# Using Cognitive Models to Train Warm Start Reinforcement Learning Agents for Human-Computer Interactions

arXiv: https://arxiv.org/abs/2103.06160 · HTML: https://mlsanity.com/paper/2103.06160/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 29 references that are in the map.
- First posted on arXiv 10 Mar 2021.

## Builds on (most cited first, 4 of 4)

- [Personalized HeartSteps: A Reinforcement Learning Algorithm for Optimizing Physical Activity](https://mlsanity.com/paper/1909.03539/) (2019)
- [A Reinforcement Learning System to Encourage Physical Activity in Diabetes Patients](https://mlsanity.com/paper/1605.04070/) (2016)
- [Offline Contextual Multi-armed Bandits for Mobile Health Interventions: A Case Study on Emotion Regulation](https://mlsanity.com/paper/2008.09472/) (2020)
- [Neural-encoding Human Experts' Domain Knowledge to Warm Start Reinforcement Learning](https://mlsanity.com/paper/1902.06007/) (2019)

## Built on by (most cited first, 1 of 1)

- [Reinforcement Learning Interventions on Boundedly Rational Human Agents in Frictionful Tasks](https://mlsanity.com/paper/2401.14923/) (2024)

## Abstract (arXiv)

Reinforcement learning (RL) agents in human-computer interactions applications require repeated user interactions before they can perform well. To address this "cold start" problem, we propose a novel approach of using cognitive models to pre-train RL agents before they are applied to real users. After briefly reviewing relevant cognitive models, we present our general methodological approach, followed by two case studies from our previous and ongoing projects. We hope this position paper stimulates conversations between RL, HCI, and cognitive science researchers in order to explore the full potential of the approach.