Multi-agent Communication meets Natural Language: Synergies between Functional and Structural Language Learning

In this work, we aim at making agents communicate On the other hand, multi-agent communication with humans in natural language. Our starting research (Foerster et al., 2016; Lazaridou et al., point is a language model that has been trained on 2017; Havrylov and Titov, 2017; Evtimova et al., generic, not task-specific language data. We then 2017; Lee et al., 2019) puts communication at the place this model in a multi-agent communication heart of agents' (language) learning. Implemented environment that generates task-specific rewards, within a multi-agent reinforcement learning setup, which are used to adapt or modulate the model, agents start tabula rasa and form communication making it task-conditional. We thus propose to decompose protocols that maximize task rewards. While this the problem of learning language use into purely utilitarian framework results in agents that two components: learning "what" to say based on successfully learn to solve the task by creating a a given situation, and learning "how" to say it. The communication protocol, these emergent communication "what" is the essence of communication that underlies protocols do not bear core properties of our intentions and is chosen by maximizing any natural language. Chaabouni et al. (2019) show that given utility, making it a functional, utility-driven protocols found through emergent communication, process. On the other hand, the "how" is a surface unlike natural language, do not conform to Zipf's realization of our intentions, i.e., the words we use Law of Abbreviation; Kottur et al. (2017) find that