# VAIN: Attentional Multi-agent Predictive Modeling

arXiv: https://arxiv.org/abs/1706.06122 · HTML: https://mlsanity.com/paper/1706.06122/

## Key facts

- Cited by 53 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2017.

## Built on by (most cited first, 30 of 53)

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- [CTRMs: Learning to Construct Cooperative Timed Roadmaps for Multi-agent Path Planning in Continuous Spaces](https://mlsanity.com/paper/2201.09467/) (2022)
- [FCMNet: Full Communication Memory Net for Team-Level Cooperation in Multi-Agent Systems](https://mlsanity.com/paper/2201.11994/) (2022)
- [Concentration Network for Reinforcement Learning of Large-Scale Multi-Agent Systems](https://mlsanity.com/paper/2203.06416/) (2022)
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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.