# Survival Multiarmed Bandits with Bootstrapping Methods

arXiv: https://arxiv.org/abs/2410.16486 · HTML: https://mlsanity.com/paper/2410.16486/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 14 references that are in the map.
- First posted on arXiv 21 Oct 2024.

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

- [A Survey on Practical Applications of Multi-Armed and Contextual Bandits](https://mlsanity.com/paper/1904.10040/) (2019)
- [Learning to Optimize for Reinforcement Learning](https://mlsanity.com/paper/2302.01470/) (2023)
- [The Survival Bandit Problem](https://mlsanity.com/paper/2206.03019/) (2022)
- [Simple Modification of the Upper Confidence Bound Algorithm by Generalized Weighted Averages](https://mlsanity.com/paper/2308.14350/) (2023)

## Abstract (arXiv)

The Multiarmed Bandits (MAB) problem has been extensively studied and has seen many practical applications in a variety of fields. The Survival Multiarmed Bandits (S-MAB) open problem is an extension which constrains an agent to a budget that is directly related to observed rewards. As budget depletion leads to ruin, an agent's objective is to both maximize expected cumulative rewards and minimize the probability of ruin. This paper presents a framework that addresses such a dual goal using an objective function balanced by a ruin aversion component. Action values are estimated through a novel approach which consists of bootstrapping samples from previously observed rewards. In numerical experiments, the policies we present outperform benchmarks from the literature.