Introduction of Reinforcement Learning and Its Application Across Different Domain

In the modern era of rapid development in Deep Neural Networks, Reinforcement Learning (RL) has evolved into a pivotal and transformative technology. RL, a learning process where these machine agent interacts with several unknown environment through trial and error. The agent, responsive to the learning machine, go through these interaction, and start receiving feedback in the form of positive rewards or negative rewards like penalties from the environment, and constantly refines its behavior. This research paper offers an in-depth introduction to the foundational concepts of RL, focusing on Markov Decision Processes and various RL algorithms. Machine Learning (ML) is a subset of Artificial Intelligence, which deals with ‘‘the question of how to develop software agents (Machine) that improve automatically with experience’’. The basic three categories of Machine Learning are. Supervised Learning Unsupervised Learning Reinforcement Learning RL method is that in any situation the agent has to choose between using its acquired knowledge of the environment i.e. using an action already tried or performed previously or exploring actions never tried before in that situation. In this review paper, we will discuss the most used learning algorithms in games robotics and healthcare, autonomous control as well as communication and networking, natural language processing.[1]