Adversarial Search and Tracking with Multiagent Reinforcement Learning in Sparsely Observable Environment
This addresses the challenge of adversarial search and tracking in large, sparsely observable spaces, which is incremental as it builds on existing MARL methods with a learnable filtering model.
The paper tackles the problem of a team of search agents tracking an adversarial, evasive agent in a sparsely observable environment, achieving a 46% increase in detection rate compared to baselines.
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.