Adversarial Search and Tracking with Multiagent Reinforcement Learning in Sparsely Observable Environment
Abstract
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.
Cite
@article{arxiv.2306.11301,
title = {Adversarial Search and Tracking with Multiagent Reinforcement Learning in Sparsely Observable Environment},
author = {Zixuan Wu and Sean Ye and Manisha Natarajan and Letian Chen and Rohan Paleja and Matthew C. Gombolay},
journal= {arXiv preprint arXiv:2306.11301},
year = {2023}
}
Comments
Accepted by IEEE International Symposium on Multi-Robot & Multi-Agent Systems (MRS) 2023