  In decision making problems for continuous state and action spaces, linear
dynamical models are widely employed. Specifically, policies for stochastic
linear systems subject to quadratic cost functions capture a large number of
applications in reinforcement learning. Selected randomized policies have been
studied in the literature recently that address the trade-off between
identification and control. However, little is known about policies based on
bootstrapping observed states and actions. In this work, we show that
bootstrap-based policies achieve a square root scaling of regret with respect
to time. We also obtain results on the accuracy of learning the model's
dynamics. Corroborative numerical analysis that illustrates the technical
results is also provided.
