Heterogeneous Mean-Field Multi-Agent Reinforcement Learning for Communication Routing Selection in SAGI-Net
Published in 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall), 2022
Recommended citation: Hengxi Zhang, Huaze Tang, Yuanquan Hu, Xiaoli Wei, Chenye Wu, Wenbo Ding, and Xiao-Ping Zhang. "Heterogeneous Mean-Field Multi-Agent Reinforcement Learning for Communication Routing Selection in SAGI-Net." In 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall), pp. 1-5. IEEE, 2022. https://ieeexplore.ieee.org/abstract/document/10012942
In this work, we represent a novel communication routing selection model for the SAGI-Net system and established a heterogeneous multi-agent reinforcement learning (HMF-MARL) framework to optimize the communication energy efficiency of this system, where the mean-field theory was introduced to enhance the ability of classic MARL method while still maintaining a relatively low computational complexity.
Hengxi Zhang, Huaze Tang, Yuanquan Hu, Xiaoli Wei, Chenye Wu, Wenbo Ding, and Xiao-Ping Zhang. “Heterogeneous Mean-Field Multi-Agent Reinforcement Learning for Communication Routing Selection in SAGI-Net.” In 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall), pp. 1-5. IEEE, 2022.