In this study, we introduced a new approach that integrates Q-learning with GPs to optimize A pivotal aspect of our approach lies in the strategic sampling of critical points by the RL agent within the pressure input space. By systematically exploring the pressure space, our methodology shows that there exist critical and most informative pressure regions -low-and high-pressure regions that are just enough to develop an accurate adsorption model across all MOFs. Our findings underscore the significance of systematically probing these regions. The general RL code for both the MRE and R 2 reward metrics, and the isotherms generated through AL for both CH4