  Referential games offer a grounded learning environment for neural agents
which accounts for the fact that language is functionally used to communicate.
However, they do not take into account a second constraint considered to be
fundamental for the shape of human language: that it must be learnable by new
language learners.
  Cogswell et al. (2019) introduced cultural transmission within referential
games through a changing population of agents to constrain the emerging
language to be learnable. However, the resulting languages remain inherently
biased by the agents' underlying capabilities.
  In this work, we introduce Language Transmission Engine to model both
cultural and architectural evolution in a population of agents. As our core
contribution, we empirically show that the optimal situation is to take into
account also the learning biases of the language learners and thus let language
and agents co-evolve. When we allow the agent population to evolve through
architectural evolution, we achieve across the board improvements on all
considered metrics and surpass the gains made with cultural transmission. These
results stress the importance of studying the underlying agent architecture and
pave the way to investigate the co-evolution of language and agent in language
emergence studies.
