Over-communicate no more: Situated RL agents learn concise communication protocols

Understand

While it is known that communication facilitates cooperation in multi-agent settings, it is unclear how to design artificial agents that can learn to effectively and efficiently communicate with each other.

• Much research on communication emergence uses reinforcement learning (RL) and explores unsituated communication in one-step referential tasks -- the tasks are not temporally interactive and lack time pressures typically present in natural communication.
• In these settings, agents may successfully learn to communicate, but they do not learn to exchange information concisely -- they tend towards over-communication and an inefficient encoding.
• Here, we explore situated communication in a multi-step task, where the acting agent has to forgo an environmental action to communicate.