User-centric Heterogeneous-action Deep Reinforcement Learning for Virtual Reality in the Metaverse over Wireless Networks

Abstract

The Metaverse emerging as maturing technologies are empowering the different facets. Virtual Reality (VR) technologies serve as the backbone of the virtual universe within the Metaverse to offer a highly immersive user experience. As mobility is emphasized in the Metaverse context, VR devices reduce their weights at the sacrifice of local computation abilities. In this paper, for a system consisting of a Metaverse server and multiple VR users, we consider two cases of (i) the server generating frames and transmitting them to users, and (ii) users generating frames locally and thus consuming device energy. As Metaverse emphasizes on the accessibility for all users anywhere and anytime, the users can have totally different characteristics, devices and demands. In this paper, the channel access arrangement (including the decisions on frame generation location), and transmission powers for the downlink communications from the server to the users are jointly optimized by our proposed user-centric Deep Reinforcement Learning (DRL) algorithm, namely User-centric Critic with Heterogenous Actors (UCHA). Comprehensive experiments demonstrate that our UCHA algorithm leads to remarkable results under various requirements and constraints.