联合计算卸载和资源分配,以通过多代理强化学习在车辆互联网中的终端协作
Cong Wang1, Yaoming Wang2, Ying Yuan2
1School of Computer and Communication Engineering, Northeastern University at Qinhuangdao, Qinhuangdao, 066004, Hebei, China; Hebei Key Laboratory of Marine Perception Network and Data Processing, Qinhuangdao, 066004, Hebei, China.
概括
本研究介绍了用于车辆边缘计算 (VEC) 的新去中心化解决方案,以有效地管理实时任务卸载和资源分配. 拟议的方法优化了资源使用,并减少了智能运输系统中的延迟.
科学领域:
- 智能运输系统 智能运输系统
- 车辆边缘计算 (VEC) 技术
- 分布式人工智能 分布式人工智能
背景情况:
- 车辆边缘计算 (VEC) 为智能运输系统提供了低延迟,但面临着资源限制和严格的延迟需求的挑战.
- 当前的VEC解决方案经常通过静态分配资源而浪费资源,而不考虑异质任务要求.
研究的目的:
- 解决VEC系统中的实时任务卸载和异质资源分配问题.
- 开发一个去中心化的解决方案,优化资源利用,尽量减少延迟.
主要方法:
- 提出了一个名为AR-MAD4PG的去中心化解决方案,利用注意力机制和反复神经网络 (RNN) 具有多代理深确定性政策梯度.
- 实现了共享代理图形,并定期进行通信,以便在边缘节点之间聚合信息.
- 设计了一个基于RNN的特征提取网络和一个多头注意力机制,以处理部分可观测性和大型状态动作空间.
主要成果:
- 拟议的AR-MAD4PG方法在模拟中显示出与现有方法相比更高的性能.
- 有效管理实时任务卸载和异质资源分配.
- 在VEC系统中提高效率和减少延迟.
结论:
- AR-MAD4PG方法提供了一个有效的去中心化解决方案,用于优化VEC中的任务卸载和资源分配.
- 该方法成功地解决了部分可观测性,异质任务和资源浪费的挑战.
- 这项工作通过高效的VEC资源管理,有助于智能交通系统的发展.
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