International Journal of Scientific Research and Engineering Development

Industrial power plants require highly efficient and adaptive control systems to manage complex, nonlinear, and time-sensitive processes. Traditional control algorithms, while effective in stable settings, struggle under dynamic load variations and unexpected operational conditions. Reinforcement Learning (RL), a subset of machine learning, has emerged as a powerful tool to enhance the intelligence of automated control systems through experience-based learning and real-time optimization. This paper investigates the application of RL in improving control precision, energy efficiency, fault resilience, and adaptive decision-making in industrial power plants. We present a modular RL-based control architecture, benchmark its performance against conventional PID and fuzzy logic controllers, and explore its implementation in scenarios such as boiler control, turbine optimization, and fault-tolerant systems. Experimental results demonstrate that RL controllers outperform baseline models in both stability and responsiveness. The study offers a framework for integrating RL into existing industrial automation systems while addressing deployment challenges and safety requirements.