Performance study of randomly coupled erbium-doped fiber amplifier using machine learning

Konda, Ramachandra Reddy; Velayudham, Narmadha Thirunavvukkarasu; Venkatesan, Ramesh

doi:10.1117/1.oe.60.7.076101

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2023

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“…Various successful cases, especially in robot controlling, intelligence driving and games, bring it to a wider world to address complex control issues. The combination of RL and optics, ranging from adaptive optics, 1, 2 structural color, 3 to optical design, [4][5][6] has also attracted the researchers' attention. Notably, in these cases, the RL agent usually interacts with the optical system directly.…”

Section: Introductionmentioning

confidence: 99%

A model-based reinforcement learning algorithm faced with the laser frequency sweep linearization for FMCW LiDAR

Zhao

Yuan

Wang

2023

AOPC 2022: Optical Sensing, Imaging, and Display Technology

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Artificial intelligence (AI) has been widely used in various fields of physics and engineering. In this work, we introduce the model-based reinforcement learning (MBRL), which is an important branch of machine learning, to the laser frequency sweep control for frequency modualted continous wave (FMCW) light detection and range (LiDAR). With the well designed neural network structure, the experimental results with control represent a significant improvement in the linearity of laser frequency sweep indicating that our proposed MBRL control method has the potential for complicated optical systems control.

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