Softmax gradient policy for variance minimization and risk-averse multi armed bandits
Algorithms for the Multi-Armed Bandit (MAB) problem play a central role in sequential decision-making and have been extensively explored both theoretically and numerically. While most classical approaches aim to identify the arm with the highest expected reward, we focus on a risk-aware setting where the goal is to select the arm with the lowest variance, favoring stability over potentially high but uncertain returns. To model the decision process, we consider a softmax parameterization of the policy; we propose a new algorithm to select the minimal variance (or minimal risk) arm and prove its convergence under natural conditions. The algorithm constructs an unbiased estimate of the objec...
| Authors | Gabriel Turinici |
|---|---|
| Primary category | cs.LG |
| Published | 31 March 2026 |
| Last updated at source | 23 September 2026 |
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