A heterogeneous-agent RL algorithm (SU-HATD3) is proposed to jointly optimize diffusion-model inference offloading, inference parameters, and UAV trajectories in order to maximize a fidelity-delay utility for GAI-empowered intelligent transportation digital twins.
Harnessing digital twin technol- ogy for adaptive traffic signal control: Improving signalized intersection performance and user satisfaction,
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Joint Task Offloading, Inference Optimization and UAV Trajectory Planning for Generative AI Empowered Intelligent Transportation Digital Twin
A heterogeneous-agent RL algorithm (SU-HATD3) is proposed to jointly optimize diffusion-model inference offloading, inference parameters, and UAV trajectories in order to maximize a fidelity-delay utility for GAI-empowered intelligent transportation digital twins.