A Deep Reinforcement Learning-Based Multi-Agent Framework for Dynamic Optimization of QoS in IoT Services

To address key challenges in IoT systems, including efficient resource allocation, adaptive service composition, and Quality of Service (QoS) under dynamic conditions, we develop a framework called DRL-MAS integrating multi-agent systems (MAS) and deep reinforcement learning (DRL). DRL-MAS leverages MAS's decentralized decision-making capabilities and DRL's adaptive learning strengths to ensure scalability, energy efficiency, and responsiveness in distributed IoT systems. By incorporating edge computing, DRL-MAS minimizes dependency on centralized systems, reduces latency, and optimizes energy consumption. Experimental results demonstrate the DRL-MAS's effectiveness in dynamically optimizing service composition and resource management while complying with QoS requirements.