用语音语调做教学信号，让智能体学得更快更好
Prosody as a Teaching Signal for Agent Learning: Exploratory Studies and Algorithmic Implications
- 用语音语调作为隐式教学信号，辅助强化学习
- 实验证明语调包含任务动态的关键信息，提升学习效果
- 适合研究人机交互与语音感知的算法设计者
智能体从人类互动中学习通常依赖显式信号，但语音中的语调等隐式社会线索可能提供有价值的信息。本文倡导将语调作为教学信号以增强智能体的学习效果。通过两项探索性研究——一项在交互式强化学习中分析语音反馈，另一项分析三个Atari游戏中人类示范的受限音频——我们发现语调携带了关于任务动态的重要信息。研究结果表明，当语调特征与显式反馈结合时，可提升强化学习表现。我们还提出了针对语调敏感算法设计的指南，并探讨了教学行为的启示。本工作强调了利用语调作为隐式信号促进更高效智能体学习的潜力，从而推动人机交互范式的演进。
原文摘要 · Abstract (English)
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.
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