用于无人机元宇宙中孪生迁移的微型多智能体深度强化学习：一种多领导者多跟随者 Stackelberg 博弈方法
Tiny Multi-Agent DRL for Twins Migration in UAV Metaverses: A Multi-Leader Multi-Follower Stackelberg Game Approach
AI总结：
针对无人机元宇宙中实时孪生迁移的 RSU 选择与带宽优化问题，提出结合剪枝的多领导者多跟随者 Stackelberg 博弈及 Tiny MADRL 算法，以更小模型实现更优迁移性能。
摘要：
无人机（UAV）与元宇宙之间的协同正在催生一种名为无人机元宇宙的新兴范式，该范式构建了融合物理空间与虚拟空间的统一生态系统，改变了无人机交互和虚拟探索方式。无人机孪生（UT）作为无人机的数字孪生，通过使无人机应用更具沉浸感、真实感和信息量而革新了无人机应用；它们被部署并更新在地面基站（例如路侧单元（RSU））上，以便为无人机元宇宙用户（UMU）提供元宇宙服务。由于无人机的动态移动性以及 RSU 有限的通信覆盖范围，必须执行实时 UT 迁移，以确保为 UMU 提供无缝的沉浸式体验。然而，选择合适的 RSU 并优化所需带宽，对于实现可靠且高效的 UT 迁移而言具有挑战性。为应对这些挑战，我们提出了一种基于剪枝技术、以微型机器学习为基础的 Stackelberg 博弈框架，用于无人机元宇宙中的高效 UT 迁移。具体而言，我们构建了一个多领导者多跟随者 Stackelberg 模型，并在无人机的效用中考虑了一种新的 UMU 沉浸度指标。随后，我们设计了一种微型多智能体深度强化学习（Tiny MADRL）算法，以获得表示最优博弈解的微型网络。具体来说，actor-critic 网络利用剪枝技术减少网络参数数量，并实现模型规模和计算量的降低，从而支持 Tiny MADRL 的高效实现。数值结果表明，我们提出的方案相比传统方案具有更优的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
