HDRL-MoE is a hierarchical DRL framework with MoE that decouples slow inference decisions from fast UAV trajectory control in a constrained POMDP to maximize inference accuracy under mission constraints.
Optimal AI model splitting and resource allocation for device-edge co-inference in multi-user wireless sensing systems,
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UAV-Assisted Cooperative Edge Inference for Low-Altitude Economy via MoE-based Hierarchical Deep Reinforcement Learning
HDRL-MoE is a hierarchical DRL framework with MoE that decouples slow inference decisions from fast UAV trajectory control in a constrained POMDP to maximize inference accuracy under mission constraints.