Discovering User Types: Mapping User Traits by Task-Specific Behaviors in Reinforcement Learning
Abstract
When assisting human users in reinforcement learning (RL), we can represent users as RL agents and study key parameters, called 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.
Cite
Text
Ankile et al. "Discovering User Types: Mapping User Traits by Task-Specific Behaviors in Reinforcement Learning." ICML 2023 Workshops: ToM, 2023.Markdown
[Ankile et al. "Discovering User Types: Mapping User Traits by Task-Specific Behaviors in Reinforcement Learning." ICML 2023 Workshops: ToM, 2023.](https://mlanthology.org/icmlw/2023/ankile2023icmlw-discovering-a/)BibTeX
@inproceedings{ankile2023icmlw-discovering-a,
title = {{Discovering User Types: Mapping User Traits by Task-Specific Behaviors in Reinforcement Learning}},
author = {Ankile, Lars Lien and Ham, Brian and Mao, Kevin and Shin, Eura and Swaroop, Siddharth and Doshi-Velez, Finale and Pan, Weiwei},
booktitle = {ICML 2023 Workshops: ToM},
year = {2023},
url = {https://mlanthology.org/icmlw/2023/ankile2023icmlw-discovering-a/}
}