Rescale-Invariant Federated Reinforcement Learning for Resource Allocation in V2X Networks
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866929334748446720 |
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| author | Xu, Kaidi Zhou, Shenglong Li, Geoffrey Ye |
| author_facet | Xu, Kaidi Zhou, Shenglong Li, Geoffrey Ye |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_01961 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Rescale-Invariant Federated Reinforcement Learning for Resource Allocation in V2X Networks Xu, Kaidi Zhou, Shenglong Li, Geoffrey Ye Signal Processing 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. |
| title | Rescale-Invariant Federated Reinforcement Learning for Resource Allocation in V2X Networks |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2405.01961 |