The current approach may face limitations in handling a wider range of reinforcement learning environments and tasks due to several factors. One limitation is the scalability of the clustering method to large state and action spaces, which can lead to computational inefficiencies and increased complexity. To address this, exploring hierarchical clustering techniques or distributed computing frameworks can improve scalability and efficiency. Another limitation is the reliance on manual hyperparameter tuning, which can be addressed by implementing automated hyperparameter optimization algorithms such as Bayesian optimization or evolutionary strategies. Furthermore, the generalizability of the clustering method across diverse environments may be limited, necessitating the development of adaptive clustering algorithms that can dynamically adjust to different task requirements and complexities. Incorporating domain-specific knowledge or expert guidance into the clustering process can also enhance its adaptability and performance across varied reinforcement learning scenarios.