Navigating complex environments poses challenges for multi-agent systems, requiring efficient extraction of insights from limited information. In this paper, we introduce the Blackbox Oracle Information Learning (BOIL) process, a scalable solution for extracting valuable insights from the environment structure. Leveraging the Pagerank algorithm and common information maximization, BOIL facilitates the extraction of information to guide long-term agent behavior applicable to problems such as coverage, patrolling, and stochastic reachability. Through experiments, we demonstrate the efficacy of BOIL in generating strategy distributions conducive to improved performance over extended time horizons, surpassing heuristic approaches in complex environments.

翻译：在复杂环境中导航对多智能体系统提出了挑战，需要从有限信息中高效提取洞察。本文提出黑盒预言机信息学习（BOIL）过程，这是一种从环境结构中提取宝贵洞察的可扩展解决方案。BOIL利用Pagerank算法和公共信息最大化，促进信息提取以指导智能体的长期行为，适用于覆盖、巡逻和随机可达性等问题。通过实验，我们证明了BOIL在生成有利于在长时间跨度内提升性能的策略分布方面的有效性，在复杂环境中超越了启发式方法。