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BATCHED BANDIT PROBLEMS
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DOI：10.1214/15-AOS1381.png)
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Abstract
En 中文
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 byproduct, we derive optimal policies with low switching cost for stochastic bandits.
Keywords:
Multi-armed bandit problems
regret bounds
batches
multi-phase allocation
grouped clinical trials
sample size determination
switching cost
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