Among an array of techniques proposed to speed-up reinforcement learning (RL), learning from human demonstration has a proven record of success. A related technique, called Human-Agent Transfer, and its confidence-based derivatives have been successfully applied to single-agent RL. This article investigates their application to collaborative multi-agent RL problems. We show that a first-cut extension may leave room for improvement in some domains, and propose a new algorithm called coordination confidence (CC). CC analyzes the difference in perspectives between a human demonstrator (global view) and the learning agents (local view) and informs the agents’ action choices when the difference is critical and simply following the human demonstration can lead to miscoordination. We conduct experiments in three domains to investigate the performance of CC in comparison with relevant baselines.

中文翻译：

---

具有协调信心的团队从人类演示中学习

在提出的加速强化学习 (RL) 的一系列技术中，从人类演示中学习已被证明是成功的记录。一种称为 Human-Agent Transfer 的相关技术及其基于置信度的衍生产品已成功应用于单智能体 RL。本文研究了它们在协作多智能体 RL 问题中的应用。我们展示了 first-cut 扩展可能会在某些领域留下改进的空间，并提出了一种新的算法，称为协调信心（抄送）。CC 分析人类演示者（全局视图）和学习代理（本地视图）之间的观点差异，并在差异很关键时告知代理的行动选择，并且简单地遵循人类演示可能会导致不协调。我们在三个领域进行实验，以研究 CC 与相关基线相比的性能。