Learning Distributed Stabilizing Controllers for Multi-Agent Systems
Abstract
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.
Cite
@article{arxiv.2103.04480,
title = {Learning Distributed Stabilizing Controllers for Multi-Agent Systems},
author = {Gangshan Jing and He Bai and Jemin George and Aranya Chakrabortty and Piyush K. Sharma},
journal= {arXiv preprint arXiv:2103.04480},
year = {2021}
}
Comments
This paper propose model-free RL algorithms for deriving stabilizing gains of continuous-time multi-agent systems