In this section we examine in detail the two compositions that best demonstrate the benefits of RL over a fixed policy. With Y2O3, the fixed Original policy is the most efficient of the three approaches. However, as Uniform is 2.3 times slower than the Original policy, RL-CSP does need time to optimize it (recall that RL-CSP begins with the Uniform policy) and reach the performance of Original. RL-CSP improves the starting policy significantly as its successful 12 runs took at most 56% of the steps required by Uniform. We observe that RL-CSP learns an efficient policy. Fig. illustrates that after the first ~30,000 steps approximately the same number of runs of RL-CSP have found the global minimum as those of Original, demonstrating the ability of RL to reproduce the efficiency of the well-tuned policy despite starting from a clearly unsuited policy. By the end of the runs, RL-CSP closes the gap, being only 1.3 times slower than Original (Fig. ).