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The Metaverse is emerging as maturing technologies are empowering thedifferent facets. Virtual Reality (VR) technologies serve as the backbone ofthe virtual universe within the Metaverse to offer a highly immersive userexperience. As mobility is emphasized in the Metaverse context, VR devicesreduce their weights at the sacrifice of local computation abilities. In thispaper, for a system consisting of a Metaverse server and multiple VR users, weconsider two cases of (i) the server generating frames and transmitting them tousers, and (ii) users generating frames locally and thus consuming deviceenergy. Moreover, in our multi-user VR scenario for the Metaverse, users havedifferent characteristics and demands for Frames Per Second (FPS). Then thechannel access arrangement (including the decisions on frame generationlocation), and transmission powers for the downlink communications from theserver to the users are jointly optimized to improve the utilities of users.This joint optimization is addressed by deep reinforcement learning (DRL) withheterogeneous actions. Our proposed user-centric DRL algorithm is calledUser-centric Critic with Heterogenous Actors (UCHA). Extensive experimentsdemonstrate that our UCHA algorithm leads to remarkable results under variousrequirements and constraints.

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