一个基于学习的多代理深度强化学习在车载网络中的流行内容分发计划
Wenwei Chen1, Xiujie Huang1,2, Quanlong Guan1,2
1College of Information Science and Technology, Jinan University, Guangzhou 510632, China.
Entropy (Basel, Switzerland)
|May 27, 2023
概括
本研究介绍了一种多代理深度强化学习方案,用于在汽车互联网中有效地分发流行的内容. 拟议的方法提高了内容交付速度,并减少了车辆网络中的传输延迟.
科学领域:
- 车辆网络 车辆网络
- 人工智能的人工智能
- 通信系统 通信系统
背景情况:
- 汽车互联网 (IoV) 在数据服务方面依赖于车辆对一切 (V2X).
- 在 IoV 中,受欢迎内容分发 (PCD) 面临着由于车辆移动性和路边单位 (RSU) 的限制而面临的挑战.
- 车辆对车辆 (V2V) 通信协作对于高效的内容交付至关重要.
研究的目的:
- 提出一种基于多代理深度强化学习 (MADRL) 的新方案,用于在车辆网络中进行流行的内容分发 (PCD).
- 为了提高效率和减少传输延迟,向车辆提供流行的内容.
- 为应对 IoV. 车辆移动性和 RSU 覆盖范围限制所带来的挑战.
主要方法:
- 开发了一个基于MADRL的方案,每个车辆都充当MADRL代理.
- 车辆使用光谱聚类进行聚类,以管理V2V通信期间的复杂性.
- 多代理近接策略优化 (MAPPO) 算法训练代理,结合自我注意力和无效动作掩盖.
主要成果:
- 与现有方法相比,拟议的MADRL-PCD方案显著提高了PCD效率.
- 该方案可显著减少流行的内容的传输延迟.
- 实验结果验证了MADRL-PCD方法对联盟游戏和贪策略的有效性.
结论:
- MADRL-PCD方案为车辆网络中流行的内容分发提供了卓越的解决方案.
- 频谱聚类,自我注意和MAPPO的整合有效地优化了内容交付.
- 这项研究有助于在车辆互联网中更高效,更快速地传播数据.
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