Learning Distributed Stabilizing Controllers for Multi-Agent Systems

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