In this study, we initially apply the Q-learning framework across eight distinct systems involving methane (CH4) and carbon dioxide (CO2) within two MOFs: Cu-BTC and IRMOF-1, at a temperature of 298 K. This approach enables us to explore the applicability of the Q-learning framework across different systems, providing insights into its versatility and effectiveness. Subsequently, we extend our analysis by applying RL to a broader set of MOFs from the CoRE MOF database for both gases, further expanding the scope of our investigation and evaluating the generalizability of our approach.