May 20, 2022 Presentation Open Access

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

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  <identifier identifierType="DOI">10.5281/zenodo.6567127</identifier>
  <creators>
    <creator>
      <creatorName>Pou, Bartomeu</creatorName>
      <givenName>Bartomeu</givenName>
      <familyName>Pou</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-8634-2316</nameIdentifier>
      <affiliation>Barcelona Supercomputing Center, Universitat Politècnica de Catalunya</affiliation>
    </creator>
    <creator>
      <creatorName>Smith, Jeffrey</creatorName>
      <givenName>Jeffrey</givenName>
      <familyName>Smith</familyName>
      <affiliation>School of Computing, Australian National University</affiliation>
    </creator>
    <creator>
      <creatorName>Quinones, Eduardo</creatorName>
      <givenName>Eduardo</givenName>
      <familyName>Quinones</familyName>
      <affiliation>Barcelona Supercomputing Center</affiliation>
    </creator>
    <creator>
      <creatorName>Martin, Mario</creatorName>
      <givenName>Mario</givenName>
      <familyName>Martin</familyName>
      <affiliation>Universitat Politècnica de Catalunya</affiliation>
    </creator>
    <creator>
      <creatorName>Gratadour, Damien</creatorName>
      <givenName>Damien</givenName>
      <familyName>Gratadour</familyName>
      <affiliation>LESIA , Observatoire de Paris, Université PSL, CNRS, Sorbonne Université, Université de Paris, Australian National University</affiliation>
    </creator>
  </creators>
  <titles>
    <title>A Non Linear Control Method with Reinforcement Learning for Adaptive Optics with Pyramid Sensors</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2022</publicationYear>
  <subjects>
    <subject>Adaptive Optics</subject>
    <subject>Reinforcement Learning</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2022-05-20</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Text">Presentation</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/6567127</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.6567126</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/sciops2022</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&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;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/100010661</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/873120/">873120</awardNumber>
      <awardTitle>RISE International Network for Solutions Technologies and Applications of Real-time Systems</awardTitle>
    </fundingReference>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/100010661</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/871669/">871669</awardNumber>
      <awardTitle>A Model-driven development framework for highly Parallel and EneRgy-Efficient computation supporting multi-criteria optimisation</awardTitle>
    </fundingReference>
  </fundingReferences>
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