论文标题
相互作用建模，多重注意
Interaction Modeling with Multiplex Attention
论文作者
论文摘要
建模多代理系统需要了解代理的相互作用。这样的系统通常很难建模，因为它们可以涉及各种类型的相互作用，以促进丰富的社会行为动态。在这里，我们介绍了一种用于准确建模多代理系统的方法。我们介绍了使用多重注意（IMMA）的相互作用建模，这是一种前向预测模型，该模型使用多重潜在图代表多种独立类型的相互作用，并注意对不同强度的关系。我们还介绍了渐进层培训，这是该体系结构的培训策略。我们表明，我们的方法在轨迹预测和关系推理中的最新模型优于最先进的模型，涵盖了三个多代理方案：社交导航，合作任务成就和团队运动。我们进一步证明，我们的方法可以改善零拍的概括，并使我们能够探究不同的相互作用如何影响代理行为。
Modeling multi-agent systems requires understanding how agents interact. Such systems are often difficult to model because they can involve a variety of types of interactions that layer together to drive rich social behavioral dynamics. Here we introduce a method for accurately modeling multi-agent systems. We present Interaction Modeling with Multiplex Attention (IMMA), a forward prediction model that uses a multiplex latent graph to represent multiple independent types of interactions and attention to account for relations of different strengths. We also introduce Progressive Layer Training, a training strategy for this architecture. We show that our approach outperforms state-of-the-art models in trajectory forecasting and relation inference, spanning three multi-agent scenarios: social navigation, cooperative task achievement, and team sports. We further demonstrate that our approach can improve zero-shot generalization and allows us to probe how different interactions impact agent behavior.
