  In this work, we propose a computational framework in which agents equipped
with communication capabilities simultaneously play a series of referential
games, where agents are trained using deep reinforcement learning. We
demonstrate that the framework mirrors linguistic phenomena observed in natural
language: i) the outcome of contact between communities is a function of inter-
and intra-group connectivity; ii) linguistic contact either converges to the
majority protocol, or in balanced cases leads to novel creole languages of
lower complexity; and iii) a linguistic continuum emerges where neighboring
languages are more mutually intelligible than farther removed languages. We
conclude that intricate properties of language evolution need not depend on
complex evolved linguistic capabilities, but can emerge from simple social
exchanges between perceptually-enabled agents playing communication games.
