MagNet: Discovering Multi-agent Interaction Dynamics using Neural Network

MagNet: Discovering Multi-agent Interaction Dynamics using Neural Network Priyabrata Saha, Arslan Ali, Burhan A. Mudassar, Y un Long and Saibal Mukhopadhyay Abstract -- We present the MagNet, a multi-agent interaction network to discover governing dynamics and predict evolution of a complex system from observations. We formulate a multi-agent system as a coupled nonlinear 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 online 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 point-mass system in two-dimensional space, Ku-ramoto phase synchronization dynamics and predator-swarm interaction dynamics demonstrating orders of magnitude improvement in prediction accuracy over traditional deep learning models. I NTRODUCTION Multi-agent systems are prevalent in both the natural world and engineered world. Engineered distributed systems of mobile robots, multiple sensors, unmanned aerial vehicles etc. often take inspiration from natural multi-agent systems like swarms, schools, flocks, and herds of social animals or birds. Understanding the behavior of such natural or engineered multi-agent systems from sensory observations is a key challenge in robotics from the design and adversarial perspective. Discovering the hidden dynamics of a multi-agent interaction from observations will enable machines to simulate and predict evolution of complex systems.