Reinforcement Learning For Energy Management In Smart Homes: A Review Of Algorithms, Environments And Iot-Integrated Control

Rising electricity demand and the spread of smart home technology create a need for intelligent, sustainable residential energy management. Traditional approaches rely on fixed schedules and predefined rules that respond poorly to changing prices, demand, weather and user requirements. Reinforcement Learning (RL) offers a different approach in which an agent learns optimal control decisions by interacting with the home environment and receiving feedback. This paper reviews RL for smart-home energy management with the goals of reducing consumption and cost while maintaining comfort. It introduces Markov decision processes and the main RL algorithms, from tabular Q-learning to deep Q-networks, proximal policy optimization and soft actor-critic, then surveys applications to demand response, appliance scheduling, heating, ventilation and air conditioning control, electric-vehicle charging, rooftop solar and battery storage. Simulation environments for training and benchmarking agents are discussed. A five-layer architecture integrating IoT sensing and smart meters, state construction with forecasting, an RL decision engine, device control and a user interface with comfort preferences is proposed, together with research gaps, objectives, a comparison of algorithms and challenges such as safety, sample efficiency, comfort modelling and transfer between homes. The review concludes that RL can adaptively optimize household energy use when combined with safety constraints and user feedback.