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Computational models of emergent communication in agent populations arecurrently gaining interest in the machine learning community due to recentadvances in Multi-Agent Reinforcement Learning (MARL). Current contributionsare however still relatively disconnected from the earlier theoretical andcomputational literature aiming at understanding how language might haveemerged from a prelinguistic substance. The goal of this paper is to positionrecent MARL contributions within the historical context of language evolutionresearch, as well as to extract from this theoretical and computationalbackground a few challenges for future research.

Multi-Agent Reinforcement Learning as a Computational Tool for Language Evolution Research: Historical Context and Future Challenges