  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.
