  Data-driven control benefits from rich datasets, but constructing such
datasets becomes challenging when gathering data is limited. We consider an
offline experiment design approach to gathering data where we design a control
input to collect data that will most improve the performance of a feedback
controller. We show how such a control-oriented approach can be used in a
setting with linear dynamics and quadratic objective and, through design of a
gradient estimator, solve the problem via stochastic gradient descent. We show
our formulation numerically outperforms an A- and L-optimal experiment design
approach as well as a robust dual control approach.
