节能多代理深度强化学习任务卸载和资源分配用于无人机边缘计算
Shu Xu1, Qingjie Liu1, Chengye Gong1
1China Nanhu Academy of Electronic and Information Technology, Jiaxing 314001, China.
Sensors (Basel, Switzerland)
|September 19, 2025
概括
本研究引入了一个新的多代理增强学习框架 (MATD3-TORA) 用于无人机 (UAV) 辅助的移动边缘计算 (MEC). 它优化了任务卸载和资源分配,减少了移动设备的延迟和能源消耗.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 网络化 网络化 网络化
背景情况:
- 移动边缘计算 (MEC) 系统面临着对延迟敏感应用程序的挑战.
- 无人驾驶飞行器 (UAV) 为MEC提供了移动性和灵活的部署.
- 在UAV-MEC中,优化资源分配和任务卸载至关重要.
研究的目的:
- 为无人机辅助MEC提出一个新的多代理强化学习框架.
- 优化任务卸载和资源配置,以减少延迟和能源消耗.
- 解决分布式决策和移动能源权衡方面的挑战.
主要方法:
- 开发了多代理双延迟深度决定性政策梯度的任务卸载和资源分配 (MATD3-TORA) 框架.
- 制定了一个联合优化问题,以尽量减少延迟和能源消耗.
- 实现了多个无人机之间的协作决策,以提供高效的服务.
主要成果:
- MATD3-TORA在系统延迟方面表现出显著的改善.
- 与传统方法相比,该框架实现了提高能源效率.
- 在实时资源分配和移动能源权衡管理中验证了有效性.
结论:
- MATD3-TORA是优化无人机辅助MEC网络的有效解决方案.
- 拟议的框架成功地解决了分布式任务卸载和资源管理的关键挑战.
- 突出了多代理增强学习在提高无人机-MEC性能方面的潜力.
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