Reinforcement learning (RL) is a unique learning paradigm inspired by the behaviour of animals and humans to learn to solve tasks autonomously. Learning occurs through interactions with an environment, exploring, and evaluating strategies under various conditions. RL excels in complex environments, can handle delayed consequences, and is able to learn solely from experience without access to an explicit model of the system. This makes RL particularly promising for particle accelerators, where the dynamic conditions of particle beams and accelerator systems require continuous adaptation, and modelling is challenging. Although RL applications are emerging in accelerator physics and showing promising results, their widespread introduction faces critical challenges. Among the main obstacles are the effective formulation of control problems, training, and the deployment of solutions in real systems. This paper provides an overview of the potential of RL in accelerator applications, highlighting current challenges and future research directions.