Graph Neural Networks for Decentralized Path Planning

Qingbiao Li, Fernando Gama, Alejandro Ribeiro, Amanda Prorok

Abstract: We propose a combined model that automatically synthesizes local communication and decision-making policies for agents navigating in constrained workspaces. Our architecture is composed of a convolutional neural network (CNN) that extracts adequate features from local observations, and a graph neural network (GNN) that communicates these features among agents. We train the model to imitate an expert algorithm, and use the resulting model online in decentralized planning involving only local communication and local observations. We evaluate our method in simulations involving teams of agents in cluttered workspaces. We measure the success rates and sum of costs over the planned paths. The results show a performance close to that of our expert algorithm, demonstrating the validity of our approach. In particular, we show our model's capability to generalize to previously unseen cases (involving larger environments and larger agent teams).