An exploration of reinforcement learning and deep reinforcement learning

Today, machine learning is evolving so quickly that new algorithms are always appearing. Deep neural networks in particular have shown positive outcomes in a variety of areas, including computer vision, natural language processing, and time series prediction. Its development moves at a very sluggish pace due to the high threshold. Therefore, a thorough examination of the reinforcement learning field should be required. This essay examines both the deep learning algorithm and the reinforcement learning operational procedure. The study identifies information retrieval, data mining, intelligent speech, natural language processing, and reinforcement learning as key technologies. The scientific study of reinforcement learning has advanced remarkably quickly, and it is now being used to tackle important decision optimization issues at academic conferences and journal research work in computer networks, computer graphics, etc. Brief introductions and reviews of both types of models are provided in this paper, along with an understanding of some of the most cutting-edge reinforcement learning applications and approaches.