  We address the problem of model-free distributed stabilization of
heterogeneous multi-agent systems using reinforcement learning (RL). Two
algorithms are developed. The first algorithm solves a centralized linear
quadratic regulator (LQR) problem without knowing any initial stabilizing gain
in advance. The second algorithm builds upon the results of the first
algorithm, and extends it to distributed stabilization of multi-agent systems
with predefined interaction graphs. Rigorous proofs are provided to show that
the proposed algorithms achieve guaranteed convergence if specific conditions
hold. A simulation example is presented to demonstrate the theoretical results.
