REINFORCEMENT LEARNING-BASED OPTIMIZATION TECHNIQUES FOR DYNAMIC SYSTEMS

Dynamic systems are widely used in industrial automation, robotics, transportation, energy management, finance, healthcare, and communication networks. Traditional optimization methods often struggle to handle uncertainties, nonlinearities, and changing environmental conditions in real time. Reinforcement Learning (RL), a branch of machine learning, has emerged as a powerful optimization framework capable of learning optimal control strategies through interaction with the environment. This paper presents a comprehensive study of reinforcement learning-based optimization techniques for dynamic systems. The study discusses RL fundamentals, system modeling approaches, optimization algorithms, and practical applications in engineering and intelligent systems. Furthermore, the paper analyzes advantages, limitations, and future research directions for RL-driven optimization. Experimental comparisons demonstrate the effectiveness of deep reinforcement learning methods over conventional optimization techniques in dynamic and uncertain environments.