  Control of networked systems, comprised of interacting agents, is often
achieved through modeling the underlying interactions. Constructing accurate
models of such interactions--in the meantime--can become prohibitive in
applications. Data-driven control methods avoid such complications by directly
synthesizing a controller from the observed data. In this paper, we propose an
algorithm referred to as Data-driven Structured Policy Iteration (D2SPI), for
synthesizing an efficient feedback mechanism that respects the sparsity pattern
induced by the underlying interaction network. In particular, our algorithm
uses temporary "auxiliary" communication links in order to enable the required
information exchange on a (smaller) sub-network during the "learning phase" --
links that will be removed subsequently for the final distributed feedback
synthesis. We then proceed to show that the learned policy results in a
stabilizing structured policy for the entire network. Our analysis is then
followed by showing the stability and convergence of the proposed distributed
policies throughout the learning phase, exploiting a construct referred to as
the "Patterned monoid.'' The performance of D2SPI is then demonstrated using
representative simulation scenarios.
