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Framework for Deep Reinforcement Learning with GPU-CPU Multiprocessing

Ivan Sosin; Oleg Svidchenko; Aleksandra Malysheva; Daniel Kudenko; Aleksei Shpilman


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  <identifier identifierType="DOI">10.5281/zenodo.1938263</identifier>
  <creators>
    <creator>
      <creatorName>Ivan Sosin</creatorName>
      <affiliation>JetBrains Research</affiliation>
    </creator>
    <creator>
      <creatorName>Oleg Svidchenko</creatorName>
      <affiliation>JetBrains Research</affiliation>
    </creator>
    <creator>
      <creatorName>Aleksandra Malysheva</creatorName>
      <affiliation>JetBrains Research</affiliation>
    </creator>
    <creator>
      <creatorName>Daniel Kudenko</creatorName>
      <affiliation>JetBrains Research</affiliation>
    </creator>
    <creator>
      <creatorName>Aleksei Shpilman</creatorName>
      <affiliation>JetBrains Research</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Framework for Deep Reinforcement Learning with GPU-CPU Multiprocessing</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2018</publicationYear>
  <dates>
    <date dateType="Issued">2018-12-04</date>
  </dates>
  <resourceType resourceTypeGeneral="Software"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/1938263</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsSupplementTo">https://github.com/iasawseen/MultiServerRL/tree/v1.0</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.1938262</relatedIdentifier>
  </relatedIdentifiers>
  <version>v1.0</version>
  <rightsList>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
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  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;One of the main challenges faced in Deep Reinforcement Learning is that running simulations may be CPU-heavy, while the optimal computing device for training neural networks is a GPU. One way to overcome this problem is building a custom machine with GPU to CPU proportions that avoid bottlenecking one or the other. Another is to have the GPU machine work together with the CPU machine and/or launching one or both via cloud computing service. We have designed a framework for such a tandem interaction.&lt;/p&gt;

&lt;p&gt;Authors: Ivan Sosin, Oleg Svidchenko, Aleksandra Malysheva, Daniel Kudenko, Aleksei Shpilman.&lt;/p&gt;</description>
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