Active Inverse Reward Design

Designers of AI agents often iterate on the reward function in a trial-and-error process until they get the desired behavior, but this only guarantees good behavior in the training environment. We propose structuring this process as a series of queries asking the user to compare between different re...

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Bibliographische Detailangaben

Hauptverfasser:	Mindermann, Sören, Shah, Rohin, Gleave, Adam, Hadfield-Menell, Dylan
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Artificial Intelligence Computer Science - Learning Statistics - Machine Learning
Online-Zugang:	Volltext bestellen
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