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.

该研究提出了一种可解释性强、基于神经网络和博弈理论的、能转化为下游决策制定的端到端可训练架构，用于预测相互作用代理的轨迹。该模型使用一个神经网络从代理的过去轨迹中提取偏好，并使用可微分的隐式层将这些偏好映射到局部纳什均衡，从而形成预测的未来轨迹的模式。实验评估表明，该方法在预测高速公路驾驶员合并轨迹和简单的决策制定任务上都取得了良好表现。