| Paper ID | SPTM-11.4 |
| Paper Title |
GRAPH NEURAL NETWORKS FOR DECENTRALIZED CONTROLLERS |
| Authors |
Fernando Gama, University of California, Berkeley, United States; Ekaterina Tolstaya, Alejandro Ribeiro, University of Pennsylvania, United States |
| Session | SPTM-11: Graphs Neural Networks |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 16:30 - 17:15 |
| Presentation Time: | Wednesday, 09 June, 16:30 - 17:15 |
| Presentation |
Poster
|
| Topic |
Signal Processing Theory and Methods: Signal Processing over Networks |
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| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Dynamical systems comprised of autonomous agents arise in many relevant problems such as multi-agent robotics, smart grids, or smart cities. Controlling these systems is of paramount importance to guarantee a successful deployment. Optimal \emph{centralized} controllers are readily available but face limitations in terms of scalability and practical implementation. Optimal \emph{decentralized} controllers, on the other hand, are difficult to find. In this paper, we propose a framework using graph neural networks (GNNs) to learn decentralized controllers from data. While GNNs are naturally distributed architectures, making them perfectly suited for the task, we adapt them to handle delayed communications as well. Furthermore, they are equivariant and stable, leading to good scalability and transferability properties. The problem of flocking is explored to illustrate the potential of GNNs in learning decentralized controllers. |
