A Survey on Multi-Agent Reinforcement Learning Methods for Vehicular Networks.
Journal
15th International Wireless Communications & Mobile Computing Conference, IWCMC 2019, Tangier, Morocco, June 24-28, 2019
Pages
1154-1159
Date Issued
2019
Author(s)
Abstract
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. © 2019 IEEE.
Subjects
5G; Caching; Data Offloading; Multi-agent; Reinforcement Learning; URLLC; Vehicular Network
SDGs
Other Subjects
Decision making; Fertilizers; Internet of things; Machine learning; Mobile agents; Mobile computing; Multi agent systems; Surveys; Vehicles; Caching; Data Offloading; Multi agent; URLLC; Vehicular networks; Reinforcement learning
Type
conference paper