May 20, 2022 Presentation Open Access

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

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  <dc:creator>Pou, Bartomeu</dc:creator>
  <dc:creator>Smith, Jeffrey</dc:creator>
  <dc:creator>Quinones, Eduardo</dc:creator>
  <dc:creator>Martin, Mario</dc:creator>
  <dc:creator>Gratadour, Damien</dc:creator>
  <dc:date>2022-05-20</dc:date>
  <dc:description>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'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.</dc:description>
  <dc:identifier>https://zenodo.org/record/6567127</dc:identifier>
  <dc:identifier>10.5281/zenodo.6567127</dc:identifier>
  <dc:identifier>oai:zenodo.org:6567127</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>info:eu-repo/grantAgreement/EC/H2020/873120/</dc:relation>
  <dc:relation>info:eu-repo/grantAgreement/EC/H2020/871669/</dc:relation>
  <dc:relation>doi:10.5281/zenodo.6567126</dc:relation>
  <dc:relation>url:https://zenodo.org/communities/sciops2022</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:subject>Adaptive Optics</dc:subject>
  <dc:subject>Reinforcement Learning</dc:subject>
  <dc:title>A Non Linear Control Method with Reinforcement Learning for Adaptive Optics with Pyramid Sensors</dc:title>
  <dc:type>info:eu-repo/semantics/lecture</dc:type>
  <dc:type>presentation</dc:type>
</oai_dc:dc>