The domains of transport and logistics are increasingly relying on autonomous mobile robots for the handling and distribution of passengers or resources. At large system scales, finding decentralized path planning and coordination solutions is key to efficient system performance. Recently, Graph Neural Networks (GNNs) have become popular due to their ability to learn communication policies in decentralized multi-agent systems. Yet, vanilla GNNs rely on simplistic message aggregation mechanisms that prevent agents from prioritizing important information. To tackle this challenge, in this paper, we extend our previous work that utilizes GNNs in multi-agent path planning by incorporating a novel mechanism to allow for message-dependent attention. Our Message-Aware Graph Attention neTwork (MAGAT) is based on a key-query-like mechanism that determines the relative importance of features in the messages received from various neighboring robots. We show that MAGAT is able to achieve a performance close to that of a coupled centralized expert algorithm. Further, ablation studies and comparisons to several benchmark models show that our attention mechanism is very effective across different robot densities and performs stably in different constraints in communication bandwidth. Experiments demonstrate that our model is able to generalize well in previously unseen problem instances, and that it achieves a 47\% improvement over the benchmark success rate, even in very large-scale instances that are $\times$100 larger than the training instances.
翻译:交通运输和物流领域日益依赖自主移动机器人来处理和分配乘客或资源。在大系统规模上,找到分散路径规划和协调解决方案是高效系统运行的关键。最近,图像神经网络(GNNS)因其在分散多试剂系统中学习通信政策的能力而变得受欢迎。然而,Vanilla GNNS依靠简单的信息汇总机制,防止代理商对重要信息进行优先排序。为了应对这一挑战,我们在本文件中扩展了以前在多试剂路径规划中利用GNNs使用GNs的工作,纳入了允许关注信息依赖性的新机制。我们的信息-Aware图形注意 NETwork(MAGAT)基于一个关键类机制,它决定了从各相邻机器人收到的信息的相对重要性。我们表明,MAGAT能够接近于一个同时集中的专家算法的简单化综合机制。此外,与一些基准模型的对比研究显示,我们的关注机制在不同的机器人密度上非常有效,并在不同的通信带宽度中进行精确的制约。实验显示,我们的模型在47年的大规模成功率上能够超越以往的标准。