Batched Multi-Armed Bandits with Optimal Regret
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We present a simple and efficient algorithm for the batched stochastic multi-armed bandit problem. We prove a bound for its expected regret that improves over the best-known regret bound, for any number of batches. In particular, our algorithm achieves the optimal expected regret by using only a logarithmic number of batches.
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