A Survey on Multi-Agent Reinforcement Learning Methods for Vehicular Networks

被引:40
作者
Althamary, Ibrahim [1 ]
Huang, Chih-Wei [1 ]
Lin, Phone [2 ]
机构
[1] Natl Cent Univ, Dept Commun Engn, Taoyuan, Taiwan
[2] Natl Taiwan Univ, Dept Comp Sci & Informat Engn, Taipei, Taiwan
来源
2019 15TH INTERNATIONAL WIRELESS COMMUNICATIONS & MOBILE COMPUTING CONFERENCE (IWCMC) | 2019年
关键词
Multi-agent; Reinforcement Learning; Vehicular Network; URLLC; Caching; Data Offloading; 5G;
D O I
10.1109/iwcmc.2019.8766739
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Under the rapid development of the Internet of Things (IoT), vehicles can be recognized as mobile smart agents that communicating, cooperating, and competing for resources and information. The task between vehicles is to learn and make decisions depending on the policy to improve the effectiveness of the multi-agent system (MAS) that deals with the continually changing environment. The multi-agent reinforcement learning (MARL) is considered as one of the learning frameworks for finding reliable solutions in a highly dynamic vehicular MAS. In this paper, we provide a survey on research issues related to vehicular networks such as resource allocation, data offloading, cache placement, ultra-reliable low latency communication (URLLC), and high mobility. Furthermore, we show the potential applications of MARL that enables decentralized and scalable decision making in vehicle-to-everything (V2X) scenarios.
引用
收藏
页码:1154 / 1159
页数:6
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