Tiny Multi-Agent DRL for Twins Migration in UAV Metaverses: A Multi-Leader Multi-Follower Stackelberg Game Approach
IEEE Internet of Things Journal(2024)
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.
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关键词
Metaverses,UAV twins,Stackelberg game,multi-agent deep reinforcement learning,pruning techniques
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