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Reward learning is a fundamental problem in human-robot interaction to haverobots that operate in alignment with what their human user wants. Manypreference-based learning algorithms and active querying techniques have beenproposed as a solution to this problem. In this paper, we present APReL, alibrary for active preference-based reward learning algorithms, which enableresearchers and practitioners to experiment with the existing techniques andeasily develop their own algorithms for various modules of the problem. APReLis available at https://github.com/Stanford-ILIAD/APReL.

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