A Review on Multi-Agent Deep Reinforcement Learning for IoT: Techniques and Applications

The Internet of Things (IoT) connects billions of devices across domains such as transportation, healthcare, agriculture, and energy-creating highly dynamic, distributed, and heterogeneous environments. These characteristics pose significant challenges for control, coordination, scalability, and adaptability. In response, Multi-Agent Deep Reinforcement Learning (MADRL) has emerged as a promising paradigm by combining the decision-making intelligence of reinforcement learning with the collaborative capabilities of multi-agent systems. To leverage MADRL in building intelligent, resilient, and adaptive IoT systems, this review systematically explores the application of MADRL in IoT, categorizing contributions by application domains, learning architectures, and coordination strategies. We analyze how MADRL enables scalable resource allocation, routing optimization, energy efficiency, and fault detection