论文标题
多代理强化学习是语言进化研究的计算工具：历史背景和未来挑战
Multi-Agent Reinforcement Learning as a Computational Tool for Language Evolution Research: Historical Context and Future Challenges
论文作者
论文摘要
由于多代理强化学习（MARL）的最新进展，目前，代理人种群中紧急沟通的计算模型目前正在对机器学习社区产生兴趣。然而，目前的贡献仍然与较早的理论和计算文献相对脱节，旨在了解语言如何从先前语言物质中出现。本文的目的是将MARL最近的贡献定位在语言进化研究的历史背景下，并从这种理论和计算背景中提取一些挑战，以实现未来的研究。
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.
