On Design and Implementation of Reinforcement Learning Based Cognitive Routing for Autonomous Networks

Cognitive routing is expected to be a key technology to realize intellectualized operation and administration for autonomous networks. Meanwhile, reinforcement learning (RL) excels especially in decision-making tasks by learning from trial and error, which is promising for solving various cognitive routing problems. Nevertheless, the research of RL-based cognitive routing has been severely constrained by a lack of general problem formulation and feasible network simulation frameworks. In this letter, we propose the first general problem formulation of RL-based cognitive routing. In addition, based on OpenAI Gym and ns-3, we implement the first packet-level network simulation framework, the RL4Net, that facilitates the rapid prototyping of RL-based cognitive routing algorithms. Finally, we conduct extensive experiments with the RL4Net to evaluate the effectiveness of RL-based cognitive routing. Numerical results demonstrate that our proposed RL-based cognitive routing effectively outperforms RL-based non-cognitive routing and the OSPF in terms of minimizing average end-to-end network delay.