Exploring Zero-Shot Emergent Communication in Embodied Multi-Agent Populations
This work addresses the limitation of generalization in emergent communication for multi-agent AI, though it is incremental by focusing on a new modality.
The paper tackles the problem of emergent communication in multi-agent systems by extending it to a new modality where agents communicate via joint actuation in 3D environments, showing that under realistic assumptions, they can develop protocols that generalize to novel partners.
Effective communication is an important skill for enabling information exchange and cooperation in multi-agent settings. Indeed, emergent communication is now a vibrant field of research, with common settings involving discrete cheap-talk channels. One limitation of this setting is that it does not allow for the emergent protocols to generalize beyond the training partners. Furthermore, so far emergent communication has primarily focused on the use of symbolic channels. In this work, we extend this line of work to a new modality, by studying agents that learn to communicate via actuating their joints in a 3D environment. We show that under realistic assumptions, a non-uniform distribution of intents and a common-knowledge energy cost, these agents can find protocols that generalize to novel partners. We also explore and analyze specific difficulties associated with finding these solutions in practice. Finally, we propose and evaluate initial training improvements to address these challenges, involving both specific training curricula and providing the latent feature that can be coordinated on during training.