基于图神经网络变分自编码器的可靠高效多智能体协同

Reliable and Efficient Multi-Agent Coordination via Graph Neural Network Variational Autoencoders

March 4, 2025

作者: Yue Meng, Nathalie Majcherczyk, Wenliang Liu, Scott Kiesel, Chuchu Fan, Federico Pecora

cs.AI

摘要

在自动化仓库等共享空间中，多智能体协调对于实现可靠的多机器人导航至关重要。在机器人流量密集的区域，局部协调方法可能无法找到无死锁的解决方案。在此类场景下，适宜由中央单元生成全局调度，决定机器人的通行顺序。然而，此类集中式协调方法的运行时间会随着问题规模的扩大而显著增加。本文提出利用图神经网络变分自编码器（GNN-VAE）来大规模解决多智能体协调问题，其速度远超集中式优化方法。我们将协调问题建模为图问题，并采用混合整数线性规划（MILP）求解器收集真实数据。训练过程中，我们的学习框架将图问题的高质量解决方案编码至潜在空间。在推理阶段，从采样的潜在变量中解码出解决方案样本，并选择成本最低的样本进行协调。最终，选取性能指标最高的可行方案进行部署。通过设计，我们的GNN-VAE框架始终返回符合所考虑协调问题约束的解决方案。数值结果表明，基于小规模问题训练的方法，即便面对250个机器人的大规模问题，也能获得高质量解决方案，且速度远超其他基线方法。项目页面：https://mengyuest.github.io/gnn-vae-coord

English

Multi-agent coordination is crucial for reliable multi-robot navigation in shared spaces such as automated warehouses. In regions of dense robot traffic, local coordination methods may fail to find a deadlock-free solution. In these scenarios, it is appropriate to let a central unit generate a global schedule that decides the passing order of robots. However, the runtime of such centralized coordination methods increases significantly with the problem scale. In this paper, we propose to leverage Graph Neural Network Variational Autoencoders (GNN-VAE) to solve the multi-agent coordination problem at scale faster than through centralized optimization. We formulate the coordination problem as a graph problem and collect ground truth data using a Mixed-Integer Linear Program (MILP) solver. During training, our learning framework encodes good quality solutions of the graph problem into a latent space. At inference time, solution samples are decoded from the sampled latent variables, and the lowest-cost sample is selected for coordination. Finally, the feasible proposal with the highest performance index is selected for the deployment. By construction, our GNN-VAE framework returns solutions that always respect the constraints of the considered coordination problem. Numerical results show that our approach trained on small-scale problems can achieve high-quality solutions even for large-scale problems with 250 robots, being much faster than other baselines. Project page: https://mengyuest.github.io/gnn-vae-coord