Conference paper Open Access

Aspiration-based Perturbed Learning Automata

Chasparis, Georgios

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  "@context": "", 
  "@id": "", 
  "@type": "ScholarlyArticle", 
  "creator": [
      "@id": "", 
      "@type": "Person", 
      "affiliation": "Software Competence Center Hagenberg GmbH", 
      "name": "Chasparis, Georgios"
  "datePublished": "2018-03-01", 
  "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>", 
  "headline": "Aspiration-based Perturbed Learning Automata", 
  "identifier": "", 
  "image": "", 
  "keywords": [
    "Learning automata, distributed optimization, coordination games"
  "license": "", 
  "name": "Aspiration-based Perturbed Learning Automata", 
  "url": ""
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