Graph Attention Reinforcement Learning for Structured Interaction in Cooperative Decision Networks
DOI:
https://doi.org/10.66238/fsrma55Keywords:
Graph attention networks, Reinforcement learning, Multi-agent systems, Actor-critic, CoordinationAbstract
Modeling interaction structure is essential for scalable coordination in cooperative decision systems. This work explores a graph attention-based reinforcement learning approach in which agents are represented as nodes and communication dependencies are encoded as dynamic edges. Policy updates are performed using actor-critic learning combined with graph attention networks (GAT) to capture context-aware influence among agents. The model is evaluated on 8,500 collaborative decision instances derived from logistics routing and distributed resource allocation tasks. Compared with fully connected communication baselines, the proposed framework achieves a 21.7% improvement in task efficiency and reduces communication overhead by 18.3%. Moreover, performance gains remain stable as the number of agents increases from 5 to 20, demonstrating improved scalability. These results highlight the importance of structured interaction modeling in reinforcement-driven coordination systems.
References
Louati, A., Louati, H., Kariri, E., Neifar, W., Hassan, M. K., Khairi, M. H., ... & El-Hoseny, H. M. (2024). Sustainable smart cities through multi-agent reinforcement learning-based cooperative autonomous vehicles. Sustainability, 16(5), 1779.
Xu, D., Liu, H., Qiu, D., & Ma, Q. (2026). Structured Modeling and Representation Methods for Post-Retrieval Inference Processes in Large Video Language Models.
Hady, M. A., Hu, S., Pratama, M., Cao, Z., & Kowalczyk, R. (2025). Multi-agent reinforcement learning for resources allocation optimization: a survey. Artificial Intelligence Review, 58(11), 354.
Zhang, Y., Gu, W., & Wang, J. (2026). Construction of Wind Farm Asset Health Index Based on Multi-Dimensional Indicators and Analytic Hierarchy Process and Its Correlation with Operational Performance. Authorea Preprints.
Charalambous, T., Pappas, N., Nomikos, N., & Wichman, R. (2025). Toward goal-oriented communication in multi-agent systems: An overview. arXiv preprint arXiv:2508.07720.
Liu, S., Liu, X., & Feng, H. (2025, November). Research on AI-Driven Visual Design and Immersive Interactive Experiences Based on Multimodal Cognition and User. In Proceedings of the 2025 International Conference on Digital Society and Intelligent Computing (pp. 734-740).
Maldonado, D., Cruz, E., Torres, J. A., Cruz, P. J., & Benitez, S. D. P. G. (2024). Multi-agent systems: A survey about its components, framework and workflow. IEEE Access, 12, 80950-80975.
Qiu, D., Xu, D., & Yue, L. (2025, December). Reinforcement Learning-Augmented LLM Agents for Collaborative Decision Making and Performance Optimization. In 2025 7th International Conference on Frontier Technologies of Information and Computer (ICFTIC) (pp. 1337-1342). IEEE.
Tam, P., Song, I., Kang, S., Ros, S., & Kim, S. (2022). Graph neural networks for intelligent modelling in network management and orchestration: a survey on communications. Electronics, 11(20), 3371.
Zhang, Y., Gu, W., & Wang, J. (2025). Research on First Article Inspection (FAI)-Driven Quality Assurance Methods for Wind Turbine Installation and Operation & Maintenance and Their Effect on Reliability Improvement. Available at SSRN 6094206.
Charalambous, T., Pappas, N., Nomikos, N., & Wichman, R. (2025). Toward goal-oriented communication in multi-agent systems: An overview. arXiv preprint arXiv:2508.07720.
Gao, G., Gao, R., Gao, R., & Zhou, H. (2026). Engineering Analysis and Quantitative Research on the Platform-Based Evolution of Enterprise Communication Systems.
Kumar, S., & Thakur, M. K. (2025). Attention dynamics: Evolution of attention-based view and its implications. Management Review Quarterly, 75(4), 3727-3772.
Jiao, Y., Zhao, B., Wang, A., & Shi, T. (2026). Construction and Empirical Study of a Modularized Teaching System for Art Courses Based on a Unified Training Pathway.
Low, D., & Zhou, Y. (2025). Cooperative multi-agent reinforcement learning for robotic systems: A review. Multiagent and Grid Systems, 21(2), 96-123.
Xu, D., Chen, H., & Gui, H. (2026). Unified Online Estimation Method for SOC, SOH, and Power Capacity Considering Safety Boundary Consistency in Battery Management Systems.
Singh, P., Hazarika, B., & Huang, W. J. (2025). Graph-based Federated Multi-Agent DRL for Semantic and Intent-Aware V2X Communication. IEEE Internet of Things Journal.
Wang, Y., Yin, X., Chen, J., & Wang, Y. (2026). Evidence-Based Study on Low-Burden Digital Phenotyping for Precision Screening of Oral Anti-Obesity Drug Efficacy.
Okubo, T., & Takahashi, M. (2023). Multi-agent action graph based task allocation and path planning considering changes in environment. IEEE Access, 11, 21160-21175.
Jiao, Y., Wang, A., Zhao, B., & Shi, T. (2026). The Impact of Visual Language Strategies in Public Art Creation on Community Spatial Perception and Public Behavior.
Munikoti, S., Agarwal, D., Das, L., Halappanavar, M., & Natarajan, B. (2023). 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, 35(11), 15051-15071.
Xu, D., Gui, H., & Chen, H. (2026). Research on Layered Control and Fault Recovery Mechanisms for Fast Charging Safety Diagnosis of High Voltage Battery Systems Under Charging Network Interoperability Conditions.
Pasham, S. D. (2024). Scalable Graph-Based Algorithms for Real-Time Analysis of Big Data in Social Networks. The Metascience, 2(1), 92-129.
Gao, G., Ma, X., Lu, C., & Gao, R. (2026). Reliability Analysis and Application Research of SMS Communication Systems in Medical Notification Scenarios.
Ahmad, N. R. (2025). Monetary policy, inflation and unemployment: The interactions and their economic impacts in Pakistan. Pakistan Journal of Economics and Public Policy, 5(1), 66–81.
Ahmad, N. R. (2025). Leadership ethics in practice: Employees’ experiences of integrity and hypocrisy in public-sector organizations in Pakistan. Journal of Applied Leadership and Transparency. https://doi.org/10.63878/jalt874
Ahmad, N. R. (2025). Blockchain beyond buzzwords: Evaluating its practical application in Pakistan’s supply chain systems. Quarterly Review Journal of Social Sciences. https://doi.org/10.63878/qrjs253
Safdar, M. R. (2025). Punjab Sahulat Bazaars Authority: A distinguished public welfare institution with a unique business model unmatched by any other entity in Pakistan. Contemporary Journal of Social Sciences and Research, 3(3). https://doi.org/10.63878/cjssr.v3i3.1311
Toor, M. J., Shabbir, S., Rauf, A., Nisar, A., Safdar, S., & Zaib-Un-Nisa. (n.d.). The role of structured-communication-technique in developing pragmatic language capability among ASD-children. https://doi.org/10.63878/cjssr.v3i2.785
Toor, M. J., Mehmood, H., Yasmeen, A., Honey, Q., Waseem, M., & Wasif, M. (n.d.). Exploring the effect of verbal encouraging phrases (VEP) on Chinese language learners’ academic achievement at university level.
Toor, M. J., Arafat, M., Ayoub, M., Ali, M., & Alessi Longa, F. E. (n.d.). From coherent self to fragmented persona: Digital identity fragmentation and academic self-concept among Generation Z, with comparative insights from Generation Alpha. https://doi.org/10.63878/cjssr.v4i2.2318
Katsitadze, N., Kharadze, N., & Pirtskhalaishvili, D. (2025). The impact of social media on students’ effective time management. Baltic Journal of Economic Studies, 11(1), 200–210. https://doi.org/10.30525/2256-0742/2025-11-1-200-210
Toor, M. J., Zafar, S., Omer, F., Sarwar, M., & Hussain, A. K. (n.d.). Teachers’ perspectives on effectiveness of translanguaging in enhancing English language learning at secondary school level.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Mikkel Jensen, Anders Nielsen, Rasmus Pedersen
This work is licensed under a Creative Commons Attribution 4.0 International License.
License Terms (Recommended – CC BY 4.0)
All articles published in Fundamental Scientific Reports in Multidisciplinary Areas are licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author(s) and source are properly cited. Users are allowed to copy, redistribute, remix, transform, and build upon the material for any purpose, including commercial use, as long as appropriate credit is given.
Authors retain copyright of their work while granting the journal the right of first publication. The journal provides immediate open access to its content on the principle that making research freely available supports a greater global exchange of knowledge.
For more information about this license, visit:
https://creativecommons.org/licenses/by/4.0/
