Related papers: Learning to cooperate: Emergent communication in multi-agent navigation

Learning to cooperate: Emergent communication in multi-agent navigation

URL: http://arxiv.org/abs/2004.01097v2
Date: Tue, 30 Jun 2020 15:13:39 GMT
Title: Learning to cooperate: Emergent communication in multi-agent navigation
Authors: Ivana Kaji\'c, Eser Ayg\"un and Doina Precup
Abstract summary: We show that agents performing a cooperative navigation task learn an interpretable communication protocol. An analysis of the agents' policies reveals that emergent signals spatially cluster the state space. Using populations of agents, we show that the emergent protocol has basic compositional structure.
Score: 49.11609702016523
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Emergent communication in artificial agents has been studied to understand language evolution, as well as to develop artificial systems that learn to communicate with humans. We show that agents performing a cooperative navigation task in various gridworld environments learn an interpretable communication protocol that enables them to efficiently, and in many cases, optimally, solve the task. An analysis of the agents' policies reveals that emergent signals spatially cluster the state space, with signals referring to specific locations and spatial directions such as "left", "up", or "upper left room". Using populations of agents, we show that the emergent protocol has basic compositional structure, thus exhibiting a core property of natural language.

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