Multi-Agent Reinforcement Learning as a Computational Tool for Language Evolution Research: Historical Context and Future Challenges
It addresses the disconnect between recent MARL models and earlier theoretical literature on language evolution, aiming to bridge gaps for researchers in machine learning and linguistics, but is incremental as it reviews and synthesizes existing work.
The paper positions recent Multi-Agent Reinforcement Learning (MARL) contributions within the historical context of language evolution research and extracts challenges for future work, without presenting new experimental results or concrete numbers.
Computational models of emergent communication in agent populations are currently gaining interest in the machine learning community due to recent advances in Multi-Agent Reinforcement Learning (MARL). Current contributions are however still relatively disconnected from the earlier theoretical and computational literature aiming at understanding how language might have emerged from a prelinguistic substance. The goal of this paper is to position recent MARL contributions within the historical context of language evolution research, as well as to extract from this theoretical and computational background a few challenges for future research.