学习合作: 多智能体导航中的紧急通信（CS LG）

• 2020 年 4 月 6 日
• 筆記

人工智能中的紧急通信已经被用来研究语言进化，以及开发学习与人类交流的人工系统。我们展示了在不同的网格世界环境中代理执行协同导航任务来了解一个可解释的通信协议，使他们能够有效、最佳地在许多情况下解决任务。对代理策略的分析表明，紧急信号在空间上聚集了状态空间，信号指向特定的位置和空间方向，如“左”、“上”或“左上房间”。 利用代理群体，我们证明了紧急协议具有基本的组合结构，从而展现了自然语言的核心特性。

Learning to cooperate: Emergent communication in multi-agent navigation

Emergent communication in artificial agents has been studied to understand language evolution, as well as to develop artificial systems that learn to communicate with humans. We show that agents performing a cooperative navigation task in various gridworld environments learn an interpretable communication protocol that enables them to efficiently, and in many cases, optimally, solve the task. An analysis of the agents' policies reveals that emergent signals spatially cluster the state space, with signals referring to specific locations and spatial directions such as "left", "up", or "upper left room". Using populations of agents, we show that the emergent protocol has basic compositional structure, thus exhibiting a core property of natural language.

原文作者：Ivana Kajić

原文地址：https://arxiv.org/abs/2004.01097