Human-in-the-Loop Reinforcement Learning: A Survey and Position on Requirements, Challenges, and Opportunities

Artificial intelligence (AI) and especially reinforcement learning (RL) have the potential to enable agents to learn and perform tasks autonomously with superhuman performance. However, we consider RL as fundamentally a Human-in-the-Loop (HITL) paradigm, even when an agent eventually performs its ta...

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Bibliographic Details

Published in:	The Journal of artificial intelligence research Vol. 79; pp. 359 - 415
Main Authors:	Retzlaff, Carl Orge, Das, Srijita, Wayllace, Christabel, Mousavi, Payam, Afshari, Mohammad, Yang, Tianpei, Saranti, Anna, Angerschmid, Alessa, Taylor, Matthew E., Holzinger, Andreas
Format:	Journal Article
Language:	English
Published:	San Francisco AI Access Foundation 01.01.2024
Subjects:	Agents (artificial intelligence) Artificial intelligence Explainable artificial intelligence Feedback Machine learning User experience Workflow
ISSN:	1076-9757, 1076-9757, 1943-5037
Online Access:	Get full text
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