# Risk-Averse Explore-Then-Commit Algorithms for Finite-Time Bandits

## TL;DR

This paper introduces risk-averse explore-then-commit algorithms for finite-time bandit problems, focusing on selecting arms with the best risk-return trade-off rather than just the highest expected reward, with proven regret bounds and robust performance.

## Contribution

It proposes two novel risk-averse explore-then-commit algorithms that do not rely on hyper-parameters and provide finite-time regret guarantees, improving robustness over existing methods.

## Key findings

- Algorithms outperform existing risk-averse bandit algorithms in robustness.
- Finite-time regret bounds are established for the proposed methods.
- Numerical evaluations confirm practical effectiveness.

## Abstract

In this paper, we study multi-armed bandit problems in explore-then-commit setting. In our proposed explore-then-commit setting, the goal is to identify the best arm after a pure experimentation (exploration) phase and exploit it once or for a given finite number of times. We identify that although the arm with the highest expected reward is the most desirable objective for infinite exploitations, it is not necessarily the one that is most probable to have the highest reward in a single or finite-time exploitations. Alternatively, we advocate the idea of risk-aversion where the objective is to compete against the arm with the best risk-return trade-off. Then, we propose two algorithms whose objectives are to select the arm that is most probable to reward the most. Using a new notion of finite-time exploitation regret, we find an upper bound for the minimum number of experiments before commitment, to guarantee an upper bound for the regret. As compared to existing risk-averse bandit algorithms, our algorithms do not rely on hyper-parameters, resulting in a more robust behavior in practice, which is verified by the numerical evaluation.