Socially Supervised Representation Learning: the Role of Subjectivity in
  Learning Efficient Representations
  Despite its rise as a prominent solution to the data inefficiency of today's
machine learning models, self-supervised learning has yet to be studied from a
purely multi-agent perspective. In this work, we propose that aligning internal
subjective representations, which naturally arise in a multi-agent setup where
agents receive partial observations of the same underlying environmental state,
can lead to more data-efficient representations. We propose that multi-agent
environments, where agents do not have access to the observations of others but
can communicate within a limited range, guarantees a common context that can be
leveraged in individual representation learning. The reason is that subjective
observations necessarily refer to the same subset of the underlying
environmental states and that communication about these states can freely offer
a supervised signal. To highlight the importance of communication, we refer to
our setting as \textit{socially supervised representation learning}. We present
a minimal architecture comprised of a population of autoencoders, where we
define loss functions, capturing different aspects of effective communication,
and examine their effect on the learned representations. We show that our
proposed architecture allows the emergence of aligned representations. The
subjectivity introduced by presenting agents with distinct perspectives of the
environment state contributes to learning abstract representations that
outperform those learned by a single autoencoder and a population of
autoencoders, presented with identical perspectives of the environment state.
Altogether, our results demonstrate how communication from subjective
perspectives can lead to the acquisition of more abstract representations in
multi-agent systems, opening promising perspectives for future research at the
intersection of representation learning and emergent communication.
