  Evidence that visual communication preceded written language and provided a
basis for it goes back to prehistory, in forms such as cave and rock paintings
depicting traces of our distant ancestors. Emergent communication research has
sought to explore how agents can learn to communicate in order to
collaboratively solve tasks. Existing research has focused on language, with a
learned communication channel transmitting sequences of discrete tokens between
the agents. In this work, we explore a visual communication channel between
agents that are allowed to draw with simple strokes. Our agents are
parameterised by deep neural networks, and the drawing procedure is
differentiable, allowing for end-to-end training. In the framework of a
referential communication game, we demonstrate that agents can not only
successfully learn to communicate by drawing, but with appropriate inductive
biases, can do so in a fashion that humans can interpret. We hope to encourage
future research to consider visual communication as a more flexible and
directly interpretable alternative of training collaborative agents.
