  The Metaverse play-to-earn games have been gaining popularity as they enable
players to earn in-game tokens which can be translated to real-world profits.
With the advancements in augmented reality (AR) technologies, users can play AR
games in the Metaverse. However, these high-resolution games are
compute-intensive, and in-game graphical scenes need to be offloaded from
mobile devices to an edge server for computation. In this work, we consider an
optimization problem where the Metaverse Service Provider (MSP)'s objective is
to reduce downlink transmission latency of in-game graphics, the latency of
uplink data transmission, and the worst-case (greatest) battery charge
expenditure of user equipments (UEs), while maximizing the worst-case (lowest)
UE resolution-influenced in-game earning potential through optimizing the
downlink UE-Metaverse Base Station (UE-MBS) assignment and the uplink
transmission power selection. The downlink and uplink transmissions are then
executed asynchronously. We propose a multi-agent, loss-sharing (MALS)
reinforcement learning model to tackle the asynchronous and asymmetric problem.
We then compare the MALS model with other baseline models and show its
superiority over other methods. Finally, we conduct multi-variable optimization
weighting analyses and show the viability of using our proposed MALS algorithm
to tackle joint optimization problems.
