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In the stochastic linear contextual bandit setting there exist severalminimax procedures for exploration with policies that are reactive to the databeing acquired. In practice, there can be a significant engineering overhead todeploy these algorithms, especially when the dataset is collected in adistributed fashion or when a human in the loop is needed to implement adifferent policy. Exploring with a single non-reactive policy is beneficial insuch cases. Assuming some batch contexts are available, we design a singlestochastic policy to collect a good dataset from which a near-optimal policycan be extracted. We present a theoretical analysis as well as numericalexperiments on both synthetic and real-world datasets.

Design of Experiments for Stochastic Contextual Linear Bandits