# Thompson Sampling on Asymmetric α-Stable Bandits

arXiv: https://arxiv.org/abs/2203.10214 · HTML: https://mlsanity.com/paper/2203.10214/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 32 references that are in the map.
- First posted on arXiv 19 Mar 2022.

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

- [Learning to Optimize](https://mlsanity.com/paper/1606.01885/) (2016)
- [Thompson Sampling for 1-Dimensional Exponential Family Bandits](https://mlsanity.com/paper/1307.3400/) (2013)
- [Optimal Algorithms for Stochastic Multi-Armed Bandits with Heavy Tailed Rewards](https://mlsanity.com/paper/2010.12866/) (2020)
- [Recommendation System-based Upper Confidence Bound for Online Advertising](https://mlsanity.com/paper/1909.04190/) (2019)

## Abstract (arXiv)

In algorithm optimization in reinforcement learning, how to deal with the exploration-exploitation dilemma is particularly important. Multi-armed bandit problem can optimize the proposed solutions by changing the reward distribution to realize the dynamic balance between exploration and exploitation. Thompson Sampling is a common method for solving multi-armed bandit problem and has been used to explore data that conform to various laws. In this paper, we consider the Thompson Sampling approach for multi-armed bandit problem, in which rewards conform to unknown asymmetric α-stable distributions and explore their applications in modelling financial and wireless data.