May 20, 2022 Presentation Open Access

Pou, Bartomeu; Smith, Jeffrey; Quinones, Eduardo; Martin, Mario; Gratadour, Damien

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    <subfield code="u">LESIA , Observatoire de Paris, Université PSL, CNRS, Sorbonne Université, Université de Paris, Australian National University</subfield>
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    <subfield code="a">A Non Linear Control Method with Reinforcement Learning for Adaptive Optics with Pyramid Sensors</subfield>
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    <subfield code="a">A Model-driven development framework for highly Parallel and EneRgy-Efficient computation supporting multi-criteria optimisation</subfield>
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    <subfield code="a">&lt;p&gt;Extreme Adaptive Optics (AO) systems are designed to provide high resolution and high contrast observing capabilities on the largest ground-based telescopes through exquisite phase reconstruction accuracy. In that context, the pyramid wavefront sensor (P-WFS) has shown promise to deliver the means to provide such accuracy due to its high sensitivity. However, traditional methods cannot leverage the highly non-linear P-WFS measurements to their full potential. We present a predictive control method based on Reinforcement Learning (RL) for AO control with a P-WFS. The proposed approach is data-driven, has no assumptions about the system&amp;#39;s evolution, and is non-linear due to the usage of neural networks. First, we discuss the challenges of using an RL control method with a P-WFS and propose solutions. Then, we show that our method outperforms an optimized integrator controller. Finally, we discuss its possible path for an actual implementation.&lt;/p&gt;</subfield>
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