  We study the synthesis of optimal control policies for large-scale
multi-agent systems. The optimal control design induces a parsimonious control
intervention by means of l-1, sparsity-promoting control penalizations. We
study instantaneous and infinite horizon sparse optimal feedback controllers.
In order to circumvent the dimensionality issues associated to the control of
large-scale agent-based models, we follow a Boltzmann approach. We generate
(sub)optimal controls signals for the kinetic limit of the multi-agent
dynamics, by sampling of the optimal solution of the associated two-agent
dynamics. Numerical experiments assess the performance of the proposed sparse
design.
