  For communication to happen successfully, a common language is required
between agents to understand information communicated by one another. Inducing
the emergence of a common language has been a difficult challenge to
multi-agent learning systems. In this work, we introduce an alternative
perspective to the communicative messages sent between agents, considering them
as different incomplete views of the environment state. Based on this
perspective, we propose a simple approach to induce the emergence of a common
language by maximizing the mutual information between messages of a given
trajectory in a self-supervised manner. By evaluating our method in
communication-essential environments, we empirically show how our method leads
to better learning performance and speed, and learns a more consistent common
language than existing methods, without introducing additional learning
parameters.
