  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.
