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. One of the drawbacks of INs is scaling 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 multiagent predictive modeling. Our method is evaluated on tasks from challenging multi-agent prediction domains: chess and soccer, and outperforms competing multi-agent approaches.
|Original language||American English|
|Number of pages||11|
|Journal||Advances in Neural Information Processing Systems|
|State||Published - 2017|
|Event||31st Annual Conference on Neural Information Processing Systems, NIPS 2017 - Long Beach, United States|
Duration: 4 Dec 2017 → 9 Dec 2017
Bibliographical notePublisher Copyright:
© 2017 Neural information processing systems foundation. All rights reserved.