# Robust Q-learning

arXiv: https://arxiv.org/abs/2003.12427 · HTML: https://mlsanity.com/paper/2003.12427/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2020.

## Built on by (most cited first, 5 of 5)

- [Transfer Q-learning](https://mlsanity.com/paper/2202.04709/) (2022)
- [Efficient Learning of Optimal Individualized Treatment Rules for Heteroscedastic or Misspecified Treatment-Free Effect Models](https://mlsanity.com/paper/2109.02570/) (2021)
- [Relative Sparsity for Medical Decision Problems](https://mlsanity.com/paper/2211.16566/) (2022)
- [A Penalized Shared-parameter Algorithm for Estimating Optimal Dynamic Treatment Regimens](https://mlsanity.com/paper/2107.07875/) (2021)
- [AI in Pharma for Personalized Sequential Decision-Making: Methods, Applications and Opportunities](https://mlsanity.com/paper/2311.18725/) (2023)

## Abstract (arXiv)

Q-learning is a regression-based approach that is widely used to formalize the development of an optimal dynamic treatment strategy. Finite dimensional working models are typically used to estimate certain nuisance parameters, and misspecification of these working models can result in residual confounding and/or efficiency loss. We propose a robust Q-learning approach which allows estimating such nuisance parameters using data-adaptive techniques. We study the asymptotic behavior of our estimators and provide simulation studies that highlight the need for and usefulness of the proposed method in practice. We use the data from the "Extending Treatment Effectiveness of Naltrexone" multi-stage randomized trial to illustrate our proposed methods.