在无人机辅助边缘计算中使用LLM增强的多代理增强学习进行任务卸载
Feifan Zhu1, Fei Huang2, Yantao Yu1
1School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China.
Sensors (Basel, Switzerland)
|January 11, 2025
概括
本研究引入了一种新的多代理深度学习框架,用于优化边缘计算中的无人机 (UAV) 轨迹. 这种新的方法提高了任务完成率,并加快了无人机集群的趋同.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 带有计算服务器的无人机 (UAV) 增强了远程用户设备 (UE) 的边缘计算.
- 现有的价值分解算法在多无人机协调方面遇到了困难,导致任务完成率降低,融合时间延长.
- 有效的无人机轨迹规划对于高效的边缘计算资源利用至关重要.
研究的目的:
- 开发一个创新的多代理深度学习框架,以优化多无人机轨迹.
- 解决当前算法的局限性,即在无人机集群中将本地观测与全球状态联系起来.
- 提高无人机辅助边缘计算中的任务完成率和融合时间.
主要方法:
- 概念化多无人机轨迹优化作为一个分散的部分可观测的马尔科夫决策过程 (Dec-POMDP).
- 将QTRAN算法与区域分解的大型语言模型 (LLM) 集成.
- 采用图形卷积网络 (GCN) 和自我注意机制来管理跨次区域关系.
主要成果:
- 拟议的框架显著优于现有的深度强化学习方法.
- 在趋同速度上有明显的改善,超过10%.
- 与基线方法相比,实现了超过10%的任务完成率改善.
结论:
- 开发的框架在边缘计算环境中推进无人机轨迹优化.
- 在无人机辅助边缘计算中增强多代理系统的性能.
- 提供了一个强大的解决方案,用于复杂的计算任务卸载使用无人机群.
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