  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.
