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29 August 2022 Model-free reinforcement learning with a non-linear reconstructor for closed-loop adaptive optics control with a pyramid wavefront sensor
B. Pou, J. Smith, E. Quinones, M. Martin, D. Gratadour
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Proceedings Volume 12185, Adaptive Optics Systems VIII; 121852U (2022) https://doi.org/10.1117/12.2627849
Event: SPIE Astronomical Telescopes + Instrumentation, 2022, Montréal, Québec, Canada
Abstract
We present a model-free reinforcement learning (RL) predictive model with a supervised learning non-linear reconstructor for adaptive optics (AO) control with a pyramid wavefront sensor (P-WFS). First, we analyse the additional problems of training an RL control method with a P-WFS compared to the Shack-Hartmann WFS. From those observations, we propose our solution: a combination of model-free RL for prediction with a non-linear reconstructor based on neural networks with a U-net architecture. We test the proposed method in simulation of closed-loop AO for an 8m telescope equipped with a 32x32 P-WFS and observe that both the predictive and non-linear reconstruction add additional benefits over an optimised integrator.
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B. Pou, J. Smith, E. Quinones, M. Martin, and D. Gratadour "Model-free reinforcement learning with a non-linear reconstructor for closed-loop adaptive optics control with a pyramid wavefront sensor", Proc. SPIE 12185, Adaptive Optics Systems VIII, 121852U (29 August 2022); https://doi.org/10.1117/12.2627849
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KEYWORDS
Adaptive optics
Wavefront sensors
Neural networks
Telescopes
Machine learning
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