Reinforcement Learning (RL) has its roots in behavioural psychology and early work on learning theories, but its modern development can be traced to the pioneering work of researchers such as Arthur Samuel in the 1950s and Richard Sutton in the 1980s. Samuel's pioneering work in developing a self-learning checker game programme laid the foundation for the trade-off between exploration and exploitation and for learning from interactions in RL. Sutton's research in time-difference learning and Q-learning algorithms further developed RL methods. With the integration of neural networks in the 1990s and significant breakthroughs in Deep Reinforcement Learning in the early 2010s, the field continued to evolve, as evidenced by the success of DeepMind's DQN algorithm in learning Atari games. As computing power and data availability increased, RL found applications in various business domains. Its usefulness in business use cases became clear as RL algorithms demonstrated impressive capabilities in optimising online advertising, recommender systems, dynamic pricing, inventory management and other decision-making problems with complex and uncertain environments, leading to its adoption and exploration in numerous business environments.