# BOIL: Learning Environment Personalized Information

arXiv: https://arxiv.org/abs/2604.17137 · HTML: https://mlsanity.com/paper/2604.17137/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 27 references that are in the map.
- First posted on arXiv 18 Apr 2026.

## Builds on (most cited first, 5 of 5)

- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021)
- [Ergodic Exploration of Distributed Information](https://mlsanity.com/paper/1708.09352/) (2017)
- [Learning Supervised PageRank with Gradient-Based and Gradient-Free Optimization Methods](https://mlsanity.com/paper/1603.00717/) (2016)
- [Decentralized Ergodic Control: Distribution-Driven Sensing and Exploration for Multi-Agent Systems](https://mlsanity.com/paper/1806.05220/) (2018)

## Abstract (arXiv)

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.