In the realm of ML, various methods have been utilized to screen MOFs and discern their adsorption behavior. These methods encompass support vector machines (SVMs) , neural networks , random forests , among others. However, this paper directs its focus towards one particularly potent approach-reinforcement learning (RL). RL stands out by enabling an agent to learn from sequential experiences and adjust its strategies through trial and error, showcasing distinct capabilities for effectively exploring the extensive landscape of MOFadsorption studies.