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Optical RL-Gym

List of publications related to this tool:

  1. C. Natalino and P. Monti, The Optical RL-Gym: an open-source toolkit for applying reinforcement learning in optical networks, ICTON, July, 2020: introduces the toolkit and presents a two use cases.
@inproceedings{optical-rl-gym,
  title = {The {Optical RL-Gym}: an open-source toolkit for applying reinforcement learning in optical networks},
  author = {Carlos Natalino and Paolo Monti},
  booktitle = {International Conference on Transparent Optical Networks (ICTON)},
  year = {2020},
  location = {Bari, Italy},
  month = {July},
  pages = {Mo.C1.1},
  doi={10.1109/ICTON51198.2020.9203239},
  url = {https://github.com/carlosnatalino/optical-rl-gym}
}

List of publications using this tool:

You can see an updated list of works citing this tool in Google Scholar.

  • Núñez Kasaneva, J. I. (2022). Aplicación de Reinforcement Learning para los problemas de Survivable-Routing, Modulation Level and Spectrum Assigment.
  • Etezadi, E., Natalino, C., Diaz, R., Lindgren, A., Melin, S., Wosinska, L., ... & Furdek, M. (2022). DeepDefrag: a deep reinforcement learning framework for spectrum defragmentation.
  • Woolfries, C., Van Laethem, M., Crooks, N., Iyer, T., & Aibin, M. (2021, December). Slot Based Round Robin Algorithm for RMCSA Problem in a Spatially-Spectrally FONs. In 2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) (pp. 0848-0850). IEEE.
  • Di Cicco, N., Mercan, E. F., Karandin, O., Ayoub, O., Troia, S., Musumeci, F., & Tornatore, M. (2022). On Deep Reinforcement Learning for Static Routing and Wavelength Assignment. IEEE Journal of Selected Topics in Quantum Electronics, 28(4), 1-12.
  • Aibin, M., Cheng, S., Xiao, D., & Huang, A. (2020, October). Optimization of Regenerator Placement in Optical Networks Using Deep Tensor Neural Network. In 2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) (pp. 0218-0225). IEEE.
  • Patri, S. K., Autenrieth, D., Elbers, D., & Mas, C. Multi-Band Transparent Optical Network Planning Strategies for 6g-Ready European Networks. Ing Jörg-Peter and Mas, Carmen, Multi-Band Transparent Optical Network Planning Strategies for 6g-Ready European Networks.
  • Pinto-Ríos, J., Calderón, F., Leiva, A., Hermosilla, G., Beghelli, A., Bórquez-Paredes, D., ... & Saavedra, G. (2022). Resource Allocation in Multicore Elastic Optical Networks: A Deep Reinforcement Learning Approach. arXiv preprint arXiv:2207.02074.
  • Morales, P., Franco, P., Lozada, A., Jara, N., Calderón, F., Pinto-Ríos, J., & Leiva, A. (2021, June). Multi-band Environments for Optical Reinforcement Learning Gym for Resource Allocation in Elastic Optical Networks. In 2021 International Conference on Optical Network Design and Modeling (ONDM) (pp. 1-6). IEEE.
  • El Sheikh, N. E. D., Paz, E., Pinto, J., & Beghelli, A. (2021, June). Multi-band provisioning in dynamic elastic optical networks: a comparative study of a heuristic and a deep reinforcement learning approach. In 2021 International Conference on Optical Network Design and Modeling (ONDM) (pp. 1-3). IEEE.