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APReL: A Library for Active Preference-based Reward Learning Algorithms

08/16/2021

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by Erdem Bıyık, et al.

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Reward learning is a fundamental problem in robotics 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.

Erdem Bıyık
27 publications
Aditi Talati
4 publications
Dorsa Sadigh
102 publications

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