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Technology advancements in wireless communications and high-performanceExtended Reality (XR) have empowered the developments of the Metaverse. Thedemand for the Metaverse applications and hence, real-time digital twinning ofreal-world scenes is increasing. Nevertheless, the replication of 2D physicalworld images into 3D virtual objects is computationally intensive and requirescomputation offloading. The disparity in transmitted object dimension (2D asopposed to 3D) leads to asymmetric data sizes in uplink (UL) and downlink (DL).To ensure the reliability and low latency of the system, we consider anasynchronous joint UL-DL scenario where in the UL stage, the smaller data sizeof the physical world images captured by multiple extended reality users (XUs)will be uploaded to the Metaverse Console (MC) to be construed and rendered. Inthe DL stage, the larger-size 3D virtual objects need to be transmitted back tothe XUs. We design a novel multi-agent reinforcement learning algorithmstructure, namely Asynchronous Actors Hybrid Critic (AAHC), to optimize thedecisions pertaining to computation offloading and channel assignment in the ULstage and optimize the DL transmission power in the DL stage. Extensiveexperiments demonstrate that compared to proposed baselines, AAHC obtainsbetter solutions with satisfactory training time.

Asynchronous Hybrid Reinforcement Learning for Latency and Reliability Optimization in the Metaverse over Wireless Communications