APReL: A Library for Active Preference-based Reward Learning Algorithms
This is an incremental contribution that provides a practical tool for researchers and practitioners in robotics and AI to facilitate experimentation and development in reward learning.
The authors tackled the problem of reward learning in human-robot interaction by introducing APReL, a library for active preference-based reward learning algorithms, which provides tools for experimenting with existing techniques and developing new algorithms.
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.