Learning collaborative behaviors is essential for multi-agent systems. Traditionally, multi-agent reinforcement learning solves this implicitly through a joint reward and centralized observations, assuming collaborative behavior will emerge. Other studies propose to learn from demonstrations of a group of collaborative experts. Instead, we propose an efficient and explicit way of learning collaborative behaviors in multi-agent systems by leveraging expertise from only a single human. Our insight is that humans can naturally take on various roles in a team. We show that agents can effectively learn to collaborate by allowing a human operator to dynamically switch between controlling agents for a short period and incorporating a human-like theory-of-mind model of teammates. Our experiments showed that our method improves the success rate of a challenging collaborative hide-and-seek task by up to 58$% with only 40 minutes of human guidance. We further demonstrate our findings transfer to the real world by conducting multi-robot experiments.

本研究解决了多智能体系统中有效学习协作行为的难题。我们提出了一种高效明确的方法，通过借助单个人类专家的指导，让智能体学习协作。这一方法在具有挑战性的合作捉迷藏任务中提升了成功率，证实了在人类指导下，智能体能够有效协作，且实验结果能够应用于现实世界。

从单人指导实现多机器人协作