The success of the iterative action masking learning framework can serve as a blueprint for applying similar iterative refinement strategies to other RL-based robotic planning and control problems. By iteratively updating and enhancing the action masking model based on the performance of the RL policy, the approach can adapt to changing environments and improve over time. This iterative process can be extended to tasks such as path planning, obstacle avoidance, and manipulation tasks, where the action space complexity poses a challenge to traditional RL methods. By iteratively refining the models and policies through feedback loops and continuous learning, robotic systems can become more adaptive, efficient, and reliable in dynamic and complex environments. This iterative approach can be a key factor in enhancing the performance and real-world applicability of RL-based robotic systems across a wide range of applications.