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VAIN: Attentional Multi-agent Predictive Modeling

arXiv 1706.06122 ↗First posted 19 Jun 2017cs.LG cs.AIcited by 51

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Abstract (arXiv)

Multi-agent predictive modeling is an essential step for understanding physical, social and team-play systems. Recently, Interaction Networks (INs) were proposed for the task of modeling multi-agent physical systems, INs scale with the number of interactions in the system (typically quadratic or higher order in the number of agents). In this paper we introduce VAIN, a novel attentional architecture for multi-agent predictive modeling that scales linearly with the number of agents. We show that VAIN is effective for multi-agent predictive modeling. Our method is evaluated on tasks from challenging multi-agent prediction domains: chess and soccer, and outperforms competing multi-agent approaches.

Key facts. Cited by 51 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Jun 2017.

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Citing papers per year: 2019: 1; 2020: 0; 2021: 18; 2022: 12; 2023: 11; 2024: 4; 2025: 4; 2026: 1.

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