MobiU-MAC deploys a new DRL algorithm, CHILL-STER, that learns optimal ranging-free channel access policies equivalent to standard MDPs despite long delays and node mobility in underwater networks.
Human-level control through deep reinforcement learn- ing
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cs.NI 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
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.
citing papers explorer
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Delay-Robust Deep Reinforcement Learning for Ranging-Free Channel Access under Mobility in Underwater Acoustic Networks
MobiU-MAC deploys a new DRL algorithm, CHILL-STER, that learns optimal ranging-free channel access policies equivalent to standard MDPs despite long delays and node mobility in underwater networks.
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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.