The sensitivity analysis of the Q-learning parameters reaffirmed that the best GP models were achieved at ɛ of 0. CO2 adsorption prediction in both Cu-BTC and IRMOF-1 were optimized in the same number of Q-learning episodes of 10, to achieve desirable outcomes. Specifically, in the case of Cu-BTC, the optimal hyperparameter combination was determined to be a 𝛼 of 0.01, 𝛾 of 0.5, and an ɛ value of 0.5, with 10 episodes. In Figure , a final MRE value of 0.03 was achieved after a transient increase in MRE was observed during the fourth episode, indicating the dynamic nature of the learning process. Furthermore, Figure showcases a comparison between GP-predicted and GCMC adsorption in Cu-BTC, demonstrating commendable agreement between the two. Figure provides insights into the pressure space sampled by the agent, highlighting a preference for exploring low-pressure regions. In Figure , we show a commendable prediction of the isotherm as compared to the GCMC simulated isotherm, indicating that the RL based GP can predict the isotherm. A subsequent sensitivity analysis conducted for the Cu-BTC adsorbate-adsorbent system unveiled that the optimal model emerged with ɛ of 0.5, suggesting an equality between exploration and exploitation in this context, as depicted in Figure .