Meta-Hierarchical Reinforcement Learning (MHRL)-Based Dynamic Resource Allocation for Dynamic Vehicular Networks

• Published in: IEEE transactions on vehicular technology Vol. 71; no. 4; pp. 3495 - 3506
• Main Authors: He, Ying, Wang, Yuhang Wang, Lin, Qiuzhen, Li, Jianqiang
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Dynamic scheduling; Dynamic vehicular networks; hierarchical reinforcement learning; Learning; Markov processes; meta-learning; Networks; Reinforcement learning; Resource allocation; Resource management; Task analysis; Training; Vehicle dynamics; Vehicles

With the rapid development of vehicular networks, there is an increasing demand for extensive networking, computting, and caching resources. How to allocate multiple resources effectively and efficiently for dynamic vehicular networks is extremely important. Most existing works on resource managemen...