In this work, we study emergent communication through the lens of cooperative multi-agent behavior in nature. Using insights from animal communication, we propose a spectrum from low-bandwidth (e.g. pheromone trails) to high-bandwidth (e.g. compositional language) communication that is based on the cognitive, perceptual, and behavioral capabilities of social agents. Through a series of experiments with pursuit-evasion games, we identify multi-agent reinforcement learning algorithms as a computational model for the low-bandwidth end of the communication spectrum.

中文翻译：

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低带宽通信自然地出现在多智能体学习系统中

在这项工作中，我们将通过自然界中的协作多主体行为的视角研究紧急交流。利用来自动物交流的见解，我们提出了一种基于社会主体的认知，感知和行为能力的频谱，从低带宽（例如信息素路径）到高带宽（例如组成语言）通信。通过对逃避游戏的一系列实验，我们将多主体强化学习算法确定为通信频谱低带宽端的计算模型。