Deep reinforcement learning (DRL) for the Internet of Things (IoT) is a cutting-edge research area that focuses on applying intelligent decision-making and self-adaptive strategies to optimize the performance of IoT systems. Research papers in this domain explore the use of DRL algorithms for dynamic resource allocation, energy-efficient communication, task offloading, traffic routing, and network management in heterogeneous IoT networks. Key contributions include integrating DRL with edge/fog computing for low-latency decision-making, IoT device clustering, spectrum management, and predictive maintenance, as well as addressing challenges of scalability, energy constraints, and real-time adaptability. Recent studies also investigate the combination of DRL with federated learning, AI-driven security mechanisms, and 5G/6G-enabled IoT frameworks to enhance efficiency, reliability, and resilience in large-scale IoT deployments. By leveraging DRL, IoT research aims to create intelligent, autonomous, and optimized ecosystems capable of adapting to dynamic network conditions and diverse application requirements.