Multi-Agent DRL-Based Adaptive Resource Allocation and Twin Migration in Multi-Tier Vehicular Metaverse
Abishu, Hayla Nahom Seid, Abegaz Mohammed Al-Fuqaha, Ala Erbad, Aiman Guizani, Mohsen
Abishu, Hayla Nahom
Seid, Abegaz Mohammed
Al-Fuqaha, Ala
Erbad, Aiman
Guizani, Mohsen
Supervisor
Department
Machine Learning
Embargo End Date
Type
Conference proceeding
Date
2025
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Language
English
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Abstract
In the dynamic vehicular metaverse, delivering a seamless user experience (UX) and effective human-machine interaction (HMI) is challenging due to vehicle mobility and varying resource needs. This paper introduces an adaptive resource allocation and twin migration framework using Multi-Agent Deep Reinforcement Learning (MADRL) for a multi-tier vehicular metaverse. The framework enables cooperative agents to dynamically allocate resources and migrate vehicle twins across vehicle, edge, and cloud layers, ensuring seamless UX and efficient HMI. The joint resource allocation and twin migration optimization problem is modeled as MDP and a hierarchical multi-agent deep deterministic policy gradient-with QMIX (MADDPG-Q) strategy is adopted to solve it, reducing latency and optimizing resource use. Moreover, the proposed framework is designed to be context-aware, adjusting HMI based on real-time conditions, and enhancing interaction quality. Simulation results show significant improvements in UX, latency reduction, and resource efficiency.
Citation
H. N. Abishu, A. M. Seid, A. Al-Fuqaha, A. Erbad and M. Guizani, "Multi-Agent DRL-Based Adaptive Resource Allocation and Twin Migration in Multi-Tier Vehicular Metaverse," ICC 2025 - IEEE International Conference on Communications, Montreal, QC, Canada, 2025, pp. 5676-5681, doi: 10.1109/ICC52391.2025.11161703.
Source
Proceedings of the ICC 2025-IEEE International Conference on Communications
Conference
ICC 2025-IEEE International Conference on Communications
Keywords
Metaverse, Human-machine interaction, Resource allocation, Twin migration, Stochastic game
Subjects
Source
ICC 2025-IEEE International Conference on Communications
Publisher
IEEE