APReL：選好に基づく能動的報酬学習アルゴリズムのライブラリ
APReL: A Library for Active Preference-based Reward Learning Algorithms
人間の選好から報酬関数を学習するための能動的クエリ手法を実装したオープンソースライブラリAPReLを公開し、既存手法の実験や新規アルゴリズム開発を容易にした。
著者: Erdem Bıyık, Aditi Talati, Dorsa Sadigh
分類: cs.LG, cs.AI, cs.RO
原文アブストラクト
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.
