Intelligent Resource Allocation in Smart Cities Using Multi-Agent Reinforcement Learning

Abstract

The rapid integration of Internet of Things (IoT), Artificial Intelligence (AI), cloud computing, edge computing, and advanced communication technologies is transforming traditional urban infrastructure into intelligent smart cities. Conventional resource allocation methods struggle to manage dynamic urban environments, creating a need for adaptive and decentralized decision-making systems. This study proposes a Multi-Agent Reinforcement Learning (MARL) framework for intelligent resource allocation across transportation, energy, water, healthcare, emergency response, and communication systems. Each urban subsystem functions as an autonomous learning agent that optimizes local decisions while coordinating to improve overall city performance. The framework combines IoT sensing, edge intelligence, cloud analytics, and deep reinforcement learning to enable real-time, adaptive resource management. It enhances resource utilization, reduces energy consumption and response time, improves system reliability, and supports scalable urban operations. The proposed approach also provides a foundation for future smart city technologies, including digital twins, federated learning, autonomous edge intelligence, and 6G networks, promoting sustainable, resilient, and efficient urban development.