In edge computing applications, it is a common practice to employ cloud resources for data processing. However, latency-sensitive applications encounter hurdles due to limitations in network bandwidth and the latency associated with cloud data processing. Efficient task scheduling algorithms play a crucial role in effectively allocating resources for executing workflows in edge computing environments and tackling associated challenges. This paper aims to evaluate the performance of the Q-learning algorithm in edge computing, with a specific emphasis on the Montage workflow as a case study. Our simulation environment compares the Q-learning algorithm against traditional scheduling approaches using the overall workflow task completion time and energy consumption performance metrics. Our experimental findings offer valuable insights into the efficacy of the Q-learning algorithm within edge computing environments.