Reinforcement Learning for Adaptive IoT Systems: A Review of Algorithms and Real-World Applications

Abstract

Reinforcement Learning (RL) has emerged as a powerful approach for enabling adaptive decision-making in dynamic IoT environments. This paper presents a comprehensive review of RL algorithms applied to IoT systems, including Q-learning, Deep Q Networks (DQN), and policy gradient methods. It examines applications such as smart energy management, traffic optimization, and industrial automation. The study highlights challenges such as sample inefficiency, scalability, and real-time deployment constraints. Future directions include hybrid learning models, edge-based RL, and integration with federated learning for decentralized intelligence.