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

Kaidi Xu; Shenglong Zhou; Geoffrey Ye Li

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arXiv · 2405.01961

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

Kaidi Xu·Shenglong Zhou·Geoffrey Ye Li

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Abstract

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 limitation, we exploit the node-wise invariance property of ReLU-activated neural networks, with the aim of reducing data discrepancy to improve learning performance. Based on this property, we introduce a backward rescale-invariant operation to develop a rescale-invariant FRL algorithm. Simulation results demonstrate that the proposed algorithm notably enhances both convergence speed and convergent performance.

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BibTeX RIS

Kaidi Xu, Shenglong Zhou, Geoffrey Ye Li. 2024-05-03. Rescale-Invariant Federated Reinforcement Learning for Resource Allocation in V2X Networks. https://arxiv.org/abs/2405.01961

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