# MagNet: Discovering Multi-agent Interaction Dynamics using Neural Network

arXiv: https://arxiv.org/abs/2001.09001 · HTML: https://mlsanity.com/paper/2001.09001/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2020.

## Built on by (most cited first, 1 of 1)

- [Forecasting Local Behavior of Self-organizing Many-agent System without Reconstruction](https://mlsanity.com/paper/2210.17289/) (2022)

## Abstract (arXiv)

We present the MagNet, a neural network-based multi-agent interaction model to discover the governing dynamics and predict evolution of a complex multi-agent system from observations. We formulate a multi-agent system as a coupled non-linear network with a generic ordinary differential equation (ODE) based state evolution, and develop a neural network-based realization of its time-discretized model. MagNet is trained to discover the core dynamics of a multi-agent system from observations, and tuned on-line to learn agent-specific parameters of the dynamics to ensure accurate prediction even when physical or relational attributes of agents, or number of agents change. We evaluate MagNet on a point-mass system in two-dimensional space, Kuramoto phase synchronization dynamics and predator-swarm interaction dynamics demonstrating orders of magnitude improvement in prediction accuracy over traditional deep learning models.