In many situations, communication between agents is a critical component of cooperative multi-agent systems, however, it can be difficult to learn or evolve. In this paper, we investigate a simple way in which the emergence of communication may be facilitated. Namely, we explore the effects of when agents can mimic preexisting, externally generated useful signals. The key idea here is that these signals incentivise listeners to develop positive responses, that can then also be invoked by speakers mimicking those signals. This investigation starts with formalising this problem, and demonstrating that this form of mimicry changes optimisation dynamics and may provide the opportunity to escape non-communicative local optima. We then explore the problem empirically with a simulation in which spatially situated agents must communicate to collect resources. Our results show that both evolutionary optimisation and reinforcement learning may benefit from this intervention.

翻译：在多智能体协作系统中，智能体间的通信常是关键组成部分，但其学习或演化过程往往存在困难。本文研究了一种促进通信机制涌现的简易途径，即探讨智能体模仿预先存在的外部有用信号时产生的影响。其核心思想在于：这些信号能够激励接收方形成积极响应模式，而发送方通过模仿这些信号亦可触发相同响应。研究首先对该问题进行形式化建模，证明此类模仿行为会改变优化动态，并可能帮助系统逃离非通信的局部最优解。随后，我们通过空间情境下智能体需借助通信收集资源的仿真实验进行实证探索。结果表明，无论是进化优化算法还是强化学习方法，均可从这种干预机制中获益。