When compared with either fixed policy, RL-CSP is faster over the whole test set of compositions within the phase field (Fig. ), as judged by the sum of Nmean for all of the compositions, using fewer than 68% and 54% of the steps required by the Uniform and Original fixed policies respectively. The effect of learning across the phase field is more pronounced for more challenging compositions (for the Uniform policy) based on the strictly decreasing numbers in the last column of Fig. . This leads to improved absolute savings of computational resources for the compositions for which they matter the most.