  Smart Grids of collaborative netted radars accelerate kill chains through
more efficient cross-cueing over centralized command and control. In this
paper, we propose two novel reward-based learning approaches to decentralized
netted radar coordination based on black-box optimization and Reinforcement
Learning (RL). To make the RL approach tractable, we use a simplification of
the problem that we proved to be equivalent to the initial formulation. We
apply these techniques on a simulation where radars can follow multiple targets
at the same time and show they can learn implicit cooperation by comparing them
to a greedy baseline.
