Agent learning from human interaction often relies on explicit signals, but implicit social cues, such as prosody in speech, could provide valuable information for more effective learning. This paper advocates for the integration of prosody as a teaching signal to enhance agent learning from human teachers. Through two exploratory studies--one examining voice feedback in an interactive reinforcement learning setup and the other analyzing restricted audio from human demonstrations in three Atari games--we demonstrate that prosody carries significant information about task dynamics. Our findings suggest that prosodic features, when coupled with explicit feedback, can enhance reinforcement learning outcomes. Moreover, we propose guidelines for prosody-sensitive algorithm design and discuss insights into teaching behavior. Our work underscores the potential of leveraging prosody as an implicit signal for more efficient agent learning, thus advancing human-agent interaction paradigms.

翻译：智能体从人类交互中学习通常依赖于显式信号，但隐式社交线索（如语音中的韵律）可能为更高效的学习提供有价值的信息。本文主张将韵律作为教学信号进行整合，以增强智能体从人类教师处学习的能力。通过两项探索性研究——一项考察交互式强化学习设置中的语音反馈，另一项分析三个Atari游戏中人类演示的受限音频——我们证明韵律携带关于任务动态的重要信息。研究结果表明，韵律特征与显式反馈结合时能够提升强化学习效果。此外，我们提出了韵律敏感算法设计的指导原则，并探讨了对教学行为的启示。本工作强调了利用韵律作为隐式信号以实现更高效智能体学习的潜力，从而推动人机交互范式的发展。