HiRL applies hierarchical RL to coordinate power and task decisions in heterogeneous edge environments, delivering 28% lower latency than Single-DDQN and up to 51% energy savings under low load while maintaining near-100% task completion.
Relto: A reliability- oriented drl approach with context-aware adaptive reward weighting for multi-objective task offloading in mec
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HiRL: Hierarchical Reinforcement Learning for Coordinated Resource Management in Heterogeneous Edge Computing
HiRL applies hierarchical RL to coordinate power and task decisions in heterogeneous edge environments, delivering 28% lower latency than Single-DDQN and up to 51% energy savings under low load while maintaining near-100% task completion.