SINET: Enabling Scalable Network Routing with Deep Reinforcement Learning on Partial Nodes

In this paper, we propose SINET, a scalable and intelligent network control framework for routing optimization. SINET uses the idea of partial control to collect network information from critical nodes and uses Deep Reinforcement Learning (DRL) to dynamically optimizes routing policies based on the collected network information. Simulation results show that SINET can reduce the average flow completion time and exhibit better robustness against minor topology changes, compared to existing DRL-based schemes.