APReL：一个基于主动偏好的奖励学习算法库
APReL: A Library for Active Preference-based Reward Learning Algorithms
摘要：
奖励学习是人机交互中的一个基本问题，让机器人按照人类用户的需求进行操作。许多基于偏好的学习算法和主动查询技术被提出来作为这个问题的解决方案。在本文中，我们提出了APReL，一个基于主动偏好的奖励学习算法库，它使研究人员和从业者能够实验现有的技术，并很容易地开发自己的算法的各个模块的问题。APReL可在https://github.com/Stanford-ILIAD/APReL上获得。
英文摘要：
Reward learning is a fundamental problem in human-robot interaction to have robots that operate in alignment with what their human user wants. Many preference-based learning algorithms and active querying techniques have been proposed as a solution to this problem. In this paper, we present APReL, a library for active preference-based reward learning algorithms, which enable researchers and practitioners to experiment with the existing techniques and easily develop their own algorithms for various modules of the problem. APReL is available at https://github.com/Stanford-ILIAD/APReL.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
