  The literature in modern machine learning has only negative results for
learning to communicate between competitive agents using standard RL. We
introduce a modified sender-receiver game to study the spectrum of
partially-competitive scenarios and show communication can indeed emerge in a
competitive setting. We empirically demonstrate three key takeaways for future
research. First, we show that communication is proportional to cooperation, and
it can occur for partially competitive scenarios using standard learning
algorithms. Second, we highlight the difference between communication and
manipulation and extend previous metrics of communication to the competitive
case. Third, we investigate the negotiation game where previous work failed to
learn communication between independent agents (Cao et al., 2018). We show
that, in this setting, both agents must benefit from communication for it to
emerge; and, with a slight modification to the game, we demonstrate successful
communication between competitive agents. We hope this work overturns
misconceptions and inspires more research in competitive emergent
communication.
