疎な観測環境におけるマルチエージェント強化学習による敵対的探索と追跡
Adversarial Search and Tracking with Multiagent Reinforcement Learning in Sparsely Observable Environment
広大な空間で逃げ回る敵を追跡するため、学習可能なフィルタリングモデルで敵位置を推定し、マルチエージェント強化学習で探索チームを協調させる手法を提案した。検出率が46%向上した。
著者: Zixuan Wu, Sean Ye, Manisha Natarajan, Letian Chen, Rohan Paleja, Matthew C. Gombolay
分類: cs.LG, cs.AI, cs.RO
原文アブストラクト
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.
