| Autor: |
Alexey Kokhanovskiy, Alexey Shevelev, Kirill Serebrennikov, Evgeny Kuprikov, Sergey Turitsyn |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Photonics, Vol 9, Iss 12, p 921 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2304-6732 |
| DOI: |
10.3390/photonics9120921 |
| Popis: |
We experimentally demonstrate the application of a double deep Q-learning network algorithm (DDQN) for design of a self-starting fiber mode-locked laser. In contrast to the static optimization of a system design, the DDQN reinforcement algorithm is capable of learning the strategy of dynamic adjustment of the cavity parameters. Here, we apply the DDQN algorithm for stable soliton generation in a fiber laser cavity exploiting a nonlinear polarization evolution mechanism. The algorithm learns the hysteresis phenomena that manifest themselves as different pumping-power thresholds for mode-locked regimes for diverse trajectories of adjusting optical pumping. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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