Centrala begrepp
Greedy algorithms outperform UCB in many-armed bandit problems.
Sammanfattning
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.
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Introduction
- Investigates Bayesian k-armed bandit problem.
- Considers many-armed regime where k ≥ √T.
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Lower Bound and an Optimal Algorithm
- UCB algorithm is optimal for small k.
- SS-UCB algorithm is optimal for large k.
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A Greedy Algorithm
- Greedy algorithm performs well due to free exploration.
- SS-Greedy surpasses other algorithms in performance.
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Simulations
- Real data simulations show SS-Greedy's superiority.
- Greedy benefits from a large number of arms for low regret rates.
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Generalizations
- Results generalized for β-regular priors.
- Sequential Greedy algorithm discussed for further improvements.
Statistik
"SS-UCB achieves rate-optimality up to logarithmic factors."
"Greedy consistently chooses empirically best arm."
"SS-Greedy surpasses all other algorithms in performance."
Citat
"The greedy algorithm pulls each arm once and thereafter pulls the empirically best arm."
"Subsampling enhances the performance of all algorithms, including UCB, TS, and Greedy."