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

no code implementations • 16 Jul 2023 • L. L. Ankile, B. S. Ham, K. Mao, E. Shin, S. Swaroop, F. Doshi-Velez, W. Pan

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.

reinforcement-learning Reinforcement Learning (RL)