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.

翻译：本研究探讨了在稀疏可观测环境下的搜索与追踪（S&T）问题，其中一组动态搜索智能体需协作追踪具有对抗性且善于规避的敌方目标。异构搜索团队在大型搜索空间中仅能获取有限的过往敌方轨迹信息。由于敌方目标在大范围内会展现出反应性与欺骗性规避行为，导致搜索智能体获得稀疏检测结果，这使得基于模型的搜索及强化学习方法均面临挑战。为应对该问题，我们提出一种新型多智能体强化学习（MARL）框架，该框架通过可学习滤波模型估计敌方目标位置。实验表明，我们的MARL架构能够超越所有基线方法，检测率提升达46%。