The verification of Unmanned Aerial Systems (UAS) trajectory generation in uncertain environments is a critical challenge, particularly when using reinforcement learning (RL) methods. The unpredictability of RL models, together with the lack of formal verification standards for UAS and AI systems, complicates the safety and reliability of autonomous decision-making. This paper proposes a learning framework as a solution towards improving the verifiability of RL-based trajectory generation. We propose an incremental training process where the RL model is progressively trained in more complex and uncertain scenarios. By controlling the complexity of training environments, the framework can guide the system towards safer, more predictable behaviour, making it easier to verify its performance under varying conditions. This approach also helps reduce the risks associated with RL's non-deterministic nature by creating a better understanding of robustness and stability in the model's decision-making process. Additionally, we discuss the current means of certifying airborne software and the need for new standards and methodologies to ensure that learning-based systems can be rigorously evaluated for safety and operational readiness in dynamic, real-world environments.