论文标题
低型宽宽沟通自然出现在多机构学习系统中
Low-Bandwidth Communication Emerges Naturally in Multi-Agent Learning Systems
论文作者
论文摘要
在这项工作中，我们通过自然界的合作多代理行为来研究新兴的沟通。利用动物交流的见解，我们提出了一个基于社会代理人认知，知觉和行为能力的频谱（例如，信息素步道）到高带宽（例如组成语言）交流。通过一系列有关追求逃避游戏的实验，我们将多代理强化学习算法确定为通信谱的低频带宽度端的计算模型。
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.
