  The ability of algorithms to evolve or learn (compositional) communication
protocols has traditionally been studied in the language evolution literature
through the use of emergent communication tasks. Here we scale up this research
by using contemporary deep learning methods and by training
reinforcement-learning neural network agents on referential communication
games. We extend previous work, in which agents were trained in symbolic
environments, by developing agents which are able to learn from raw pixel data,
a more challenging and realistic input representation. We find that the degree
of structure found in the input data affects the nature of the emerged
protocols, and thereby corroborate the hypothesis that structured compositional
language is most likely to emerge when agents perceive the world as being
structured.
