BOIL: Learning Environment Personalized Information
Keywords: learning on graphs, Information Theory, Pagerank, applications to robotics, probabilistic methods
TL;DR: Leveraging pagerank to learn agent strategies over directed graphs
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.
Primary Area: learning on graphs and other geometries & topologies
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Submission Number: 3851
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