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

arXiv: https://arxiv.org/abs/2307.08169 · HTML: https://mlsanity.com/paper/2307.08169/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 17 references that are in the map.
- First posted on arXiv 16 Jul 2023.

## Builds on (most cited first, 7 of 7)

- [Online Bayesian Goal Inference for Boundedly-Rational Planning Agents](https://mlsanity.com/paper/2006.07532/) (2020)
- [Plannable Approximations to MDP Homomorphisms: Equivariance under Actions](https://mlsanity.com/paper/2002.11963/) (2020)
- [On the Feasibility of Learning, Rather than Assuming, Human Biases for Reward Inference](https://mlsanity.com/paper/1906.09624/) (2019)
- [Learning the Preferences of Ignorant, Inconsistent Agents](https://mlsanity.com/paper/1512.05832/) (2015)
- [Where Do You Think You're Going?: Inferring Beliefs about Dynamics from Behavior](https://mlsanity.com/paper/1805.08010/) (2018)
- [Online Abstraction with MDP Homomorphisms for Deep Learning](https://mlsanity.com/paper/1811.12929/) (2018)
- [Modeling Mobile Health Users as Reinforcement Learning Agents](https://mlsanity.com/paper/2212.00863/) (2022)

## Built on by (most cited first, 2 of 2)

- [Inverse Reinforcement Learning with Multiple Planning Horizons](https://mlsanity.com/paper/2409.18051/) (2024)
- [On the Strengths and Weaknesses of Data for Open-set Embodied Assistance](https://mlsanity.com/paper/2603.04819/) (2026)

## Abstract (arXiv)

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.