Hierarchical Control Multi-Agent DRL for Vehicle Twin Migration With Workload Prediction in UAV-Assisted Vehicular Metaverses

Vehicular metaverses enable immersive digital experiences through seamless Vehicle Twin (VT) services. As vehicles move, VT service instances must migrate between RoadSide Units (RSUs) to sustain low-latency interactions. However, RSUs face significant challenges from dynamic workload fluctuations and uneven geographical distribution. These limitations often result in service degradation during peak demand periods. Unmanned Aerial Vehicles (UAVs) offer promising solutions to augment fixed infrastructure capacity. Nevertheless, their energy constraints and trajectory optimization create additional complexity for resource management. To address these challenges, we develop a novel framework integrating workload forecasting with coordinated decision-making for VT migration and UAV routing. We first design a long short-term memory-based workload prediction model. This model predicts workload patterns by combining spatial feature extraction with temporal dependency modeling. We enhance the prediction capability through noise-augmented training to improve robustness. Then, we formulate the VT migration and UAV routing optimization as a markov decision process, which captures the sequential nature of decision-making. Finally, we propose a hierarchical control multi-agent deep reinforcement learning algorithm where the upper-layer controller uses multi-agent proximal policy optimization for collaborative decision-making, and the lower-layer controller handles VT migration and UAV routing execution. Simulation results show that the proposed approach reduces average latency by 25.70% and validation loss by 63.70% for workload prediction compared to baseline methods.