Multiagent Deep-Reinforcement-Learning-Based Resource Allocation for Heterogeneous QoS Guarantees for Vehicular Networks

Vehicle-to-vehicle communications can offer direct information interaction, including security-centered information and entertainment information. However, the rapid proliferation of vehicles and the diversity of communications services demand for a more intelligent and efficient resource allocation...

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Vydané v:	IEEE internet of things journal Ročník 9; číslo 3; s. 1683 - 1695
Hlavní autori:	Tian, Jie, Liu, Qianqian, Zhang, Haixia, Wu, Dalei
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.02.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Copper Deep learning Deep reinforcement learning (DRL) Entertainment Entertainment industry heterogeneous applications Machine learning multi-agent deep deterministic policy gradient (MADDPG) Multiagent systems Network latency Optimization Power control Quality of service Quality of service architectures Reinforcement learning Resource allocation Resource management Training Vehicles
ISSN:	2327-4662, 2327-4662
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