Learning how, what, and whether to communicate: emergence of protocommunication in reinforcement learning agents

Abstract

This paper examines whether and how a primitive form of communication emerges between adaptive agents by using their excess degrees of freedom in action and perception. As a case study, we consider a game in which two reinforcement learning agents learn to earn rewards by intruding into the other’s territory. Our simulation shows that agents with lights and light sensors can learn turn-taking behavior for avoiding collisions using visual communication. Further analysis reveals a variety in the mapping of messages to signals. In some cases, the differentiation of roles into a sender and a receiver was observed. The result confirmed that protocommunication can emerge through interaction between agents having generic reinforcement learning capability.