For CH₄ adsorption in Cu-BTC, we determined the optimal combination of hyperparameters to be α = 0.01, γ = 0.5, and ɛ = 0.1. Remarkably, the algorithm achieved an R 2 exceeding 0.99, leading to action termination by the 4th episode (Figure ). Sensitivity analysis on Q-learning Similarly, CO₂ adsorption predictions in Cu-BTC and IRMOF-1 required an extended number of Qlearning episodes. For Cu-BTC, the criterion for a final R 2 ≥ 0.99 was met by the sixth episode (Figure , SI). Sensitivity analysis indicated an optimal model with ɛ = 0.9, favoring exploration over exploitation. Similar results were observed for IRMOF-1, achieving a final R 2 ≥ 0.99 by the sixth episode with ɛ = 0.9 (Figure , SI).