Optimizing Vehicular Networks with Variational Quantum Circuits-based Reinforcement Learning

• URL: http://arxiv.org/abs/2405.18984v1
• Date: Wed, 29 May 2024 10:57:25 GMT
• Title: Optimizing Vehicular Networks with Variational Quantum Circuits-based Reinforcement Learning
• Authors: Zijiang Yan, Ramsundar Tanikella, Hina Tabassum,
• Abstract summary: 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)
• Score: 10.964841612918539
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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