# Graph attention networks based multi-agent path finding via temporal-spatial information aggregation

## TL;DR

This paper introduces a new method for multi-agent path finding using graph attention networks to improve performance and scalability in complex environments.

## Contribution

A novel temporal-spatial information aggregation approach using GAT and GRU-CNN for multi-agent path finding is proposed.

## Key findings

- The proposed method improves accuracy and time efficiency by 24.5%, 47%, and 37.5%, 73% over GNN and GAT.
- Performance enhancements are more significant in larger maps, demonstrating scalability.
- GAT-based inter-agent communication effectively addresses partial observability issues.

## Abstract

An effective Multi-Agent Path Finding (MAPF) algorithm must efficiently plan paths for multiple agents while adhering to constraints, ensuring safe navigation from start to goal. However, due to partial observability, agents often struggle to determine optimal strategies. Thus, developing a robust information fusion method is crucial for addressing these challenges. Information fusion expands the observation range of each agent, thereby enhancing the overall performance of the MAPF system. This paper explores a fusion approach in both temporal and spatial dimensions based on Graph Attention Networks (GAT). Since MAPF is a long-horizon, continuous task, leveraging historical observation dependencies is key for predicting future actions. Initially, historical observations are fused by incorporating a Gated Recurrent Unit (GRU) with a Convolutional Neural Network (CNN), extracting local observations to form an encoder. Next, GAT is used to enable inter-agent communication, utilizing the stability of the scaled dot-product aggregation to merge agents’ information. Finally, the aggregated data is decoded into the agent’s final action strategy, effectively solving the partial observability problem. Experimental results show that the proposed method improves accuracy and time efficiency by 24.5%, 47%, and 37.5%, 73% over GNN and GAT, respectively, under varying map sizes and agent densities. Notably, the performance enhancement is more pronounced in larger maps, highlighting the algorithm’s scalability.