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：元宇宙的“边玩边赚”游戏日益流行，玩家可通过游戏内代币获取实际收益。随着增强现实（AR）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，需将游戏图形场景从移动设备卸载至边缘服务器进行处理。本文研究了优化问题：元宇宙服务提供商（MSP）旨在通过优化下行链路用户设备-元宇宙基站（UE-MBS）分配与上行链路传输功率选择，降低游戏图形的下行链路传输延迟、上行数据延迟及用户设备（UE）的最差（最大）电池消耗，同时最大化受UE分辨率影响的最差（最小）游戏内收益潜力。下行与上行传输异步执行。我们提出了一种多智能体损失共享（MALS）强化学习模型，以解决异步与非对称问题。将MALS模型与其他基线模型对比后，证明了其优越性。最后，通过多变量优化权重分析，验证了MALS算法在联合优化问题中的可行性。