  There exists a Classification accuracy gap of about 20% between our best
methods of generating Unsupervisedly Learned Representations and the accuracy
rates achieved by (naturally Unsupervisedly Learning) humans. We are at our
fourth decade at least in search of this class of paradigms. It thus may well
be that we are looking in the wrong direction. We present in this paper a
possible solution to this puzzle. We demonstrate that Reinforcement Learning
schemes can learn representations, which may be used for Pattern Recognition
tasks such as Classification, achieving practically the same accuracy as that
of humans. Our main modest contribution lies in the observations that: a. when
applied to a real world environment (e.g. nature itself) Reinforcement Learning
does not require labels, and thus may be considered a natural candidate for the
long sought, accuracy competitive Unsupervised Learning method, and b. in
contrast, when Reinforcement Learning is applied in a simulated or symbolic
processing environment (e.g. a computer program) it does inherently require
labels and should thus be generally classified, with some exceptions, as
Supervised Learning. The corollary of these observations is that further search
for Unsupervised Learning competitive paradigms which may be trained in
simulated environments like many of those found in research and applications
may be futile.
