experience, balancing resource allocation, and increasing resource utilization rate by at least 27%, 11%, and 8%, respectively.

翻译：多接入边缘计算（MEC）是应对元宇宙中计算密集且低延迟渲染任务的一种有前景的方案。然而，如何在边缘侧为元宇宙中大量用户最优地分配有限的通信与计算资源是一项极具挑战的任务。本文提出一种基于多智能体软演员-评论家与图卷积网络（SAC-GCN）的自适应边缘资源分配方法。具体而言，SAC-GCN将多用户元宇宙环境建模为一个图，其中每个智能体由一个节点表示。每个智能体通过带有自注意力机制的图卷积网络学习智能体间的相互作用，从而进一步确定元宇宙中一个用户的资源使用情况。以虚拟城市公园元宇宙为例，通过分析用户体验、资源分配平衡性和资源利用率，验证了SAC-GCN的有效性。实验结果表明，SAC-GCN在提升整体用户体验、平衡资源分配和提高资源利用率方面，分别至少优于其他资源分配方法27%、11%和8%。