Learning Decentralized Swarms Using Rotation Equivariant Graph Neural Networks

TL;DR

This paper introduces a rotation equivariant graph neural network for decentralized flocking control, significantly reducing training data and model complexity while improving generalization in autonomous agent swarms.

Contribution

The paper develops a symmetry-aware GNN controller that enforces rotation and translation invariance, enhancing flocking performance and generalization with less data and fewer parameters.

Findings

Achieves comparable flocking with 70% less training data

Uses 75% fewer trainable weights than non-symmetry GNNs

Demonstrates improved generalization over existing controllers

Abstract

The orchestration of agents to optimize a collective objective without centralized control is challenging yet crucial for applications such as controlling autonomous fleets, and surveillance and reconnaissance using sensor networks. Decentralized controller design has been inspired by self-organization found in nature, with a prominent source of inspiration being flocking; however, decentralized controllers struggle to maintain flock cohesion. The graph neural network (GNN) architecture has emerged as an indispensable machine learning tool for developing decentralized controllers capable of maintaining flock cohesion, but they fail to exploit the symmetries present in flocking dynamics, hindering their generalizability. We enforce rotation equivariance and translation invariance symmetries in decentralized flocking GNN controllers and achieve comparable flocking control with 70% less training data and 75% fewer trainable weights than existing GNN controllers without these symmetries enforced. We also show that our symmetry-aware controller generalizes better than existing GNN controllers. Code and animations are available at http://github.com/Utah-Math-Data-Science/Equivariant-Decentralized-Controllers.