AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
We have 2 of 14 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
A transport-network-aware dynamic congestion representation that serves as costs in a risk-constrained route-scheduling rule that minimizes predicted delivery time subject to a bound on extreme-congestion probability, embedding the learned predictors within a lightweight operations-research decision model.
This work proposes Neural Double Q-routing, which replaces destination-indexed tables with a shared state--action value network, and achieves the lowest mean completion time among all compared methods in the six 150- and 200-OHT settings, whereas Dijkstra remains best in the three 100-OHT settings.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.