The insights gained from the clustering of policy decisions can be leveraged to guide the design of more interpretable reinforcement learning architectures and training algorithms in several ways. Firstly, the identified clusters of policy decisions can serve as building blocks for constructing modular and interpretable policy architectures, where each cluster represents a coherent set of actions that contribute significantly to task performance. By structuring policies based on these clusters, interpretability and explainability can be enhanced, enabling stakeholders to understand the decision-making process of the agent more intuitively. Additionally, the clustered ranking method can inform the development of curriculum learning strategies, where policies are trained sequentially based on the importance of decision clusters, facilitating a more structured and efficient learning process. Moreover, the insights from clustering can guide the selection of diverse and representative training scenarios, promoting robustness and generalization in reinforcement learning models. By integrating the clustered policy insights into the design and training phases, the transparency and comprehensibility of reinforcement learning systems can be significantly improved.