BOIL: 学習環境におけるパーソナライズされた情報
原題: BOIL: Learning Environment Personalized Information
分析結果
- カテゴリ
- 教育
- 重要度
- 53
- トレンドスコア
- 12
- 要約
- 複雑な環境をナビゲートすることは、マルチエージェントシステムにとって課題であり、限られた情報からの効率的な洞察の抽出が求められます。本論文では、これに対処するための新しいアプローチを提案します。
- キーワード
arXiv:2604.17137v2 Announce Type: replace 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. arXiv:2604.17137v2 Announce Type: replace 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.