  Communication is a powerful tool for coordination in multi-agent RL. But
inducing an effective, common language is a difficult challenge, particularly
in the decentralized setting. In this work, we introduce an alternative
perspective where communicative messages sent between agents are considered as
different incomplete views of the environment state. By examining the
relationship between messages sent and received, we propose to learn to
communicate using contrastive learning to maximize the mutual information
between messages of a given trajectory. In communication-essential
environments, our method outperforms previous work in both performance and
learning speed. Using qualitative metrics and representation probing, we show
that our method induces more symmetric communication and captures global state
information from the environment. Overall, we show the power of contrastive
learning and the importance of leveraging messages as encodings for effective
communication.
