Graph attention networks based multi-agent path finding via temporal-spatial information aggregation.
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 i...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0318981 |
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| author | Qingling Zhang Peng Wang Cui Ni Xianchang Liu |
| author_facet | Qingling Zhang Peng Wang Cui Ni Xianchang Liu |
| author_sort | Qingling Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b2785a0ff33a458daf2db43711ff6c99 |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-b2785a0ff33a458daf2db43711ff6c992025-08-20T02:09:51ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01206e031898110.1371/journal.pone.0318981Graph attention networks based multi-agent path finding via temporal-spatial information aggregation.Qingling ZhangPeng WangCui NiXianchang LiuAn 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.https://doi.org/10.1371/journal.pone.0318981 |
| spellingShingle | Qingling Zhang Peng Wang Cui Ni Xianchang Liu Graph attention networks based multi-agent path finding via temporal-spatial information aggregation. PLoS ONE |
| title | Graph attention networks based multi-agent path finding via temporal-spatial information aggregation. |
| title_full | Graph attention networks based multi-agent path finding via temporal-spatial information aggregation. |
| title_fullStr | Graph attention networks based multi-agent path finding via temporal-spatial information aggregation. |
| title_full_unstemmed | Graph attention networks based multi-agent path finding via temporal-spatial information aggregation. |
| title_short | Graph attention networks based multi-agent path finding via temporal-spatial information aggregation. |
| title_sort | graph attention networks based multi agent path finding via temporal spatial information aggregation |
| url | https://doi.org/10.1371/journal.pone.0318981 |
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