Year: 2025
Type: preprint
Abstract: We consider non-stationary multi-arm bandit (MAB) where the expected reward of each action follows a linear function of the number of times we executed the action. Our main result is a tight regret bound of $\tildeΘ(T^{4/5}K^{3/5})$, by providing both upper and lower bounds. We extend our results to derive instance dependent regret bounds, which depend on the unknown parametrization of the linear drift of the rewards.
Source: arXiv (Cornell University)
Authors: Amichay, Omer, Mansour, Yishay
Institutions: Tel Aviv University, Google (United States), Google (Israel)
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W4406231611