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