The Q-table, a critical component, maintains Q-values for each state-action pair. The Q-values represent the expected cumulative rewards the agent anticipates from choosing a specific action in each state. During exploration and exploitation, the agent relies on the Q-table to inform its decision-making. The action selected by the agent is often the one with the highest Q-value in each state. The dynamics of the Q-table are shaped by the Q-learning update rule, which incorporates immediate rewards and anticipated future rewards. The agent learns from sequential experiences, adjusting its strategies through trial and error. By maximizing the Qvalues, the agent hones its decision-making skills, progressively selecting actions that lead to higher rewards and, consequently, more accurate predictions of adsorption isotherms.