arXiv Open Access 2021

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

Erdem Bıyık Aditi Talati Dorsa Sadigh

Lihat Sumber

Abstrak

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.

Topik & Kata Kunci

cs.LG cs.AI cs.RO

Penulis (3)

Erdem Bıyık

Aditi Talati

Dorsa Sadigh

Format Sitasi

APA MLA BibTeX

Bıyık, E., Talati, A., Sadigh, D. (2021). APReL: A Library for Active Preference-based Reward Learning Algorithms. https://arxiv.org/abs/2108.07259

Akses Cepat

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Informasi Jurnal

Tahun Terbit: 2021
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓