HRL-VNEAP applies hierarchical RL to dynamic VNE with alternative topologies and reports up to 20.7% higher acceptance ratio than strong baselines on realistic networks.
Joint admission control and resource allocation of virtual network embedding via hierarchical deep reinforcement learning,
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Hierarchical Reinforcement Learning for the Dynamic VNE with Alternatives Problem
HRL-VNEAP applies hierarchical RL to dynamic VNE with alternative topologies and reports up to 20.7% higher acceptance ratio than strong baselines on realistic networks.