Centralized Training with Decentralized Execution for Scalable Coordination in Distributed Decision Environments
DOI:
https://doi.org/10.66238/fsrma58Keywords:
CTDE, QMIX, Value decomposition, Multi-agent reinforcement learning, ScalabilityAbstract
Scalability remains a limiting factor in collaborative decision systems as the number of agents increases. This study examines a centralized training with decentralized execution (CTDE) paradigm to improve coordination efficiency under large-scale settings. A QMIX-based value decomposition framework is employed, allowing joint action-value estimation during training while preserving independent execution policies. The approach is evaluated on 9,400 large-scale coordination tasks with agent populations ranging from 10 to 50. Compared with independent learning baselines, the proposed method increases global reward by 28.6% and improves coordination success rate from 68.5% to 82.9%. Notably, performance degradation remains below 5% as agent count scales, indicating strong robustness. These results demonstrate that value factorization under CTDE is effective for managing complexity in distributed decision systems
References
Gronauer, S., & Diepold, K. (2022). Multi-agent deep reinforcement learning: a survey. Artificial Intelligence Review, 55(2), 895-943.
Gao, G., Ma, X., Lu, C., & Gao, R. (2026). Reliability Analysis and Application Research of SMS Communication Systems in Medical Notification Scenarios.
Kazim, R. M., Wang, G., Ming, Z., Cao, J., Allen, J. K., & Mistree, F. (2025). A Design Framework for Scalable and Adaptive Multi-Agent Coordination in Dynamic Environments: Addressing Concurrent Agent and Environment Interactions. IEEE Access.
Xu, D., Liu, H., Qiu, D., & Ma, Q. (2026). Structured Modeling and Representation Methods for Post-Retrieval Inference Processes in Large Video Language Models.
Myakala, P. K., & Kamatala, S. (2023). Scalable decentralized multi-agent federated reinforcement learning: Challenges and advances. International Journal of Electrical, Electronics and Computers, 8(6), 10-22161.
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.
Karim, M. M., Van, D. H., Khan, S., Qu, Q., & Kholodov, Y. (2025). Ai agents meet blockchain: A survey on secure and scalable collaboration for multi-agents. Future Internet, 17(2), 57.
Wang, Y., Chen, J., Wang, Y., & Yin, X. (2026). Application of Obtainable Biological Agent Characteristics in Efficacy Stratification of Oral Anti-Obesity Drugs.
Wibisono, A., Song, H. K., & Lee, B. M. (2025). A survey of multi-agent reinforcement learning for cooperative control in multi-AUV systems. IEEE Access.
Zhang, Y., & Wang, J. (2026). Design and Implementation of a Computer-Aided Full Lifecycle Quality Management System for Wind Farms in Upgrades, Renovations, and Subcontractor Supervision.
Baisero, A., Bhati, R., Liu, S., Pillai, A., & Amato, C. (2025). Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning. arXiv preprint arXiv:2505.10484.
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.
Rügamer, D., Bender, A., Wiegrebe, S., Racek, D., Bischl, B., Müller, C. L., & Stachl, C. (2022, September). Factorized structured regression for large-scale varying coefficient models. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 20-35). Cham: Springer Nature Switzerland.
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).
Marino, A. (2025). Learning to Communicate and Coordinate: Distributed Learning-based Control for Multi-Robot Systems (Doctoral dissertation, Université de Rennes).
Yue, L., Xu, D., Qiu, D., Shi, Y., Xu, S., & Shah, M. (2025, December). Sequential Cooperative Multi-Agent Online Learning and Adaptive Coordination Control in Dynamic and Uncertain Environments. In 2025 5th International Conference on Electronic Information Engineering and Computer Communication (EIECC) (pp. 692-697). IEEE.
Vallarino, D. (2024). Modeling adaptive fraud patterns: An agent-centric hybrid framework with MoE and deep learning. Available at SSRN 5001848.
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.
Bossens, D. M., Ramchurn, S., & Tarapore, D. (2022). Resilient robot teams: a review integrating decentralised control, change-detection, and learning. Current Robotics Reports, 3(3), 85-95.
Gao, G., Gao, R., Gao, R., & Zhou, H. (2026). Engineering Analysis and Quantitative Research on the Platform-Based Evolution of Enterprise Communication Systems.
Mai, T., & Nguyen, T. H. (2024). Inverse factorized soft q-learning for cooperative multi-agent imitation learning. Advances in Neural Information Processing Systems, 37, 27178-27206.
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.
Reginald, P. J. (2025). Context-Driven Cooperative Intelligent Control for Distributed Cyber-Physical Actuation Platforms Using CTDE Multi-Agent Reinforcement Learning. Recent Advances in Next-Generation Wireless Communication Systems, 43-50.
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.
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
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
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 Lukas Meier, Anna Schmid, David Müller
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/
