  Lewis signaling games are a class of simple communication games for
simulating the emergence of language. In these games, two agents must agree on
a communication protocol in order to solve a cooperative task. Previous work
has shown that agents trained to play this game with reinforcement learning
tend to develop languages that display undesirable properties from a linguistic
point of view (lack of generalization, lack of compositionality, etc). In this
paper, we aim to provide better understanding of this phenomenon by
analytically studying the learning problem in Lewis games. As a core
contribution, we demonstrate that the standard objective in Lewis games can be
decomposed in two components: a co-adaptation loss and an information loss.
This decomposition enables us to surface two potential sources of overfitting,
which we show may undermine the emergence of a structured communication
protocol. In particular, when we control for overfitting on the co-adaptation
loss, we recover desired properties in the emergent languages: they are more
compositional and generalize better.
