Aspects of organizing game interactions among asymmetric agents using graph neural networks
https://doi.org/10.17586/2226-1494-2024-24-6-1044-1048
Abstract
The article considers the structures of representation of the graph of inter-agent connections for increasing the efficiency of agent interaction in cooperative competitive games using graph neural networks. A comparative assessment of metrics and adjacency matrices for graphs of connections defined using geometric and semantic metrics of proximity is performed. It is shown that semantic proximity is more effective in constructing a graph of inter-agent connections, and the use of oriented graphs ensures flexible management of information flows. The proposed patterns are important to consider when organizing multi-agent reinforcement learning in a wide range of application areas.
Keywords
graph theory,
graph neural networks,
reinforcement learning,
multi-agent systems,
cooperative-competitive behavior
Supporting agencies: The Ministry of Science and Higher Education of the Russian Federation: State Assignment No. 2019-1339.
About the Authors
A. O. Isakov
ITMO University
Russian Federation
Russian Federation
Artem O. Isakov - PhD Student,
Saint Petersburg, 197101
D. E. Peregorodiev
ITMO University
Russian Federation
Russian Federation
Danil E. Peregorodiev - Student,
Saint Petersburg, 197101
I. V. Tomilov
ITMO University
Russian Federation
Russian Federation
Ivan V. Tomilov - Student,
Saint Petersburg, 197101
N. F. Gusarova
ITMO University
Russian Federation
Russian Federation
Natalia F. Gusarova - PhD, Senior Researcher, Associate Professor,
Saint Petersburg, 197101
A. A. Golubev
ITMO University
Russian Federation
Russian Federation
Alexander A. Golubev - PhD Student,
Saint Petersburg, 197101
References
1. Yang S. Hierarchical graph multi-agent reinforcement learning for traffic signal control. Information Sciences, 2023, vol. 634, pp. 55–72. https://doi.org/10.1016/j.ins.2023.03.087
2. Veličković P. Everything is connected: Graph neural networks. Current Opinion in Structural Biology, 2023, vol. 79, pp. 102538. https://doi.org/10.1016/j.sbi.2023.102538
3. Khemani B., Patil S., Kotecha K., Tanwar S. A review of graph neural networks: concepts, architectures, techniques, challenges, datasets, applications, and future directions. Journal of Big Data, 2024, vol. 11, no. 1, pp. 18. https://doi.org/10.1186/s40537-023-00876-4
4. Nie M., Chen D., Wang D. Reinforcement learning on graphs: A survey. IEEE Transactions on Emerging Topics in Computational Intelligence, 2023, vol. 7, no. 4, pp. 1065–1082. https://doi.org/10.1109/tetci.2022.3222545
5. Zhou J., Cui G., Hu S., Zhang Z., Yang C., Liu Z., Wang L., Li C., Sun M. Graph neural networks: A review of methods and applications. AI Open, 2020, vol. 1, pp. 57–81. https://doi.org/10.1016/j.aiopen.2021.01.001
6. Wu Z., Pan S., Chen F., Long G., Zhang C., Yu P.S. A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems, 2020, vol. 32, no. 1, pp. 4–24. https://doi.org/10.1109/tnnls.2020.2978386
7. Bhatti U.A., Tang H., Wu G., Marjan S., Hussain A. Deep learning with graph convolutional networks: An overview and latest applications in computational intelligence. International Journal of Intelligent Systems, 2023, vol. 2023, pp. 8342104. https://doi.org/10.1155/2023/8342104
8. Wu L., Cui P., Pei J., Zhao L., Guo X. Graph neural networks: foundation, frontiers and applications. Proc. of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022, pp. 4840–4841. https://doi.org/10.1145/3534678.3542609
9. Meng L., Shao Y., Yuan L., Lai L., Cheng P., Li X., Yu W., Zhang W., Lin X., Zhou J. A survey of distributed graph algorithms on massive graphs. ACM Computing Surveys, 2024, vol. 57, no. 2, pp. 1–39. https://doi.org/10.1145/3694966
10. Vrahatis A.G., Lazaros K., Kotsiantis S. Graph Attention Networks: A Comprehensive Review of Methods and Applications. Future Internet, 2024, vol. 16, no. 9, pp. 318. https://doi.org/10.3390/ fi16090318
11. Munikoti S., Agarwal D., Das L., Halappanavar M., Natarajan B. Challenges and opportunities in deep reinforcement learning with graph neural networks: A comprehensive review of algorithms and applications. IEEE Transactions on Neural Networks and Learning Systems, 2023, vol. 35, no. 11, pp. 15051–15071. https://doi.org/10.1109/tnnls.2023.3283523
12. Verbavatz V., Barthelemy M. Betweenness centrality in dense spatial networks. Physical Review E, 2022, vol. 105, no. 5, pp. 054303. https://doi.org/10.1103/physreve.105.054303
13. Tsalouchidou I., Baeza-Yates R., Bonchi F., Liao K., Sellis T. Temporal betweenness centrality in dynamic graphs. International Journal of Data Science and Analytics, 2020, vol. 9, no. 3, pp. 257– 272. https://doi.org/10.1007/s41060-019-00189-x
14. Wills P., Meyer F.G. Metrics for graph comparison: a practitionerʼs guide. PLoS ONE, 2020, vol. 15, no. 2, pp. e0228728. https://doi.org/10.1371/journal.pone.0228728
15. O’Bray L., Horn M., Rieck B., Borgwardt K. Evaluation metrics for graph generative models: Problems, pitfalls, and practical solutions. Proc. of the ICLR 2022 — 10th International Conference on Learning Representations, 2022.
Review
For citations:
Isakov A.O., Peregorodiev D.E., Tomilov I.V., Gusarova N.F., Golubev A.A. Aspects of organizing game interactions among asymmetric agents using graph neural networks. Scientific and Technical Journal of Information Technologies, Mechanics and Optics. 2024;24(6):1044-1048. (In Russ.) https://doi.org/10.17586/2226-1494-2024-24-6-1044-1048
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