Journal article Open Access

Stochastic Stability of Perturbed Learning Automata in Positive-Utility Games

Chasparis, Georgios


DataCite XML Export

<?xml version='1.0' encoding='utf-8'?>
<resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd">
  <identifier identifierType="DOI">10.5281/zenodo.1186647</identifier>
  <creators>
    <creator>
      <creatorName>Chasparis, Georgios</creatorName>
      <givenName>Georgios</givenName>
      <familyName>Chasparis</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-3059-3575</nameIdentifier>
      <affiliation>Software Competence Center Hagenberg GmbH</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Stochastic Stability of Perturbed Learning Automata in Positive-Utility Games</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2018</publicationYear>
  <subjects>
    <subject>Learning automata, distributed optimization, stochastic stability</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2018-02-21</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/1186647</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.1186646</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://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;This paper considers a class of reinforcement-based learning (namely, perturbed learning automata) and provides a&lt;br&gt;
stochastic-stability analysis in repeatedly-played, positive-utility, strategic-form games. Prior work in this class of learning dynamics primarily analyzes asymptotic convergence through stochastic approximations, where convergence can be associated with the limit points of an ordinary-differential equation (ODE). However, analyzing global convergence through an ODE-approximation requires the existence of a Lyapunov or a potential function, which naturally restricts the analysis to a fine class of games. To overcome these limitations, this paper introduces an alternative framework for analyzing asymptotic convergence that is based upon an explicit characterization of the invariant probability measure of the induced Markov chain. We further provide a methodology for computing the invariant probability measure in&lt;br&gt;
positive-utility games, together with an illustration in the context of coordination games.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/644235/">644235</awardNumber>
      <awardTitle>REfactoring Parallel Heterogeneous Resource-Aware Applications  - a Software Engineering Approach</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
4
16
views
downloads
All versions This version
Views 44
Downloads 1616
Data volume 7.1 MB7.1 MB
Unique views 44
Unique downloads 1515

Share

Cite as