Effective communication is an essential component in collaborative multi-agent systems. Situations where explicit messaging is not feasible have been common in human society throughout history, which motivate the study of implicit communication. Previous works on learning implicit communication mostly rely on theory of mind (ToM), where agents infer the mental states and intentions of others by interpreting their actions. However, ToM-based methods become less effective in making accurate inferences in complex tasks. In this work, we propose the Implicit Channel Protocol (ICP) framework, which allows agents to construct implicit communication channels similar to the explicit ones. ICP leverages a subset of actions, denoted as the scouting actions, and a mapping between information and these scouting actions that encodes and decodes the messages. We propose training algorithms for agents to message and act, including learning with a randomly initialized information map and with a delayed information map. The efficacy of ICP has been tested on the tasks of Guessing Number, Revealing Goals, and Hanabi, where ICP significantly outperforms baseline methods through more efficient information transmission.

翻译：有效的通信是协作多智能体系统中的关键组成部分。在人类社会的历史进程中，显式消息传递不可行的情况屡见不鲜，这推动了隐式通信的研究。先前关于学习隐式通信的工作大多依赖于心智理论，即智能体通过解读其他智能体的行为来推断其心理状态和意图。然而，在复杂任务中，基于心智理论的方法进行准确推断的效果会降低。在本工作中，我们提出了隐式信道协议框架，该框架允许智能体构建类似于显式信道的隐式通信信道。ICP利用一个被称为侦察动作的动作子集，以及信息与这些侦察动作之间的映射关系来编码和解码消息。我们提出了智能体进行消息传递和行动的训练算法，包括使用随机初始化的信息映射和延迟信息映射进行学习。ICP的有效性已在猜数字、目标揭示和Hanabi等任务上得到验证，结果表明，通过更高效的信息传输，ICP显著优于基线方法。