Reinforcement learning (RL) has emerged as a powerful tool for addressing complex decision making problems in various domains, including aviation. This paper provides a comprehensive overview of RL and its applications in the aviation industry. We begin by introducing the fundamental concepts and algorithms of RL, highlighting their unique advantages in learning from interaction and optimizing decision-making processes. We then delve into a detailed examination of the successful implementation of RL methods in aviation, covering areas such as flight control, air traffic management, airline revenue management, aircraft maintenance scheduling, etc. Furthermore, we discuss the potential benefits of RL in enhancing safety, and sustainability within the aviation sector. Finally, we identify and explore open challenges and areas for future research, emphasizing the need for continued innovation and collaboration between the fields of reinforcement learning and aviation.