In this methodology, rewards are evaluated using two metrics: the improvement in the R 2 score or mean relative error (MRE) between predicted and actual adsorption data. The reward (𝑟) is calculated based on the difference in MRE or R 2 between states t and t+1, reflecting the enhancement in predictive accuracy upon adding a selected data point to the training set. These rewards drive the update of Q-values within the training loop, influencing the agent's decisionmaking process.