This work has the potential for wide-ranging applications in human-in-the-loop (e.g. We set the radius of agents to 0.3, the radius of The dataset will be made public. The only difference of our model's architecture to theirs is that we use agent-centric representations Then, we construct an edge from the agent that corresponds to the row to the "column agent" then compare this with the ground truth graph. The smaller the circle, the further it is into the future.

This work has the potential for wide-ranging applications in human-in-the-loop (e.g. We set the radius of agents to 0.3, the radius of The dataset will be made public. The only difference of our model's architecture to theirs is that we use agent-centric representations Then, we construct an edge from the agent that corresponds to the row to the "column agent" then compare this with the ground truth graph. The smaller the circle, the further it is into the future.

Modeling multi-agent systems requires understanding how agents interact.

Modeling multi-agent systems requires understanding how agents interact. Such systems are often difficult to model because they can involve a variety of types of interactions that layer together to drive rich social behavioral dynamics. Here we introduce a method for accurately modeling multi-agent systems. We present Interaction Modeling with Multiplex Attention (IMMA), a forward prediction model that uses a multiplex latent graph to represent multiple independent types of interactions and attention to account for relations of different strengths. We also introduce Progressive Layer Training, a training strategy for this architecture. We show that our approach outperforms state-of-the-art models in trajectory forecasting and relation inference, spanning three multi-agent scenarios: social navigation, cooperative task achievement, and team sports. We further demonstrate that our approach can improve zero-shot generalization and allows us to probe how different interactions impact agent behavior.