Given the established Q-learning parameters for CO2 from IRMOF-1 and Cu-BTC, the RL scheme is applied to the same CoRE MOFs as in the previous sections. Figure below shows the isotherms of some selected MOFs for CO2. The first four frames indicate the best performance in terms of lowest MRE(s) and the next four frames are the least performing cases in terms of highest MRE(s). In the case of CO2, our analysis of the combined actual and RL-based GP predictions across all MOFs yielded a R 2 value of 0.99, as depicted in Figure below. The MAE stood at 2.393 cm3 (stp)/gr framework. The differences in R and MAE values between CO2 and CH4 can be attributed to the stronger interactions of CO2, leading to more complex adsorption isotherms with sharper transitions. This complexity poses a challenge for accurate prediction, potentially resulting in higher MAE value, compared to CH4. Upon closer examination, as depicted in Figure , we analyzed the critical points sampled by the RL agent, revealing a discernible pattern in their distribution across the pressure input space. Primarily, these data points congregated around the boundaries of the pressure range, particularly in the low-pressure regions. It is noteworthy that the agreement in critical points of sampling between CO2 and CH4 is notable, sampled data points clustered around the boundaries of the pressure range, particularly favoring regions characterized by low and high pressures. This uniformity suggests a systematic exploration strategy adopted by the RL agents, regardless of the specific adsorbate. The convergence in sampling behavior underscores the robustness of the RL-guided exploration approach in identifying critical regions within the pressure space with significant implications for adsorption behavior.