面向多人建模的带部分观测与力学约束的去中心化策略学习
Decentralized policy learning with partial observation and mechanical constraints for multiperson modeling
AI总结：
本文提出一种去中心化多智能体模仿学习方法，结合部分观测、力学约束和分层变分循环神经网络，在篮球和足球数据上实现了更符合生物合理性的多人行为建模与轨迹预测。
摘要：
从现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面临的挑战。生物智能体各自独立地具有有限的观测能力和力学约束；然而，大多数传统数据驱动模型忽略了这些假设，导致在行为分析中缺乏生物合理性和模型可解释性。在此，我们提出以去中心化方式结合部分观测与力学约束的序列生成模型，该模型能够对智能体的认知和身体动力学进行建模，并预测具有生物合理性的行为。我们将该问题形式化为一个去中心化多智能体模仿学习问题，利用二值部分观测和基于分层变分循环神经网络的去中心化策略模型，并加入物理和生物力学惩罚项。使用真实的篮球和足球数据集，我们从约束违反、长期轨迹预测和部分观测等方面证明了所提方法的有效性。我们的方法可用作多智能体模拟器，利用真实世界数据生成逼真的轨迹。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
