The synergy between Unmanned Aerial Vehicles (UAVs) and metaverses is giving rise to an emerging paradigm named UAV metaverses, which create a unified ecosystem that blends physical and virtual spaces, transforming drone interaction and virtual exploration. UAV Twins (UTs), as the digital twins of UAVs that revolutionize UAV applications by making them more immersive, realistic, and informative, are deployed and updated on ground base stations, e.g., RoadSide Units (RSUs), to offer metaverse services for UAV Metaverse Users (UMUs). Due to the dynamic mobility of UAVs and limited communication coverages of RSUs, it is essential to perform real-time UT migration to ensure seamless immersive experiences for UMUs. However, selecting appropriate RSUs and optimizing the required bandwidth is challenging for achieving reliable and efficient UT migration. To address the challenges, we propose a tiny machine learning-based Stackelberg game framework based on pruning techniques for efficient UT migration in UAV metaverses. Specifically, we formulate a multi-leader multi-follower Stackelberg model considering a new immersion metric of UMUs in the utilities of UAVs. Then, we design a Tiny Multi-Agent Deep Reinforcement Learning (Tiny MADRL) algorithm to obtain the tiny networks representing the optimal game solution. Specifically, the actor-critic network leverages the pruning techniques to reduce the number of network parameters and achieve model size and computation reduction, allowing for efficient implementation of Tiny MADRL. Numerical results demonstrate that our proposed schemes have better performance than traditional schemes.

翻译：无人机与元宇宙的协同催生了名为无人机元宇宙的新兴范式，该范式创建了融合物理与虚拟空间的统一生态系统，革新了无人机交互与虚拟探索。作为无人机数字孪生，无人机孪生通过增强沉浸感、真实感和信息丰富度彻底改变了无人机应用，它们被部署并更新于地面基站（如路侧单元）上，为无人机元宇宙用户提供元宇宙服务。由于无人机的动态移动性和路侧单元的有限通信覆盖范围，必须执行实时无人机孪生迁移以确保用户获得无缝沉浸式体验。然而，选择合适的路侧单元并优化所需带宽对于实现可靠高效的无人机孪生迁移具有挑战性。为应对这些挑战，我们提出一种基于剪枝技术的小规模机器学习斯塔克尔伯格博弈框架，用于无人机元宇宙中的高效孪生迁移。具体而言，我们建立了考虑无人机效用中用户新沉浸度指标的多领导者-多追随者斯塔克尔伯格模型。随后，设计了一种小规模多智能体深度强化学习算法，以获得代表最优博弈解的小规模网络。具体而言，行动者-评论家网络利用剪枝技术减少网络参数量，实现模型规模与计算量缩减，从而支持小规模多智能体深度强化学习的高效实现。数值结果表明，所提方案相较于传统方案具有更优性能。