BOIL: Learning Environment Personalized Information

arXiv cs.LG / 4/21/2026

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Key Points

The paper introduces BOIL (Blackbox Oracle Information Learning), a scalable process for learning useful insights from environment structure when information is limited in multi-agent settings.
BOIL combines the PageRank algorithm with common information maximization to extract actionable information for guiding agents’ long-term behavior.
The approach targets problems such as coverage, patrolling, and stochastic reachability by producing strategy distributions that improve performance over long time horizons.
Experiments show BOIL outperforms heuristic baselines in complex environments, indicating stronger planning/behavior learning under uncertainty.
Overall, BOIL provides a general learning mechanism for transforming environmental relationships into guidance for multi-agent strategy over time.

Abstract

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.