Rescale-Invariant Federated Reinforcement Learning for Resource Allocation in V2X Networks

Federated Reinforcement Learning (FRL) offers a promising solution to various practical challenges in resource allocation for vehicle-to-everything (V2X) networks. However, the data discrepancy among individual agents can significantly degrade the performance of FRL-based algorithms. To address this...

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Veröffentlicht in:	IEEE communications letters Jg. 28; H. 12; S. 2799 - 2803
Hauptverfasser:	Xu, Kaidi, Zhou, Shenglong, Ye Li, Geoffrey
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.12.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Distributed databases FRL Invariants Machine learning Neural networks Observability Performance evaluation policy gradient Receivers rescale-invariant operation Resource allocation Resource management Training Trajectory Transmitters V2X communications Vehicle-to-everything
ISSN:	1089-7798, 1558-2558
Online-Zugang:	Volltext
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