We use reinforcement learning to study how language can be used as a tool for agents to accomplish tasks in their environment, and show that structure in the evolved language emerges naturally through iterated learning, leading to the development of compositional language for describing and generalising about unseen objects.

Reimplementation of Manin (2008)'s models of lexical-semantic evolution

A spreading activation (neural network) model of word production and lexicon evolution