部分可观测环境下的规避者无关型团队追击策略
Evader-Agnostic Team-Based Pursuit Strategies in Partially-Observable Environments
AI总结：
针对两名无人机在城市部分可观测环境中追击未知规避者的问题，提出两阶段神经符号算法，通过离线深度强化学习训练k级理性策略、在线分类匹配对手并响应，提升了对随机规避者的平均追击性能。
摘要：
我们考虑两名无人机（UAV）组成的团队在城市环境中追击一名规避者无人机的场景。每个智能体对环境的视野有限，建筑物会遮挡其视场。此外，追击团队对规避者的初始位置、最终位置及行为均不知情。因此，团队需通过搜索环境收集信息，再追踪并最终拦截规避者。为解决这一多智能体、部分可观测的追逃博弈问题，我们开发了一种以有限理性原则为核心的两阶段神经符号算法。首先，我们设计离线方法，利用深度强化学习逐步训练追击团队针对虚拟规避者的对抗策略，为每个智能体创建k级理性以准备在线阶段。然后，我们采用在线分类算法，从迭代训练的策略智能体集中确定当前对手的“最佳猜测”，并应用最佳玩家响应。使用该方案，我们在面对环境中的随机规避者时提升了平均性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
