In recent years, reinforcement learning (RL) based quadrupedal locomotion control has emerged as an extensively researched field, driven by the potential advantages of autonomous learning and adaptation compared to traditional control methods. This paper provides a comprehensive study of the latest research in applying RL techniques to develop locomotion controllers for quadrupedal robots. We present a detailed overview of the core concepts, methodologies, and key advancements in RL-based locomotion controllers, including learning algorithms, training curricula, reward formulations, and simulation-to-real transfer techniques. The study covers both gait-bound and gait-free approaches, highlighting their respective strengths and limitations. Additionally, we discuss the integration of these controllers with robotic hardware and the role of sensor feedback in enabling adaptive behavior. The paper also outlines future research directions, such as incorporating exteroceptive sensing, combining model-based and model-free techniques, and developing online learning capabilities. Our study aims to provide researchers and practitioners with a comprehensive understanding of the state-of-the-art in RL-based locomotion controllers, enabling them to build upon existing work and explore novel solutions for enhancing the mobility and adaptability of quadrupedal robots in real-world environments.