Reinforcement Learning (RL) is a field of Machine Learning that is concerned with how intelligent agents should act in an environment, so as to maximise the notion of cumulative reward. Generally, a Reinforcement Learning agent can perceive its environment, interpret it, and take action, as well as learn through trial and error. Reinforcement Learning, along with supervised learning and unsupervised learning, is one of the three basic paradigms used in Machine Learning. According to a report by GlobeNewswire, the global Machine Learning and Reinforcement Learning market was valued at US$ 9.9 billion in 2019, and is projected to reach US$ 14.7 billion by 2025, growing at a Compound Annual Growth Rate (CAGR) of 6.5% between 2020 and 2025. The major driving factors in Machine Learning and Reinforcement Learning market are the increasing need for business strategy planning, machine learning and data processing, to create training systems that provide custom instructions and materials according to need, as well as in robotics and aircraft control. Reinforcement Learning is a course that provides the methods and procedures to solve very complex problems, which cannot be solved by conventional techniques. The methods of Reinforcement learning are preferred for achieving long-term results, which otherwise can be a very difficult goal to achieve. The Reinforcement Learning method is based on human learning. This course is useful for those interested in learning Artificial Intelligence using Reinforcement Learning methods. Hands-On A virtual hands-on environment is integrated within the course. Students will have to leverage this environment to complete the industry assignment as well as to complete Part B of the summative assessment.