On Design and Implementation of Reinforcement Learning Based Cognitive Routing for Autonomous Networks
- 3 October 2022
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Communications Letters
- Vol. 27 (1) , 205-209
Abstract
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.Keywords
Funding Information
- BUPT Excellent Ph.D. Students Foundation (CX2021112)
- Ministry of Education (MoE)-China Mobile Communications Group Co.,Ltd. (CMCC) Artificial Intelligence Project (MCM20190701)
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