Learning to Improve Representations by Communicating About Perspectives

Effective latent representations need to capture abstract features of the externalworld. We hypothesise that the necessity for a group of agents to reconcile theirsubjective interpretations of a shared environment state is an essential factor in-fluencing this property. To test this hypothesis, we propose an architecture whereindividual agents in a population receive different observations of the same under-lying state and learn latent representations that they communicate to each other. Wehighlight a fundamental link between emergent communication and representationlearning: the role of language as a cognitive tool and the opportunities conferredby subjectivity, an inherent property of most multi-agent systems. We present aminimal architecture comprised of a population of autoencoders, where we defineloss functions, capturing different aspects of effective communication, and examinetheir effect on the learned representations. We show that our proposed architectureallows the emergence of aligned representations. The subjectivity introduced bypresenting agents with distinct perspectives of the environment state contributes tolearning abstract representations that outperform those learned by both a single au-toencoder and a population of autoencoders, presented with identical perspectives.Altogether, our results demonstrate how communication from subjective perspec-tives can lead to the acquisition of more abstract representations in multi-agentsystems, opening promising perspectives for future research at the intersection ofrepresentation learning and emergent communication.