Discovering User Types: Mapping User Traits by Task-Specific Behaviors in Reinforcement Learning
This work addresses the challenge of rapid personalization for human users in RL, though it appears incremental as it builds on existing trait-behavior mapping concepts.
The paper tackles the problem of personalizing interventions in reinforcement learning by mapping user traits to behaviors, showing that different environments can share the same user types and enabling faster intervention design through equivalence classes.
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. We study the relationship between user behaviors (policy classes) and user traits. Given an environment, we introduce an intuitive tool for studying the breakdown of "user types": broad sets of traits that result in the same behavior. We show that seemingly different real-world environments admit the same set of user types and formalize this observation as an equivalence relation defined on environments. By transferring intervention design between environments within the same equivalence class, we can help rapidly personalize interventions.