Optimizing Vehicular Networks with Variational Quantum Circuits-based Reinforcement Learning
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910462475501568 |
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| author | Yan, Zijiang Tanikella, Ramsundar Tabassum, Hina |
| author_facet | Yan, Zijiang Tanikella, Ramsundar Tabassum, Hina |
| contents | In vehicular networks (VNets), ensuring both road safety and dependable network connectivity is of utmost importance. Achieving this necessitates the creation of resilient and efficient decision-making policies that prioritize multiple objectives. In this paper, we develop a Variational Quantum Circuit (VQC)-based multi-objective reinforcement learning (MORL) framework to characterize efficient network selection and autonomous driving policies in a vehicular network (VNet). Numerical results showcase notable enhancements in both convergence rates and rewards when compared to conventional deep-Q networks (DQNs), validating the efficacy of the VQC-MORL solution. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_18984 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Optimizing Vehicular Networks with Variational Quantum Circuits-based Reinforcement Learning Yan, Zijiang Tanikella, Ramsundar Tabassum, Hina Machine Learning Artificial Intelligence Networking and Internet Architecture In vehicular networks (VNets), ensuring both road safety and dependable network connectivity is of utmost importance. Achieving this necessitates the creation of resilient and efficient decision-making policies that prioritize multiple objectives. In this paper, we develop a Variational Quantum Circuit (VQC)-based multi-objective reinforcement learning (MORL) framework to characterize efficient network selection and autonomous driving policies in a vehicular network (VNet). Numerical results showcase notable enhancements in both convergence rates and rewards when compared to conventional deep-Q networks (DQNs), validating the efficacy of the VQC-MORL solution. |
| title | Optimizing Vehicular Networks with Variational Quantum Circuits-based Reinforcement Learning |
| topic | Machine Learning Artificial Intelligence Networking and Internet Architecture |
| url | https://arxiv.org/abs/2405.18984 |