Machine learning plays a pivotal role in artificial intelligence, allowing machines to mimic human language and making tremendous progress in a wide range of fields. Machine learning has become widely popular owing to its adaptability and breadth of application. Reinforcement learning is one of the most well-known uses of machine learning; it allows robots and software agents to learn and adjust their behaviour in order to achieve better results in a given setting. As a result of its many advantages in developing intelligent agents, including self-improvement, web-based learning, and decreased programming requirements, reinforcement learning has emerged as a leading technique in this field. Even if there is constant research to increase security and efficiency of algorithms, there is still a lot of potential for advancement. Therefore, the purpose of this study is to give a thorough examination of reinforcement learning and its applications within the larger field of Machine Learning, and it does so by making use of a wide range of algorithmic techniques.