  We study a search and tracking (S&T) problem where a team of dynamic search
agents must collaborate to track an adversarial, evasive agent. The
heterogeneous search team may only have access to a limited number of past
adversary trajectories within a large search space. This problem is challenging
for both model-based searching and reinforcement learning (RL) methods since
the adversary exhibits reactionary and deceptive evasive behaviors in a large
space leading to sparse detections for the search agents. To address this
challenge, we propose a novel Multi-Agent RL (MARL) framework that leverages
the estimated adversary location from our learnable filtering model. We show
that our MARL architecture can outperform all baselines and achieves a 46%
increase in detection rate.
