APReL: A Library for Active Preference-based Reward Learning Algorithms
Erdem Bıyık, Aditi Talati, Dorsa Sadigh
Abstract
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.
Topics & Concepts
PreferenceComputer sciencePreference learningActive learning (machine learning)Artificial intelligenceRobotMachine learningAlgorithmHuman–computer interactionMathematicsStatisticsMachine Learning and AlgorithmsReinforcement Learning in RoboticsFormal Methods in Verification