In this study, we utilize Q-learning as a strategic methodology to optimize the selection of training data points for predicting adsorption isotherms within MOFs. Q-learning, rooted in the principles of RL, operates within the framework of a Markov decision process (MDP), where initial and updated training data serve as states (𝑠), and the selection of training data points represents actions (𝑎). The fundamental objective is to adapt the Q-learning algorithm iteratively to improve the efficiency of predicting adsorption loading in MOFs.