Quantum-Inspired Multi Agent Reinforcement Learning for Exploration Exploitation Optimization in UAV-Assisted 6G Network Deployment

• URL: http://arxiv.org/abs/2512.20624v1
• Date: Tue, 25 Nov 2025 04:35:43 GMT
• Title: Quantum-Inspired Multi Agent Reinforcement Learning for Exploration Exploitation Optimization in UAV-Assisted 6G Network Deployment
• Authors: Mazyar Taghavi, Javad Vahidi,
• Abstract summary: This study introduces a quantum inspired framework for optimizing the exploration exploitation tradeoff in multiagent learning, applied to UAVassisted 6G network deployment.<n>We consider a cooperative scenario where ten intelligent UAVs coordinate autonomously to maximize signal coverage and support efficient network expansion under partial observability and dynamic conditions.
• Score: 0.5729426778193399
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study introduces a quantum inspired framework for optimizing the exploration exploitation tradeoff in multiagent reinforcement learning, applied to UAVassisted 6G network deployment. We consider a cooperative scenario where ten intelligent UAVs autonomously coordinate to maximize signal coverage and support efficient network expansion under partial observability and dynamic conditions. The proposed approach integrates classical MARL algorithms with quantum-inspired optimization techniques, leveraging variational quantum circuits VQCs as the core structure and employing the Quantum Approximate Optimization Algorithm QAOA as a representative VQC based method for combinatorial optimization. Complementary probabilistic modeling is incorporated through Bayesian inference, Gaussian processes, and variational inference to capture latent environmental dynamics. A centralized training with decentralized execution CTDE paradigm is adopted, where shared memory and local view grids enhance local observability among agents. Comprehensive experiments including scalability tests, sensitivity analysis, and comparisons with PPO and DDPG baselines demonstrate that the proposed framework improves sample efficiency, accelerates convergence, and enhances coverage performance while maintaining robustness. Radar chart and convergence analyses further show that QI MARL achieves a superior balance between exploration and exploitation compared to classical methods. All implementation code and supplementary materials are publicly available on GitHub to ensure reproducibility.

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