Kalesha Bullard

Effective communication is an important skill for enabling information exchange and cooperation in multi-agent settings, in which AI agents coexist in shared environments with other agents (artificial or human). Indeed, emergent communication is now a vibrant field of research, with common settings involving discrete cheap-talk channels. One limitation of this setting however is that it does not allow for the emergent protocols to generalize beyond the training partners. Furthermore, the typical problem setting of discrete cheap-talk channels may be less appropriate for embodied agents that communicate implicitly through physical action. This talk presents research that investigates methods for enabling AI agents to learn general communication skills through interaction with other artificial agents. In particular, the talk will focus on my ongoing work within Multi-Agent Reinforcement Learning, investigating emergent communication protocols, inspired by communication in more realistic settings. We present a novel problem setting and a general approach that allows for zero-shot communication (ZSC), i.e., emergence of communication protocols that can generalize to independently trained agents. We also explore and analyze specific difficulties associated with finding globally optimal ZSC protocols, as complexity of the communication task increases or the modality for communication changes (e.g. from symbolic communication to implicit communication through physical movement, by an embodied artificial agent). Overall, this work opens up exciting avenues for learning general communication protocols in more complex domains.