For CH₄ adsorption in Cu-BTC, the optimal hyperparameter combination was determined to be a learning rate (𝛼) of 0.1, a discount factor (𝛾) of 0.5, and an exploration/exploitation (ɛ) value of 0.9, with 10 episodes. This was done by a sensitivity analysis based on the hyperparameters which result in the lowest MREs. To understand the influence of Q-learning hyperparameters on the selection of the training dataset and its consequent impact on the final MRE of the predictive model, we conducted a sensitivity analysis as depicted in Figure in the supporting information (SI). Our findings revealed that the most favorable outcomes, in terms of lowest MRE were consistently achieved when employing an epsilon value of 0.9. This observation remained consistent across varying combinations of learning rates and discount factors. The epsilon value of 0.9 indicates that the RL agent engaged in exploration 90% of the time while predominantly exploiting known information 10% of the time.