  The need for opponent modeling and tracking arises in several real-world
scenarios, such as professional sports, video game design, and drug-trafficking
interdiction. In this work, we present Graph based Adversarial Modeling with
Mutal Information (GrAMMI) for modeling the behavior of an adversarial opponent
agent. GrAMMI is a novel graph neural network (GNN) based approach that uses
mutual information maximization as an auxiliary objective to predict the
current and future states of an adversarial opponent with partial
observability. To evaluate GrAMMI, we design two large-scale, pursuit-evasion
domains inspired by real-world scenarios, where a team of heterogeneous agents
is tasked with tracking and interdicting a single adversarial agent, and the
adversarial agent must evade detection while achieving its own objectives. With
the mutual information formulation, GrAMMI outperforms all baselines in both
domains and achieves 31.68% higher log-likelihood on average for future
adversarial state predictions across both domains.
