Towards Sample Efficient Learners in Population based Referential Games through Action Advising

The ability of agents to learn to communicate through interaction has been studied through emergent communication tasks. Previous works in this domain have studied the linguistic properties of the emergent languages like compositionality, generalization, and as well as the environmental pressures that shape them. However, most of these experiments require a considerable amount of shared training time between agents to communicate successfully. Our work highlights the problem of sample inefficiency of agents in population-based referential games and proposes an Action Advising framework to counter it.