We study MAB problems where the rewards are multivariate Gaussian, to account for data-driven estimation errors. We employ a percentile optimization approach, wherein the goal is to find an arm-pulling policy that maximizes the sum of percentiles of expected total discounted rewards earned from individual arms.
Jul 19, 2024 · We employ a percentile optimization approach, wherein the goal is to find an arm-pulling policy that maximizes the sum of percentiles of ...
Jul 19, 2024 · We employ a percentile optimization approach, wherein the goal is to find an arm-pulling policy that maximizes the sum of percentiles of ...
Oct 22, 2024 · It allows to model dependence between probability transitions across different states and it is significantly less conservative than prior ...
We employ a percentile optimization approach, wherein the goal is to find an arm-pulling policy that maximizes the sum of percentiles of expected total ...
Discover this 2024 paper in Annals of Operations Research by Ghatrani, Zahra; and, Ghate, Archis focusing on: MULTI-armed bandit problem (Probability ...
Cases with multiple servers may interfere with some of our main analytical results, notably the relation to multi-armed bandit problems and the optimality ...
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Jun 1, 2012 · The point of multi-armed bandit situations is that there is a trade-off to be made between gaining new knowledge and exploiting existing knowledge.
Missing: Percentile | Show results with:Percentile
The multi-armed bandit problem [1, 2] is a model of the process of decision making in situations with uncertainty. Players are motivated to maximize the sum of ...
We investigate the impact on performance of this approach to deal with missing data for several bandit algorithms through an extensive simulation study