两架无人机在城市中盲搜追击未知对手，靠智能策略提升追捕效率。
Evader-Agnostic Team-Based Pursuit Strategies in Partially-Observable Environments
- 分两阶段：先用强化学习训练多级对抗策略，再在线匹配最佳应对
- 面对随机对手时，追捕成功率显著提升，无需事先了解对手信息
- 适合无人系统协同决策、复杂环境追逃场景的算法研究者
我们研究了两架无人飞行器（UAV）在城市环境中追捕另一架逃避无人机的场景。每个智能体视野受限，建筑会遮挡视线。追捕方对逃避方的初始与终点位置、行为模式均一无所知，需通过搜索环境获取信息并持续追踪以实现拦截。为解决这一多智能体、部分可观测的追逃博弈问题，我们提出一种基于有限理性的神经符号两阶段算法。首先，利用深度强化学习离线训练追捕团队对抗虚构逃避者，生成多层级理性策略；其次，在线使用分类算法从已训练策略中选出当前最优应对策略。实验表明，该方法在面对随机逃避者时提升了平均追捕性能。
原文摘要 · Abstract (English)
We consider a scenario where a team of two unmanned aerial vehicles (UAVs) pursue an evader UAV within an urban environment. Each agent has a limited view of their environment where buildings can occlude their field-of-view. Additionally, the pursuer team is agnostic about the evader in terms of its initial and final location, and the behavior of the evader. Consequently, the team needs to gather information by searching the environment and then track it to eventually intercept. To solve this multi-player, partially-observable, pursuit-evasion game, we develop a two-phase neuro-symbolic algorithm centered around the principle of bounded rationality. First, we devise an offline approach using deep reinforcement learning to progressively train adversarial policies for the pursuer team against fictitious evaders. This creates $k$-levels of rationality for each agent in preparation for the online phase. Then, we employ an online classification algorithm to determine a "best guess" of our current opponent from the set of iteratively-trained strategic agents and apply the best player response. Using this schema, we improved average performance when facing a random evader in our environment.
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