An Introduction to Reinforcement Learning and Its Application in Various Domains

Abstract

Reinforcement learning (RL) is a dynamic and evolving subfield of machine learning that focuses on training intelligent agents to learn and adapt through interactions with their environment. This introductory article provides an overview of the fundamental concepts and principles of RL, elucidating its core components, such as the agent, environment, actions, and rewards. This study aims to give readers an in-depth introduction to RL and show examples of its different uses in various domains. RL can allow agents to learn through interaction with an environment, which has led to its enormous interest. The core ideas of RL and its essential elements will be covered in this study, after which it will go into applications in industries including robotics, gaming, finance, healthcare, and more. The fundamental ideas of RL will become clearer to readers, and they will recognize how transformative it can be when used to address challenging decision-making issues. These applications demonstrate the versatility and significance of RL in shaping the future of technology and automation.