Reinforcement Learning in AI: Transforming Real-Time Optimization through Deep Neural Networks

Abstract

Reinforcement Learning (RL) has emerged as a powerful approach for real-time optimization, enabling autonomous decision-making in dynamic environments. By leveraging deep neural networks (DNNs), RL models can efficiently approximate complex value functions and policies, facilitating improved learning and generalization across various domains. This paper examines the transformative impact of RL in real-time optimization, emphasising the integration of deep learning techniques, including Deep Q Networks (DQN), Policy Gradient Methods, and Actor-Critic frameworks. Key applications of deep reinforcement learning (DRL) are examined in various fields, including robotics, autonomous systems, finance, and healthcare, highlighting its potential in real-world scenarios. The inherent challenges, including computational demands, sample inefficiency, and ethical considerations, as well as future directions aimed at enhancing the efficiency, interpretability, and scalability of RL, are also discussed. The findings underscore the significance of deep Reinforcement Learning in driving innovation and efficiency in AI-driven decision-making systems.