# Policy Learning for Individualized Treatment Regimes on Infinite Time Horizon

arXiv: https://arxiv.org/abs/2309.13458 · HTML: https://mlsanity.com/paper/2309.13458/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2023.

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

- [Reinforcement Learning with Continuous Actions Under Unmeasured Confounding](https://mlsanity.com/paper/2505.00304/) (2025)
- [Boosted Distributional Reinforcement Learning: Analysis and Healthcare Applications](https://mlsanity.com/paper/2604.04334/) (2026)

## Abstract (arXiv)

With the recent advancements of technology in facilitating real-time monitoring and data collection, "just-in-time" interventions can be delivered via mobile devices to achieve both real-time and long-term management and control. Reinforcement learning formalizes such mobile interventions as a sequence of decision rules and assigns treatment arms based on the user's status at each decision point. In practice, real applications concern a large number of decision points beyond the time horizon of the currently collected data. This usually refers to reinforcement learning in the infinite horizon setting, which becomes much more challenging. This article provides a selective overview of some statistical methodologies on this topic. We discuss their modeling framework, generalizability, and interpretability and provide some use case examples. Some future research directions are discussed in the end.