  How do we know if communication is emerging in a multi-agent system? The vast
majority of recent papers on emergent communication show that adding a
communication channel leads to an increase in reward or task success. This is a
useful indicator, but provides only a coarse measure of the agent's learned
communication abilities. As we move towards more complex environments, it
becomes imperative to have a set of finer tools that allow qualitative and
quantitative insights into the emergence of communication. This may be
especially useful to allow humans to monitor agents' behaviour, whether for
fault detection, assessing performance, or even building trust. In this paper,
we examine a few intuitive existing metrics for measuring communication, and
show that they can be misleading. Specifically, by training deep reinforcement
learning agents to play simple matrix games augmented with a communication
channel, we find a scenario where agents appear to communicate (their messages
provide information about their subsequent action), and yet the messages do not
impact the environment or other agent in any way. We explain this phenomenon
using ablation studies and by visualizing the representations of the learned
policies. We also survey some commonly used metrics for measuring emergent
communication, and provide recommendations as to when these metrics should be
used.
