The study explores the effectiveness of greedy algorithms compared to UCB in multi-armed bandit scenarios. It highlights the importance of subsampling and free exploration, showing that SS-Greedy performs exceptionally well. The research indicates that Greedy benefits from a large number of arms, leading to low regret rates. Simulations with real data support these findings, showcasing the superior performance of SS-Greedy over other algorithms. The study also delves into contextual settings, demonstrating the robustness of insights across different scenarios.
Introduction
Lower Bound and an Optimal Algorithm
A Greedy Algorithm
Simulations
Generalizations
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by Mohsen Bayat... klokken arxiv.org 03-21-2024
https://arxiv.org/pdf/2002.10121.pdfDypere Spørsmål