WebWe present a complete solution for the multi-armed problem in this setting. That is, for every metric space (L, X) we define an isometry invariant MaxMinCOV(X) which bounds from below the performance of Lipschitz MAB algorithms for X, and we present an algorithm which comes arbitrarily close to meeting this bound. Web19 feb. 2008 · We consider the framework of stochastic multi-armed bandit problems and study the possibilities and limitations of forecasters that perform an on-line exploration of the arms. These forecasters are assessed in terms of their simple regret, a regret notion that captures the fact that exploration is only constrained by the number of available rounds …

arXiv:2006.12367v3 [cs.LG] 12 Aug 2024

WebMulti–Armed Bandits (MABs) have been widely considered in the last decade to model settings in which an agent wants to learn the action providing the highest expected … Webarmed bandit problem in which the strategies form a metric space, and the payoff function satisfies a Lipschitz condition with respect to the metric. We refer to this problem as the … flight 0022

[0809.4882] Multi-Armed Bandits in Metric Spaces - arXiv.org

Web12 dec. 2011 · The multi-armed bandit (MAB) setting is a useful abstraction of many online learning tasks which focuses on the trade-off between exploration and exploitation. In this setting, an online algorithm has a fixed set of alternatives ("arms"), and in each round it selects one arm and then observes the corresponding reward. Web15 oct. 2024 · Multi-armed bandits in metric spaces Robert D. Kleinberg, Aleksandrs Slivkins, E. Upfal Computer Science, Mathematics STOC 2008 TLDR This work defines an isometry invariant Max Min COV (X) which bounds from below the performance of Lipschitz MAB algorithms for X, and presents an algorithm which comes arbitrarily close to … WebMulti-Armed Bandits in Metric Spaces Robert Kleinbergy Aleksandrs Slivkinsz Eli Upfalx March 2008 Abstract In a multi-armed bandit problem, an online algorithm chooses … chemetry