Batched Bandit Problems
ANNALS OF STATISTICS, no. 2 (2016): 660-681
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Abstract
Motivated by practical applications, chiefly clinical trials, we study the regret achievable for stochastic bandits under the constraint that the employed policy must split trials into a small number of batches. We propose a simple policy, and show that a very small number of batches gives close to minimax optimal regret bounds. As a bypr...More
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