MULTI-AGENT REINFORCEMENT LEARNING FOR REAL-TIME SYSTEM OPTIMIZATION: FROM THEORY TO INDUSTRIAL APPLICATIONS
DOI:
https://doi.org/10.34218/IJCET_16_01_190Keywords:
Multi-Agent Reinforcement Learning (MARL), Distributed Decision-Making, System Optimization, Real-time Coordination, Adaptive Learning SystemsAbstract
Multi-Agent Reinforcement Learning (MARL) has emerged as a transformative approach to real-time system optimization, representing a significant advancement from traditional single-agent systems. This comprehensive article explores the evolution, implementation challenges, and practical applications of MARL systems across various industrial domains. The article examines crucial aspects including communication protocols, scalability considerations, and system performance metrics while highlighting the importance of coordination mechanisms in maintaining system stability. Through detailed analysis of inter-agent communication, reward sharing mechanisms, and error handling protocols, this article demonstrates how MARL systems effectively manage complex, distributed decision-making processes. The article encompasses both theoretical frameworks and practical implementations, focusing on applications in smart city infrastructure, food delivery systems, supply chain management, and emergency response coordination. The article also addresses critical challenges in learning stability, goal conflict resolution, and system robustness, providing insights into how modern MARL implementations overcome these obstacles through sophisticated adaptive mechanisms and fault-tolerant architectures.
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Copyright (c) 2025 Mohit Agarwal (Author)
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