Adversarial Search and Tracking with Multiagent Reinforcement Learning in Sparsely Observable Environment

Zixuan Wu, Sean Ye, Manisha Natarajan, Letian Chen, Rohan Paleja +1 more
6/20/2023
cs.LGcs.AIcs.RO

No official code found. Best implementation: CORE-Robotics-Lab/Opponent-Modeling (40 stars, MIT, last commit 2025-01-03).

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.

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Code Implementations(1)

CORE-Robotics-Lab/Opponent-ModelingCommunity100%
MIT
406Python, Jupyter NotebookMar 2, 20231 years agoMIT

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Cite this paper

@article{wu2023adversarial,
  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},
  year   = {2023},
  eprint = {2306.11301},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2306.11301v2}
}

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