BOIL让多智能体系统从环境结构中自动提取关键信息，提升长期决策能力。
BOIL: Learning Environment Personalized Information
- 基于网页排名与信息最大化，从环境结构中挖掘隐藏规律
- 在覆盖、巡逻等任务中，策略性能显著优于传统启发式方法
- 适合复杂环境下的长期规划问题，如智能巡检、路径优化
多智能体系统在复杂环境中导航时面临挑战，需从有限信息中高效提取洞察。本文提出黑箱奥托信息学习（BOIL）机制，一种可扩展的环境结构信息提取方法。该方法结合网页排名算法与共同信息最大化，有效引导智能体在覆盖、巡逻及随机可达性等任务中的长期行为。实验表明，BOIL生成的策略分布可在长时间跨度内显著提升性能，优于各类启发式方法，在复杂场景中表现突出。
原文摘要 · Abstract (English)
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.
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