For prediction of interacting agents' trajectories, we propose an end-to-end trainable architecture that hybridizes neural nets with game-theoretic reasoning, has interpretable intermediate representations, and transfers to downstream decision making. It uses a net that reveals preferences from the agents' past joint trajectory, and a differentiable implicit layer that maps these preferences to local Nash equilibria, forming the modes of the predicted future trajectory. Additionally, it learns an equilibrium refinement concept. For tractability, we introduce a new class of continuous potential games and an equilibrium-separating partition of the action space. We provide theoretical results for explicit gradients and soundness. In experiments, we evaluate our approach on two real-world data sets, where we predict highway driver merging trajectories, and on a simple decision-making transfer task.

翻译：为了预测互动剂的轨迹,我们建议了一个端到端的可训练结构,将神经网与游戏理论推理混合,具有可解释的中间表现,并转移至下游决策。它使用一个网,显示该剂过去联合轨迹的偏好,以及一个可区分的隐含层,绘制这些偏好与当地Nash 平衡的地图,形成预测未来轨迹的模式。此外,它学会了一种平衡完善概念。为了可感性,我们引入了一个新的循环潜在游戏和动作空间的平衡分隔类别。我们为明确的梯度和稳健性提供了理论结果。在实验中,我们评估了我们对两个真实世界数据集的处理方法,我们预测高速公路驱动器合并轨迹,以及简单的决策转移任务。