  Social learning plays an important role in the development of human
intelligence. As children, we imitate our parents' speech patterns until we are
able to produce sounds; we learn from them praising us and scolding us; and as
adults, we learn by working with others. In this work, we survey the degree to
which this paradigm -- social learning -- has been mirrored in machine
learning. In particular, since learning socially requires interacting with
others, we are interested in how embodied agents can and have utilised these
techniques. This is especially in light of the degree to which recent advances
in natural language processing (NLP) enable us to perform new forms of social
learning. We look at how behavioural cloning and next-token prediction mirror
human imitation, how learning from human feedback mirrors human education, and
how we can go further to enable fully communicative agents that learn from each
other. We find that while individual social learning techniques have been used
successfully, there has been little unifying work showing how to bring them
together into socially embodied agents.
