论文标题
磁铁：使用神经网络发现多代理相互作用动力学
MagNet: Discovering Multi-agent Interaction Dynamics using Neural Network
论文作者
论文摘要
我们提出了磁铁，这是一种基于神经网络的多代理相互作用模型，可发现控制动力学并预测复杂的多代理系统从观察值中的演变。我们将多代理系统制定为具有通用普通微分方程（ODE）状态进化的耦合的非线性网络，并开发了基于神经网络的时间消化模型的实现。对磁铁进行了训练，可以从观察值中发现多代理系统的核心动力学，并在线学习动力学的特定特定参数，以确保即使是代理的物理或关系属性，或者代理的数量也会发生变化。我们在二维空间中的点质量系统上评估磁铁，库拉莫托相同步动力学和捕食者 - 舒适的相互作用动力学，这些动力学表明了比传统深度学习模型的预测准确性提高的数量级。
We present the MagNet, a neural network-based multi-agent interaction model to discover the governing dynamics and predict evolution of a complex multi-agent system from observations. We formulate a multi-agent system as a coupled non-linear network with a generic ordinary differential equation (ODE) based state evolution, and develop a neural network-based realization of its time-discretized model. MagNet is trained to discover the core dynamics of a multi-agent system from observations, and tuned on-line to learn agent-specific parameters of the dynamics to ensure accurate prediction even when physical or relational attributes of agents, or number of agents change. We evaluate MagNet on a point-mass system in two-dimensional space, Kuramoto phase synchronization dynamics and predator-swarm interaction dynamics demonstrating orders of magnitude improvement in prediction accuracy over traditional deep learning models.
