OnDemand Phase Control of a 7Fiber Amplifiers Array with Neural Network and QuasiReinforcement Learning
Abstract
:1. Introduction
2. Neural Network in a Phase Reduction Loop with QuasiReinforcement Learning
3. Target Adaptive NN with QRL Process
Algorithm 1: Quasireinforcement learning algorithm for TANN 
Input: Measurement model: ${\u2102}^{n}\to {\mathbb{R}}_{+}^{m}$, reward function $R:{\u2102}^{n}\times {\u2102}^{n}\to \left[0,1\right]$ Output:Trained target adaptive neural network TANN:${\u2102}^{mn\times n}\times {\u2102}^{n}\to {\u2102}^{n\times m}$

4. Simulations
5. Experiments
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Shpakovych, M.; Maulion, G.; Boju, A.; Armand, P.; Barthélémy, A.; DesfargesBerthelemot, A.; Kermene, V. OnDemand Phase Control of a 7Fiber Amplifiers Array with Neural Network and QuasiReinforcement Learning. Photonics 2022, 9, 243. https://doi.org/10.3390/photonics9040243
Shpakovych M, Maulion G, Boju A, Armand P, Barthélémy A, DesfargesBerthelemot A, Kermene V. OnDemand Phase Control of a 7Fiber Amplifiers Array with Neural Network and QuasiReinforcement Learning. Photonics. 2022; 9(4):243. https://doi.org/10.3390/photonics9040243
Chicago/Turabian StyleShpakovych, Maksym, Geoffrey Maulion, Alexandre Boju, Paul Armand, Alain Barthélémy, Agnès DesfargesBerthelemot, and Vincent Kermene. 2022. "OnDemand Phase Control of a 7Fiber Amplifiers Array with Neural Network and QuasiReinforcement Learning" Photonics 9, no. 4: 243. https://doi.org/10.3390/photonics9040243