  We study the problem of control policy design for decentralized
state-feedback linear quadratic control with a partially nested information
structure, when the system model is unknown. We propose a model-based learning
solution, which consists of two steps. First, we estimate the unknown system
model from a single system trajectory of finite length, using least squares
estimation. Next, based on the estimated system model, we design a control
policy that satisfies the desired information structure. We show that the
suboptimality gap between our control policy and the optimal decentralized
control policy (designed using accurate knowledge of the system model) scales
linearly with the estimation error of the system model. Using this result, we
provide an end-to-end sample complexity result for learning decentralized
controllers for a linear quadratic control problem with a partially nested
information structure.
