  Control of large-scale networked systems often necessitates the availability
of complex models for the interactions amongst the agents. While building
accurate models of these interactions could become prohibitive in many
applications, data-driven control methods can circumvent model complexities by
directly synthesizing a controller from the observed data. In this paper, we
propose the Data-Driven Distributed Policy Iteration (D3PI) algorithm to design
a feedback mechanism for a potentially large system that enjoys an underlying
graph structure characterizing communications among the agents. Rather than
having access to system parameters, our algorithm requires temporary
"auxiliary" links to boost information exchange of a small portion of the graph
during the learning phase. Therein, the costs are partitioned for learning and
non-learning agents in order to ensure consistent control of the entire
network. After the termination of the learning process, a distributed policy is
proposed for the entire networked system by leveraging estimated components
obtained in the learning phase. We provide extensive stability and convergence
guarantees of the proposed distributed controller throughout the learning phase
by exploiting the structure of the system parameters that occur due to the
graph topology and existence of the temporary links. The practicality of our
method is then illustrated with a simulation.
