可靠且高效的多智能體協調：基於圖神經網絡的變分自編碼器

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

在多機器人導航於自動化倉庫等共享空間中，多智能體協調至關重要。在機器人流量密集的區域，局部協調方法可能無法找到無死鎖的解決方案。在這些情況下，適宜由中央單元生成全局調度，決定機器人的通行順序。然而，此類集中式協調方法的運行時間會隨著問題規模的增大而顯著增加。本文提出利用圖神經網路變分自編碼器（GNN-VAE）來大規模解決多智能體協調問題，其速度遠超集中式優化。我們將協調問題表述為圖問題，並使用混合整數線性規劃（MILP）求解器收集地面真值數據。在訓練過程中，我們的學習框架將圖問題的高質量解編碼到潛在空間中。在推理時，從採樣的潛在變量中解碼出解樣本，並選擇成本最低的樣本進行協調。最終，選擇性能指標最高的可行方案進行部署。通過設計，我們的GNN-VAE框架返回的解始終遵守所考慮協調問題的約束。數值結果表明，我們在小規模問題上訓練的方法，即使對於擁有250個機器人的大規模問題，也能實現高質量解，且速度遠超其他基線。項目頁面：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