在基于混合层次深度强化学习的车辆边缘计算网络中,针对ICV的任务卸载和资源配置
Jiahui Liu1,2,3, Yuan Zou1,2,3, Guodong Du1,2
1School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China.
Sensors (Basel, Switzerland)
|November 27, 2025
概括
智能互联汽车可以通过车辆边缘计算网络卸载任务. 混合层次深度强化学习算法显著降低了这些车辆的计算成本.
科学领域:
- 智能运输系统 智能运输系统
- 边缘计算 边缘计算
- 人工智能的人工智能
背景情况:
- 智能互联汽车 (ICV) 面临着车载计算限制.
- 车辆边缘计算网络 (VECNs) 能够将任务卸载到移动边缘计算 (MEC),以减少计算负载.
- 动态的运输环境需要有效的任务卸载和资源分配.
研究的目的:
- 为车辆-道路协作边缘计算网络开发一个高效的任务卸载和资源分配策略.
- 为了最大限度地减少与汽车计算相关的时间和能源成本.
- 解决动态VECN中混合离散和连续决策变量的复杂性.
主要方法:
- 制定了任务卸载调度和资源分配问题.
- 提出了一种两层混合层次层次的深度强化学习 (HHDRL) 算法.
- 上层:增强的双深Q网络 (DDQN) 具有对离散行动 (通信决策) 的自我注意.
- 下层:用于持续行动的深度决定性政策梯度 (DDPG) (权力控制,资源分配).
主要成果:
- 与基准算法相比,HHDRL显示了总计算成本的显著降低.
- 该算法在动态和变化的环境条件下显示出强度和适应性.
- 混合方法有效地分解了复杂的行动空间,以提高性能.
结论:
- 在VECN中,HHDRL为任务卸载和资源分配提供了有效的解决方案.
- 拟议的算法提高了计算效率,并降低了ICV的成本.
- 该研究强调了人工智能驱动的边缘计算在智能交通系统中的潜力.
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