Extracting the rules of real-world multi-agent behaviors is a current challenge in various scientific and engineering fields. Biological agents independently have limited observation and mechanical constraints; however, most of the conventional data-driven models ignore such assumptions, resulting in lack of biological plausibility and model interpretability for behavioral analyses. Here we propose sequential generative models with partial observation and mechanical constraints in a decentralized manner, which can model agents' cognition and body dynamics, and predict biologically plausible behaviors. We formulate this as a decentralized multi-agent imitation-learning problem, leveraging binary partial observation and decentralized policy models based on hierarchical variational recurrent neural networks with physical and biomechanical penalties. Using real-world basketball and soccer datasets, we show the effectiveness of our method in terms of the constraint violations, long-term trajectory prediction, and partial observation. Our approach can be used as a multi-agent simulator to generate realistic trajectories using real-world data.

翻译：摘要：从真实世界多智能体行为中提取规则是当前多个科学与工程领域的挑战。生物个体天然具有观测受限和机械约束的特点，然而大多数传统数据驱动模型忽略了这些假设，导致行为分析缺乏生物学合理性与模型可解释性。本文提出了一种在去中心化框架下融合局部观测与机械约束的序列生成模型，该模型能够刻画智能体的认知与身体动力学特征，并预测具有生物学合理性的行为。我们将该问题形式化为去中心化多智能体模仿学习问题，利用二元局部观测机制，并基于融合物理与生物力学惩罚项的分层变分循环神经网络构建去中心化策略模型。通过真实篮球与足球数据集验证，我们的方法在约束违反程度、长期轨迹预测及局部观测处理方面均展现出有效性。该方法可作为多智能体仿真器，利用真实数据生成逼真的运动轨迹。