Summary of Tiny Multi-agent Drl For Twins Migration in Uav Metaverses: a Multi-leader Multi-follower Stackelberg Game Approach, by Jiawen Kang et al.
Tiny Multi-Agent DRL for Twins Migration in UAV Metaverses: A Multi-Leader Multi-Follower Stackelberg Game Approach
by Jiawen Kang, Yue Zhong, Minrui Xu, Jiangtian Nie, Jinbo Wen, Hongyang Du, Dongdong Ye, Xumin Huang, Dusit Niyato, Shengli Xie
First submitted to arxiv on: 18 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Science and Game Theory (cs.GT)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The emerging paradigm of UAV metaverses combines physical and virtual spaces, revolutionizing drone interaction and virtual exploration. This fusion enables immersive, realistic, and informative experiences for users. To achieve seamless transitions between physical and virtual environments, digital twins (UAV Twins) are deployed on ground base stations, such as RoadSide Units. However, real-time UT migration is crucial due to UAVs’ dynamic mobility and limited communication coverages. A machine learning-based framework addresses this challenge by optimizing bandwidth requirements and selecting appropriate RSUs. We propose a tiny Stackelberg game framework based on pruning techniques for efficient UT migration in UAV metaverses. Our approach leverages multi-leader multi-follower Stackelberg models, considering immersion metrics of users in UAV utilities. A Tiny Multi-Agent Deep Reinforcement Learning algorithm is designed to obtain optimal game solutions. Numerical results demonstrate the proposed schemes’ better performance than traditional approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a world where virtual and physical spaces blend seamlessly together. This concept, called UAV metaverses, allows us to interact with drones in new and exciting ways. To make this happen, digital twins of drones are created and connected to ground stations. However, these connections can be disrupted when drones move or communication signals fade away. To solve this problem, a tiny machine learning framework is developed to optimize how information is shared between drones and the ground. This ensures that we can continue to explore and interact with virtual environments smoothly. |
Keywords
» Artificial intelligence » Machine learning » Pruning » Reinforcement learning
