Quasi-Equivalence Discovery for Zero-Shot Emergent Communication

by   Kalesha Bullard, et al.

Effective communication is an important skill for enabling information exchange in multi-agent settings and emergent communication is now a vibrant field of research, with common settings involving discrete cheap-talk channels. Since, by definition, these settings involve arbitrary encoding of information, typically they do not allow for the learned protocols to generalize beyond training partners. In contrast, in this work, we present a novel problem setting and the Quasi-Equivalence Discovery (QED) algorithm that allows for zero-shot coordination (ZSC), i.e., discovering protocols that can generalize to independently trained agents. Real world problem settings often contain costly communication channels, e.g., robots have to physically move their limbs, and a non-uniform distribution over intents. We show that these two factors lead to unique optimal ZSC policies in referential games, where agents use the energy cost of the messages to communicate intent. Other-Play was recently introduced for learning optimal ZSC policies, but requires prior access to the symmetries of the problem. Instead, QED can iteratively discovers the symmetries in this setting and converges to the optimal ZSC policy.


page 2

page 7

page 8

page 12

page 13

page 14


Exploring Zero-Shot Emergent Communication in Embodied Multi-Agent Populations

Effective communication is an important skill for enabling information e...

Off-Belief Learning

The standard problem setting in Dec-POMDPs is self-play, where the goal ...

K-level Reasoning for Zero-Shot Coordination in Hanabi

The standard problem setting in cooperative multi-agent settings is self...

A New Formalism, Method and Open Issues for Zero-Shot Coordination

In many coordination problems, independently reasoning humans are able t...

Adaptive Coordination in Social Embodied Rearrangement

We present the task of "Social Rearrangement", consisting of cooperative...

Cheap Talk Discovery and Utilization in Multi-Agent Reinforcement Learning

By enabling agents to communicate, recent cooperative multi-agent reinfo...

Zero-Shot Generalization using Intrinsically Motivated Compositional Emergent Protocols

Human language has been described as a system that makes use of finite m...

Please sign up or login with your details

Forgot password? Click here to reset