We compare the performances of different policies using Nmean; the mean number of steps required to find the global minimum among 40 runs (Fig. ). The numbers in Fig. show clear RL-CSP improvement over the starting policy, as it uses only 71% of the steps required by Uniform to find the global minimum. Due to learning sharing between runs the RL-CSP policy (Fig. ) can be efficiently learned even though the average number of steps per run is relatively small (~1500). Finally, RL-CSP essentially reproduces the performance of the Original policy, requiring just 4% more steps despite starting from Uniform.