用多样且有信息量的语言指导，让机器人更快学会新任务。
Teaching Embodied Reinforcement Learning Agents: Informativeness and Diversity of Language Use
- 设计包含过去反馈和未来指引的语言输入方式
- 在4个基准上实现更好泛化与快速适应能力
- 适合研究人机交互与智能体教学的学者
在真实场景中，让具身智能体利用人类语言获取显性或隐性知识以学习任务是理想目标。尽管已有进展，以往方法多采用简单低级指令，难以反映自然语言交流。本文研究不同类型的语言输入如何促进强化学习具身智能体的学习。具体考察语言的信息量（如对过往行为的反馈与未来引导）和多样性（语言表达的变化）对智能体学习与推理的影响。基于四个强化学习基准的实证结果表明，使用多样且信息丰富的语言反馈训练的智能体，在新任务上表现出更强的泛化能力和快速适应性。这些发现凸显了语言使用在开放世界中指导具身智能体学习的关键作用。项目主页：https://github.com/sled-group/Teachable_RL
原文摘要 · Abstract (English)
In real-world scenarios, it is desirable for embodied agents to have the ability to leverage human language to gain explicit or implicit knowledge for learning tasks. Despite recent progress, most previous approaches adopt simple low-level instructions as language inputs, which may not reflect natural human communication. It's not clear how to incorporate rich language use to facilitate task learning. To address this question, this paper studies different types of language inputs in facilitating reinforcement learning (RL) embodied agents. More specifically, we examine how different levels of language informativeness (i.e., feedback on past behaviors and future guidance) and diversity (i.e., variation of language expressions) impact agent learning and inference. Our empirical results based on four RL benchmarks demonstrate that agents trained with diverse and informative language feedback can achieve enhanced generalization and fast adaptation to new tasks. These findings highlight the pivotal role of language use in teaching embodied agents new tasks in an open world. Project website: https://github.com/sled-group/Teachable_RL
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