Batched Bandit Problems

author: Philippe Rigollet, Department of Mathematics, Massachusetts Institute of Technology, MIT
published: Aug. 20, 2015,   recorded: July 2015,   views: 1707


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Motivated by practical applications, chiefly clinical trials, we study the regret achievable for stochastic multi-armed 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 already close to minimax optimal regret bounds and we also evaluate the number of trials in each batch. As a byproduct, we derive optimal policies with low switching cost for stochastic bandits.

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