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 robust downstream decision making. It combines (1) a differentiable implicit layer that maps preferences to local Nash equilibria with (2) a learned equilibrium refinement concept and (3) a learned preference revelation net, given initial trajectories as input. This is accompanied by a new class of continuous potential games. We provide theoretical results for explicit gradients and soundness, and several measures to ensure tractability. 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.

中文翻译：

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基于博弈论和隐式层学习的多智能体轨迹模型

为了预测交互代理的轨迹，我们提出了一种端到端的可训练架构，该架构将神经网络与博弈论推理相结合，具有可解释的中间表示，并转移到稳健的下游决策。它结合了 (1) 将偏好映射到局部纳什均衡的可微隐层与 (2) 学习均衡细化概念和 (3) 学习偏好揭示网络，给定初始轨迹作为输入。这伴随着一类新的连续潜力游戏。我们提供了显式梯度和稳健性的理论结果，以及一些确保易处理性的措施。在实验中，我们在两个真实世界的数据集上评估我们的方法，其中我们预测高速公路司机合并轨迹，以及一个简单的决策转移任务。