Decentralized Control with Graph Neural Networks

• URL: http://arxiv.org/abs/2012.14906v1
• Date: Tue, 29 Dec 2020 18:59:14 GMT
• Title: Decentralized Control with Graph Neural Networks
• Authors: Fernando Gama, Qingbiao Li, Ekaterina Tolstaya, Amanda Prorok, Alejandro Ribeiro
• Abstract summary: We propose a novel framework using graph neural networks (GNNs) to learn decentralized controllers. GNNs are well-suited for the task since they are naturally distributed architectures and exhibit good scalability and transferability properties. The problems of flocking and multi-agent path planning are explored to illustrate the potential of GNNs in learning decentralized controllers.
• Score: 147.84766857793247
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dynamical systems consisting of a set of autonomous agents face the challenge of having to accomplish a global task, relying only on local information. While centralized controllers are readily available, they face limitations in terms of scalability and implementation, as they do not respect the distributed information structure imposed by the network system of agents. Given the difficulties in finding optimal decentralized controllers, we propose a novel framework using graph neural networks (GNNs) to learn these controllers. GNNs are well-suited for the task since they are naturally distributed architectures and exhibit good scalability and transferability properties. The problems of flocking and multi-agent path planning are explored to illustrate the potential of GNNs in learning decentralized controllers.

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