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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Veröffentlicht in:	The Journal of artificial intelligence research Jg. 79; S. 359 - 415
Hauptverfasser:	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
Sprache:	Englisch
Veröffentlicht:	San Francisco AI Access Foundation 01.01.2024
Schlagworte:	Agents (artificial intelligence) Artificial intelligence Explainable artificial intelligence Feedback Machine learning User experience Workflow
ISSN:	1076-9757, 1076-9757, 1943-5037
Online-Zugang:	Volltext
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