A Transformer-empowered actor-critic RL framework with ε-LoPe exploration and asymptotic return normalization improves long-term service acceptance, resource utilization, and scalability for sequence-aware SFC partitioning in heterogeneous networks.
A reinforcement learning-based approach for availability-aware service function chain placement in large-scale networks,
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Transformer-Empowered Actor-Critic Reinforcement Learning for Sequence-Aware Service Function Chain Partitioning
A Transformer-empowered actor-critic RL framework with ε-LoPe exploration and asymptotic return normalization improves long-term service acceptance, resource utilization, and scalability for sequence-aware SFC partitioning in heterogeneous networks.