The Metaverse is 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. Moreover, in our multi-user VR scenario for the Metaverse, users have different characteristics and demands for Frames Per Second (FPS). Then 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 to improve the utilities of users. This joint optimization is addressed by deep reinforcement learning (DRL) with heterogeneous actions. Our proposed user-centric DRL algorithm is called User-centric Critic with Heterogenous Actors (UCHA). Extensive experiments demonstrate that our UCHA algorithm leads to remarkable results under various requirements and constraints.

翻译：随着技术日益成熟，元宇宙的各个维度正逐步被赋能。虚拟现实（VR）技术作为元宇宙中虚拟宇宙的支柱，旨在提供高度沉浸式的用户体验。由于元宇宙场景强调移动性，VR设备在牺牲本地计算能力的前提下减轻了重量。针对包含一个元宇宙服务器与多个VR用户的系统，本文考虑两种情形：（i）服务器生成帧并传输给用户；（ii）用户本地生成帧，因此消耗设备能量。此外，在面向元宇宙的多用户VR场景中，用户对每秒帧数（FPS）具有不同的特征与需求。我们联合优化信道接入安排（包括帧生成位置的决策）以及从服务器到用户的下行链路传输功率，以提升用户效用。通过异构动作的深度强化学习（DRL）解决该联合优化问题。我们提出的以用户为中心的DRL算法称为“以用户为中心的异构行为者评论家”（UCHA）。大量实验表明，在各种需求与约束条件下，UCHA算法均能取得显著效果。