# Sample Efficient Policy Search for Optimal Stopping Domains

arXiv: https://arxiv.org/abs/1702.06238 · HTML: https://mlsanity.com/paper/1702.06238/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2017.

## Built on by (most cited first, 4 of 4)

- [CaRT: Teaching LLM Agents to Know When They Know Enough](https://mlsanity.com/paper/2510.08517/) (2025)
- [Deep Reinforcement Learning for Optimal Stopping with Application in Financial Engineering](https://mlsanity.com/paper/2105.08877/) (2021)
- [Estimation of Optimal Dynamic Treatment Assignment Rules under Policy Constraints](https://mlsanity.com/paper/2106.05031/) (2021)
- [agent-risk: Risk Assessment for Changes to AI Agents](https://mlsanity.com/paper/2610.16280/) (2026)

## Abstract (arXiv)

Optimal stopping problems consider the question of deciding when to stop an observation-generating process in order to maximize a return. We examine the problem of simultaneously learning and planning in such domains, when data is collected directly from the environment. We propose GFSE, a simple and flexible model-free policy search method that reuses data for sample efficiency by leveraging problem structure. We bound the sample complexity of our approach to guarantee uniform convergence of policy value estimates, tightening existing PAC bounds to achieve logarithmic dependence on horizon length for our setting. We also examine the benefit of our method against prevalent model-based and model-free approaches on 3 domains taken from diverse fields.