论文标题
人类不是鲍尔茨曼分布：在增强学习中为人类反馈和互动建模的挑战和机会
Humans are not Boltzmann Distributions: Challenges and Opportunities for Modelling Human Feedback and Interaction in Reinforcement Learning
论文作者
论文摘要
加强学习（RL）通常假设访问明确指定的奖励功能，许多实际应用无法提供。取而代之的是，最近，更多的工作探索了从与人互动中学习该怎么做的事情。到目前为止，这些方法中的大多数方法都将人类模仿为（卑鄙的）理性，尤其是提供无偏见的反馈。我们认为这些模型过于简单，RL研究人员需要开发更现实的人类模型来设计和评估其算法。特别是，我们认为人类模型必须是个人，上下文和动态的。本文呼吁从不同学科的研究中进行研究，以解决有关人类如何向AI提供反馈以及我们如何构建更强大的人类RL系统的关键问题。
Reinforcement learning (RL) commonly assumes access to well-specified reward functions, which many practical applications do not provide. Instead, recently, more work has explored learning what to do from interacting with humans. So far, most of these approaches model humans as being (nosily) rational and, in particular, giving unbiased feedback. We argue that these models are too simplistic and that RL researchers need to develop more realistic human models to design and evaluate their algorithms. In particular, we argue that human models have to be personal, contextual, and dynamic. This paper calls for research from different disciplines to address key questions about how humans provide feedback to AIs and how we can build more robust human-in-the-loop RL systems.
