论文标题
多代理路由值迭代网络
Multi-Agent Routing Value Iteration Network
论文作者
论文摘要
在本文中，我们解决了以协调方式路由多个代理的问题。这是一个复杂的问题，在车队管理中具有广泛的应用程序，以实现一个共同的目标，例如从一群机器人和乘车共享中绘制。传统方法通常不是为现实环境而设计的，即包含稀疏连接的图和未知流量，并且运行时通常太慢而无法实用。相比之下，我们提出了一个基于图神经网络的模型，该模型能够基于稀疏连接的图形在具有动态变化的流量条件的稀疏连接图中基于学习的值迭代执行多代理路由。此外，我们博学的通信模块使代理可以在线协调并更有效地适应更改。我们创建了一个模拟环境，以模仿由具有未知最小边缘覆盖范围和交通状况未知的自动驾驶汽车执行的现实映射；我们的方法在总成本和运行时都大大优于传统求解器。我们还表明，只有两个具有25个节点的图表上的代理训练的模型很容易概括到具有更多代理和/或节点的情况。
In this paper we tackle the problem of routing multiple agents in a coordinated manner. This is a complex problem that has a wide range of applications in fleet management to achieve a common goal, such as mapping from a swarm of robots and ride sharing. Traditional methods are typically not designed for realistic environments hich contain sparsely connected graphs and unknown traffic, and are often too slow in runtime to be practical. In contrast, we propose a graph neural network based model that is able to perform multi-agent routing based on learned value iteration in a sparsely connected graph with dynamically changing traffic conditions. Moreover, our learned communication module enables the agents to coordinate online and adapt to changes more effectively. We created a simulated environment to mimic realistic mapping performed by autonomous vehicles with unknown minimum edge coverage and traffic conditions; our approach significantly outperforms traditional solvers both in terms of total cost and runtime. We also show that our model trained with only two agents on graphs with a maximum of 25 nodes can easily generalize to situations with more agents and/or nodes.
