Emergence of linguistic conventions in multi-agent reinforcement learning

Dorota Lipowska and Adam Lipowski

PLOS ONE, 2018, vol. 13, issue 11, 1-18

Abstract: Recently, emergence of signaling conventions, among which language is a prime example, draws a considerable interdisciplinary interest ranging from game theory, to robotics to evolutionary linguistics. Such a wide spectrum of research is based on much different assumptions and methodologies, but complexity of the problem precludes formulation of a unifying and commonly accepted explanation. We examine formation of signaling conventions in a framework of a multi-agent reinforcement learning model. When the network of interactions between agents is a complete graph or a sufficiently dense random graph, a global consensus is typically reached with the emerging language being a nearly unique object-word mapping or containing some synonyms and homonyms. On finite-dimensional lattices, the model gets trapped in disordered configurations with a local consensus only. Such a trapping can be avoided by introducing a population renewal, which in the presence of superlinear reinforcement restores an ordinary surface-tension driven coarsening and considerably enhances formation of efficient signaling.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0208095

DOI: 10.1371/journal.pone.0208095

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