Storing every individual experience in memory would be inefficient both in terms of amount of memory required and in terms of generalization time. Therefore, we store and only at discrete, evenly-spaced values of . That is, for a memory of size M (with M dividing evenly into 360 for simplicity), we keep values of and for . We store memory as an array ``Mem'' of size M such that Mem[n] has values for both and . Using a fixed memory size precludes using memory-based techniques such as K-Nearest-Neighbors (kNN) and kernel regression which require that every experience be stored, choosing the most relevant only at decision time. Most of our experiments were conducted with memories of size 360 (low generalization) or of size 18 (high generalization), i.e. M = 18 or M = 360. As will be seen from our results, the memory size had a large effect on the rate of learning.