Title: Dynamic Relational Inference in Multi-Agent Trajectories. Abstract: Unsupervised learning of interactions from multi-agent trajectories has broad applications in physics, vision, and robotics. However, existing neural relational inference works are limited to static relations. We consider a more general setting of dynamic relational inference where interactions change over time. We propose  DYnamic multi-Agent Relational Inference (DYARI) model, a deep generative model that can reason about dynamic relations. Using a simulated physics system, we study various dynamic relation scenarios, including periodic and additive dynamics. We perform a comprehensive study on the trade-off between dynamic and inference period, the impact of the training scheme, and model architecture on dynamic relational inference accuracy. We also showcase an application of our model to infer coordination and competition patterns from real-world multi-agent basketball trajectories.