Routing Based on Deep Reinforcement Learning in Optical Transport Networks

This paper addresses the use of Deep Reinforcement Learning for automatic routing in Optical Transport Networks at the electrical-layer level. We propose a DRL-based solution that achieves both high performance and fast learning.

[1]  Roberto Proietti,et al.  Deep-RMSA: A Deep-Reinforcement-Learning Routing, Modulation and Spectrum Assignment Agent for Elastic Optical Networks , 2018, 2018 Optical Fiber Communications Conference and Exposition (OFC).

[2]  Shane Legg,et al.  Human-level control through deep reinforcement learning , 2015, Nature.

[3]  Liang Guo,et al.  The war between mice and elephants , 2001, Proceedings Ninth International Conference on Network Protocols. ICNP 2001.

[4]  Christophe Diot,et al.  Traffic matrix estimation: existing techniques and new directions , 2002, SIGCOMM 2002.

[5]  Sergey Levine,et al.  Trust Region Policy Optimization , 2015, ICML.

[6]  Jawad A. Salehi,et al.  A Quality-of-Transmission Aware Dynamic Routing and Spectrum Assignment Scheme for Future Elastic Optical Networks , 2013, Journal of Lightwave Technology.