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.
Cooperative multi-agent deep reinforce- ment learning for computation offloading in digital twin satellite edge networks,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.