Conference paper Open Access

# Aspiration-based Perturbed Learning Automata

Chasparis, Georgios

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<dc:creator>Chasparis, Georgios</dc:creator>
<dc:date>2018-03-01</dc:date>
<dc:description>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.</dc:description>
<dc:identifier>https://zenodo.org/record/1186662</dc:identifier>
<dc:identifier>10.5281/zenodo.1186662</dc:identifier>
<dc:identifier>oai:zenodo.org:1186662</dc:identifier>
<dc:relation>info:eu-repo/grantAgreement/EC/H2020/644235/</dc:relation>
<dc:relation>doi:10.5281/zenodo.1186661</dc:relation>
<dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
<dc:subject>Learning automata, distributed optimization, coordination games</dc:subject>
<dc:title>Aspiration-based Perturbed Learning Automata</dc:title>
<dc:type>info:eu-repo/semantics/conferencePaper</dc:type>
<dc:type>publication-conferencepaper</dc:type>
</oai_dc:dc>

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