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Batched Bandit Problems
2015
Social Science Research Network
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. Our results show that a very small number of batches gives close to minimax optimal regret bounds. As a byproduct, we derive optimal policies with low switching cost for stochastic bandits.
doi:10.2139/ssrn.2683578
fatcat:mfc2dtzeunebdb4tijcqwho7ga