Authors - Marco Torres-Umana, Ernesto Rivera-Alvarado Abstract - This paper presents a reinforcement learning (RL) approach for validating graphical user interfaces (GUIs) on networking devices. Traditional methods, including manual and automated approaches, face challenges in scalability, efficiency, and adaptability. The proposed RL solution generates test cases dynamically, exploring diverse GUI states and behaviors without relying on pre-established models or extensive data. By leveraging internal and external device observations and encoding techniques, the RL agent effectively navigates GUIs. Results demonstrate high solution similarity and shorter convergence times across various configurations, enhancing test coverage while minimizing manual effort. Future work will refine reward definitions, tackle larger state spaces, and extend the system to support additional devices and vendor interfaces.
