Therefore, lower efficiency of the problem solver implies slower learning. . The larger number of macros indicates a larger number of training problems before quiescence. The mean length of the macros acquired with the MD heuristic is twice as large as the mean length of macros acquired with the RR heuristic. Even more significant is the difference in the maximal length of a macro. This indicates that the maximal radius of the MD heuristic is much larger than that of the RR heuristic.
Larger radius implies more resources invested in escaping from local minima.
The above explanation is only partially satisfactory, since it does not tell us the source of the problem. Examples for macros acquired with the RR and the MD heuristics. Each example shows the state where the macro was acquired, the macro itself, and the state after the application of the acquired macro. It is more difficult to explain the larger number of macros. This implies that the likelihood of a macro acquired with MD to be useful in a state is much lower than that of a macro acquired with RR .