Modeling Variation in Human Feedback with User Inputs: An Exploratory Methodology

To expedite the development process of interactive reinforcement learning (IntRL) algorithms, prior work often uses perfect oracles as simulated human teachers to furnish feedback signals. These oracles typically derive from ground-truth knowledge or optimal policies, providing dense and error-free...

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Bibliographic Details

Published in:	2024 19th ACM/IEEE International Conference on Human-Robot Interaction (HRI) pp. 303 - 312
Main Authors:	Huang, Jindan, Aronson, Reuben M., Short, Elaine Schaertl
Format:	Conference Proceeding
Language:	English
Published:	ACM 11.03.2024
Subjects:	Behavioral sciences Computational modeling human behavior modeling Human computer interaction human feedback human-centered robotics Human-robot interaction interactive robot learning Refining Reinforcement learning Social computing
Online Access:	Get full text
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