A digital twin is a virtual representation or simulation of a real object, system or process. It captures the data and behaviour of its physical counterpart in real time, enabling continuous monitoring, analysis and optimisation. In the context of reinforcement learning (RL) for business use cases, a digital twin is critical because it provides a safe and controlled environment for training RL agents. By simulating the business process or environment in a digital twin, RL algorithms can explore and learn from interactions without risking real-world consequences. This enables more efficient learning, faster experimentation and fine-tuning of decision strategies, leading to improved performance and optimised results when the RL agent is used in the actual business context. The digital twin reduces the risks associated with using RL, minimises potential disruptions and helps organisations make informed decisions, making it a valuable resource when applying RL to solve complex business challenges.