Consider a collaborative task that requires communication. Two agents are placed in an environment and must create a language from scratch in order to coordinate. Recent work has been interested in what kinds of languages emerge when deep reinforcement learning agents are put in such a situation, and in particular in the factors that cause language to be compositional-i.e. meaning is expressed by combining words which themselves have meaning. Evolutionary linguists have also studied the emergence of compositional language for decades, and they find that in addition to structural priors like those already studied in deep learning, the dynamics of transmitting language from generation to generation contribute significantly to the emergence of compositionality. In this paper, we introduce these cultural evolutionary dynamics into language emergence by periodically replacing agents in a population to create a knowledge gap, implicitly inducing cultural transmission of language. We show that this implicit cultural transmission encourages the resulting languages to exhibit better compositional generalization and suggest how elements of cultural dynamics can be further integrated into populations of deep agents.
翻译:考虑一个需要沟通的协作任务。 两个代理商被置于一个环境中,必须从零开始创造一种语言,以便协调。最近的工作一直关注到当深层强化学习代理商在这种情况下出现何种语言,特别是导致语言构成的因素,特别是导致语言构成的因素,意思是通过合并本身具有意义的词语来表达的。进化语言学家还研究了几十年来组成语言的出现,他们发现,除了像深层次学习中研究的这些结构前科外,一代代之间传播语言的动态也极大地促进了组成能力的出现。在本文件中,我们将这些文化进化动态引入语言的出现,通过定期替换一个人口代理商来创造知识差距,暗含地诱导语言的文化传播。我们表明,这种隐含的文化传播鼓励由此产生的语言展示更好的组成普遍性,并建议如何将文化动态的要素进一步融入深层次的代理商群中。