  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.
