# Forced Exploration in Bandit Problems

arXiv: https://arxiv.org/abs/2312.07285 · HTML: https://mlsanity.com/paper/2312.07285/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 30 references that are in the map.
- First posted on arXiv 12 Dec 2023.

## Builds on (most cited first, 12 of 12)

- [Introduction to Multi-Armed Bandits](https://mlsanity.com/paper/1904.07272/) (2019)
- [A New Algorithm for Non-stationary Contextual Bandits: Efficient, Optimal, and Parameter-free](https://mlsanity.com/paper/1902.00980/) (2019)
- [Nearly Optimal Adaptive Procedure with Change Detection for Piecewise-Stationary Bandit](https://mlsanity.com/paper/1802.03692/) (2018)
- [A Change-Detection based Framework for Piecewise-stationary Multi-Armed Bandit Problem](https://mlsanity.com/paper/1711.03539/) (2017)
- [On Explore-Then-Commit Strategies](https://mlsanity.com/paper/1605.08988/) (2016)
- [Unimodal Bandits: Regret Lower Bounds and Optimal Algorithms](https://mlsanity.com/paper/1405.5096/) (2014)
- [Taming Non-stationary Bandits: A Bayesian Approach](https://mlsanity.com/paper/1707.09727/) (2017)
- [Learning Contextual Bandits in a Non-stationary Environment](https://mlsanity.com/paper/1805.09365/) (2018)
- [Perturbed-History Exploration in Stochastic Multi-Armed Bandits](https://mlsanity.com/paper/1902.10089/) (2019)
- [Residual Bootstrap Exploration for Bandit Algorithms](https://mlsanity.com/paper/2002.08436/) (2020)
- [A Scale Free Algorithm for Stochastic Bandits with Bounded Kurtosis](https://mlsanity.com/paper/1703.08937/) (2017)
- [On Limited-Memory Subsampling Strategies for Bandits](https://mlsanity.com/paper/2106.10935/) (2021)

## Built on by (most cited first, 1 of 1)

- [Reliable Scale-Free Bandits via Empirical Likelihood](https://mlsanity.com/paper/2610.18055/) (2026)

## Abstract (arXiv)

The multi-armed bandit(MAB) is a classical sequential decision problem. Most work requires assumptions about the reward distribution (e.g., bounded), while practitioners may have difficulty obtaining information about these distributions to design models for their problems, especially in non-stationary MAB problems. This paper aims to design a multi-armed bandit algorithm that can be implemented without using information about the reward distribution while still achieving substantial regret upper bounds. To this end, we propose a novel algorithm alternating between greedy rule and forced exploration. Our method can be applied to Gaussian, Bernoulli and other subgaussian distributions, and its implementation does not require additional information. We employ a unified analysis method for different forced exploration strategies and provide problem-dependent regret upper bounds for stationary and piecewise-stationary settings. Furthermore, we compare our algorithm with popular bandit algorithms on different reward distributions.