A Deep-Reinforcement Learning Approach for Software-Defined Networking Routing Optimization
This addresses routing optimization in software-defined networking, but appears incremental as it applies an existing method to a specific domain.
The paper tackled network routing optimization by designing a Deep-Reinforcement Learning agent that adapts to traffic conditions to minimize delay, showing promising performance and operational advantages over traditional methods.
In this paper we design and evaluate a Deep-Reinforcement Learning agent that optimizes routing. Our agent adapts automatically to current traffic conditions and proposes tailored configurations that attempt to minimize the network delay. Experiments show very promising performance. Moreover, this approach provides important operational advantages with respect to traditional optimization algorithms.