Fundamentals of AI and Deep Reinforcement Learning

Abstract

The sphere of Artificial Intelligence (AI) develops quickly, and Deep Reinforcement Learning (DRL) is one of the innovative methods to create adaptive intelligent systems. DRL is a combination of reinforcement learning and deep neural networks, allowing agents to learn the best strategies by interacting with their environments and achieve better performance as time goes on. The following paper will provide an overview of the concept of DRL and its usage and limitations, especially in three areas where it has been used most: computer vision, natural language processing (NLP), and robotics. A comparative analysis of the literature was conducted. The review grouped research in the three domains and compared DRL algorithms according to the performance measures of accuracy, success rates, and sample efficiency. It is found that with DRL, significant progress has been made on vision-related tasks, such as 88% accuracy in medical imaging and 85% in object detection. Dialogue and conversational systems based on DRL models show 75-82% success rates in NLP. Robotics DRL allows significant amounts of manipulation and locomotion control, and has been adequate in the range of 72 to 78 percent, even though safety and efficiency issues have still been encountered. The study details that DRL is a valuable AI technology that needs specific improvements to achieve data effectiveness, readability, and risk-free implementation. Overcoming these challenges will enable wider acceptance in high-stakes industries and increase DRL’s potential to solve even more complex problems and issues in society and technology.