  Multi-agent imitation learning aims to train multiple agents to perform tasks
from demonstrations by learning a mapping between observations and actions,
which is essential for understanding physical, social, and team-play systems.
However, most existing works on modeling multi-agent interactions typically
assume that agents make independent decisions based on their observations,
ignoring the complex dependence among agents. In this paper, we propose to use
copula, a powerful statistical tool for capturing dependence among random
variables, to explicitly model the correlation and coordination in multi-agent
systems. Our proposed model is able to separately learn marginals that capture
the local behavioral patterns of each individual agent, as well as a copula
function that solely and fully captures the dependence structure among agents.
Extensive experiments on synthetic and real-world datasets show that our model
outperforms state-of-the-art baselines across various scenarios in the action
prediction task, and is able to generate new trajectories close to expert
demonstrations.
