Title: Learning to Communicate using Contrastive Learning . Abstract: Communication is a powerful tool for coordination in multi-agent RL.  Inducing an effective, common language has been 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. Based on this perspective, we propose to learn to communicate using contrastive learning by maximizing 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 task-relevant information from the environment. Finally, we demonstrate promising results on zero-shot communication, a first for MARL. Overall, we show the power of contrastive learning, and self-supervised learning in general, as a method for learning to communicate.