During the last two decades, the operation of the electrical grid has undergone significant changes. This evolution is closely tied to the integration of power electronics into distributed generation systems, which led to increased utilization of renewable energy and as a consequence mitigating climate change by lowering emissions. On the other hand, artificial intelligence plays a considerable role in shaping the development and progress of various technologies, such as the electrical grid. This work presents a study for the application of Reinforcement Learning (RL) tools in distributed generation systems. The objective of using RL is to address voltage perturbations in real time. RL will be useful to maximize the resilience of the system in the event of short circuits of a short duration and minimize the risk of disconnect.