用深度强化学习优化元宇宙边缘计算，提升数字孪生响应速度
Maximizing the Promptness of Metaverse Systems using Edge Computing by Deep Reinforcement Learning
- 通过深度强化学习动态调度任务至边缘服务器
- 实验表明算法在动态环境下可保障数字孪生的及时性
- 适合研究元宇宙实时系统与边缘智能的学者
元宇宙与数字孪生（DT）正成为构建未来数字世界的重要方向。本文探讨了深度强化学习（DRL）在基于数字孪生的元宇宙系统中的应用优势。系统包含多个元宇宙用户设备，负责从现实世界采集数据并传输至虚拟世界；一个元宇宙虚拟接入点（MVAP）负责数据处理，并将任务卸载至边缘计算服务器。所提模型运行于参数随时间动态变化的复杂环境中。实验结果表明，该DRL算法能有效实现任务卸载，确保数字孪生在动态环境下的及时性与可靠性。
原文摘要 · Abstract (English)
Metaverse and Digital Twin (DT) have attracted much academic and industrial attraction to approach the future digital world. This paper introduces the advantages of deep reinforcement learning (DRL) in assisting Metaverse system-based Digital Twin. In this system, we assume that it includes several Metaverse User devices collecting data from the real world to transfer it into the virtual world, a Metaverse Virtual Access Point (MVAP) undertaking the processing of data, and an edge computing server that receives the offloading data from the MVAP. The proposed model works under a dynamic environment with various parameters changing over time. The experiment results show that our proposed DRL algorithm is suitable for offloading tasks to ensure the promptness of DT in a dynamic environment.
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