most citedTowards Optimizing Human-Centric Objectives in AI-Assisted Decision-Making With Offline Reinforcement Learning

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cs.LG2023

Discovering User Types: Mapping User Traits by Task-Specific Behaviors in Reinforcement Learning

L. L. Ankile, B. S. Ham, K. Mao +4

When assisting human users in reinforcement learning (RL), we can represent users as RL agents and study key parameters, called \emph{user traits}, to inform intervention design. W…