Reinforcement Learning Background

The first chapter of this book provides an in-depth exploration of reinforcement learning (RL) and its background. It begins with an overview of RL, explaining its fundamental principles and mechanisms. The concept of RL is introduced, highlighting its powerful capability to optimize complex problems via the interaction between an agent and an environment to learn optimal behaviors through trial and error. The chapter then delves into popular algorithms for optimizing problems with discrete action spaces. It presents a comprehensive discussion on Q-learning and deep Q-learning approaches. Furthermore, the chapter explores RL algorithms designed for continuous action spaces, focusing on deep deterministic policy gradient. By covering these topics, the chapter provides readers with a comprehensive understanding of RL algorithms for both discrete and continuous action spaces. The insights gained from this chapter serve as a solid foundation for subsequent chapters in the book, which discuss advanced RL techniques and applications in wireless systems.