Conference paper Open Access

Aspiration-based Perturbed Learning Automata

Chasparis, Georgios


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{
  "description": "<p>This paper introduces a novel payoff-based learning scheme for distributed optimization in repeatedly-played strategic-form games. Standard reinforcement-based learning exhibits several limitations with respect to their asymptotic stability. For example, in two-player coordination games, payoff-dominant (or efficient) Nash equilibria may not be stochastically stable. In this work, we present an extension of perturbed learning automata, namely aspiration-based perturbed learning automata (APLA) that overcomes these limitations. We provide a stochastic stability analysis in multi-player coordination games. In the case of two-player coordination games, we show that the payoff-dominant Nash equilibrium is the unique stochastically stable state.</p>", 
  "license": "http://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "Software Competence Center Hagenberg GmbH", 
      "@id": "https://orcid.org/0000-0003-3059-3575", 
      "@type": "Person", 
      "name": "Chasparis, Georgios"
    }
  ], 
  "url": "https://zenodo.org/record/1186662", 
  "image": "https://zenodo.org/static/img/logos/zenodo-gradient-round.svg", 
  "datePublished": "2018-03-01", 
  "headline": "Aspiration-based Perturbed Learning Automata", 
  "keywords": [
    "Learning automata, distributed optimization, coordination games"
  ], 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.5281/zenodo.1186662", 
  "@id": "https://doi.org/10.5281/zenodo.1186662", 
  "@type": "ScholarlyArticle", 
  "name": "Aspiration-based Perturbed Learning Automata"
}
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