The proposed approach can be extended to handle more complex palletization scenarios by incorporating advanced techniques and strategies. To address heterogeneous box sizes, the action masking model can be enhanced to consider the weight and dimensions of each box, ensuring that stability is maintained regardless of the box characteristics. Dynamic arrival patterns can be accommodated by integrating a predictive element into the task planner, allowing it to anticipate and adapt to changing box sequences in real-time. Multiple robots working in coordination can be managed by introducing collaborative planning algorithms that enable communication and synchronization between the robots. This coordination can involve sharing information about planned actions, optimizing task allocation, and ensuring efficient use of shared resources. By incorporating these enhancements, the approach can effectively handle a wide range of complex palletization scenarios with varying box sizes, arrival patterns, and multiple robotic agents.