Learning interpretable communication is essential for multi-agent and human-agent teams (HATs). In reinforcement learning partially-observable environments, agents may convey information to others via learned communication, allowing the team complete its task. Inspired by human languages, recent works study discrete (using only a finite set of tokens) sparse (communicating at some time-steps) c...