  Computational modeling plays an essential role in the study of language
emergence. It aims to simulate the conditions and learning processes that could
trigger the emergence of a structured language within a simulated controlled
environment. Several methods have been used to investigate the origin of our
language, including agent-based systems, Bayesian agents, genetic algorithms,
and rule-based systems. This chapter explores another class of computational
models that have recently revolutionized the field of machine learning: deep
learning models. The chapter introduces the basic concepts of deep and
reinforcement learning methods and summarizes their helpfulness for simulating
language emergence. It also discusses the key findings, limitations, and recent
attempts to build realistic simulations. This chapter targets linguists and
cognitive scientists seeking an introduction to deep learning as a tool to
investigate language evolution.
