Q-learning, also known as Quality learning, treats the selection of training points as a dynamic decision-making process. Within this framework, the RL agent, representing the exploration strategy, learns to iteratively choose training points based on the sequential feedback received from the GP model, thereby optimizing its predictions over the agent's experience. This approach not only enhances the efficiency of predicting adsorption isotherms within MOFs but also offers a fresh perspective on leveraging RL in data-driven materials science. The integration of Qlearning into the RL-driven design framework signifies a departure from traditional methodologies, highlighting the model's adaptability to the complexities inherent in MOF adsorption studies. This research paves the way for new frontiers at the intersection of RL and materials science, demonstrating the potential of Q-learning to redefine our approach to training predictive models for intricate phenomena like adsorption in MOFs.