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.

本文研究了Lewis信号博弈学习的问题，发现其标准目标可以分解为两个部分：协作损失和信息损失，进而揭示了其过拟合的两个潜在源（协作损失过度匹配和信息损失过拟合），并且控制了协作损失的过拟合，从而恢复出期望的属性，使得新兴语言更加结构化和有更好的泛化能力。

Lewis博弈中的紧急交流：概括与过拟合