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.

翻译：元宇宙"边玩边赚"游戏近年来日益流行，玩家可通过游戏赚取可兑换现实收益的游戏代币。随着增强现实技术的发展，用户可在元宇宙中参与AR游戏。然而，这类高分辨率游戏计算密集，游戏画面需从移动设备卸载至边缘服务器进行处理。本文研究了一项优化问题：元宇宙服务提供商需通过优化下行链路用户设备-元宇宙基站分配策略与上行链路传输功率选择，在最小化游戏画面下行传输延迟、上行数据延迟及用户设备最差电池消耗的同时，最大化受分辨率影响的用户设备最低游戏收益潜力。下行与上行传输采用异步执行方式。我们提出一种多智能体损失共享强化学习模型应对这一异步非对称问题，通过与其他基线模型对比验证了该模型的优越性。最后进行的多变量优化权重分析表明，所提出的MALS算法可有效解决联合优化问题。