Dynamical systems comprised of autonomous agents arise in many relevant
problems such as multi-agent robotics, smart grids, or smart cities.
Controlling these systems is of paramount importance to guarantee a successful
deployment. Optimal centralized controllers are readily available but face
limitations in terms of scalability and practical implementation. Optimal
decentralized controllers, on the other hand, are difficult to find. In this
paper, we propose a framework using graph neural networks (GNNs) to learn
decentralized controllers from data. While GNNs are naturally distributed
architectures, making them perfectly suited for the task, we adapt them to
handle delayed communications as well. Furthermore, they are equivariant and
stable, leading to good scalability and transferability properties. The problem
of flocking is explored to illustrate the potential of GNNs in learning
decentralized controllers.

本文提出一种利用图网络来从数据中学习去中心化控制器的框架，通过解决群聚问题展示了其在动力学系统中的应用潜力。

基于图神经网络的分散式控制器

Graph Neural Networks for Decentralized Controllers

Network data can be conveniently modeled as a graph signal, where data values
are assigned to nodes of a graph that describes the underlying network
topology. Successful learning from network data is built upon methods that
effectively exploit this graph structure. In this work, we leverage graph
signal processing to characterize the representation space of graph neural
networks (GNNs). We discuss the role of graph convolutional filters in GNNs and
show that any architecture built with such filters has the fundamental
properties of permutation equivariance and stability to changes in the
topology. These two properties offer insight about the workings of GNNs and
help explain their scalability and transferability properties which, coupled
with their local and distributed nature, make GNNs powerful tools for learning
in physical networks. We also introduce GNN extensions using edge-varying and
autoregressive moving average graph filters and discuss their properties.
Finally, we study the use of GNNs in recommender systems and learning
decentralized controllers for robot swarms.

本文利用图信号处理来表征图神经网络（GNNs）的表征空间，讨论了图卷积滤波器在 GNNs 中的作用，并展示了这类滤波器所建立的任何架构都具有置换等变性和对网络拓扑的稳定性的基本属性。然后，我们讨论了扩展了边变量和自回归移动平均图滤波器的 GNNs 以及它们的属性，并最终研究了在推荐系统和学习机器人群体的分散式控制器中使用 GNNs 的相关研究。

图表，卷积和神经网络：从图滤波器到图神经网络

Graphs, Convolutions, and Neural Networks: From Graph Filters to Graph  Neural Networks

Teams of autonomous unmanned aircraft can be used to monitor wildfires,
enabling firefighters to make informed decisions. However, controlling multiple
autonomous fixed-wing aircraft to maximize forest fire coverage is a complex
problem. The state space is high dimensional, the fire propagates
stochastically, the sensor information is imperfect, and the aircraft must
coordinate with each other to accomplish their mission. This work presents two
deep reinforcement learning approaches for training decentralized controllers
that accommodate the high dimensionality and uncertainty inherent in the
problem. The first approach controls the aircraft using immediate observations
of the individual aircraft. The second approach allows aircraft to collaborate
on a map of the wildfire's state and maintain a time history of locations
visited, which are used as inputs to the controller. Simulation results show
that both approaches allow the aircraft to accurately track wildfire expansions
and outperform an online receding horizon controller. Additional simulations
demonstrate that the approach scales with different numbers of aircraft and
generalizes to different wildfire shapes.

本文研究了利用自主无人机团队监测野火的方法，通过使用两种深度强化学习方法训练分散控制器，可以有效地追踪野火的扩散，这两种方法在不同数量的飞行器和不同野火形状下都有较好的表现，模拟结果表明，这两种方法优于在线递归视角控制器。