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.

中文翻译：

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无监督学习的表示：任务应该结束吗？

我们生成无监督学习表示的最佳方法与（自然无监督学习）人类实现的准确率之间存在大约 20% 的分类准确率差距。我们至少在寻找这类范式的第四个十年。因此，我们很可能正朝着错误的方向寻找。我们在本文中提出了这个难题的可能解决方案。我们证明了强化学习方案可以学习表征，这些表征可用于模式识别任务，例如分类，实现与人类几乎相同的准确性。我们的主要贡献在于观察到： a．当应用于现实世界环境（例如自然本身）时，强化学习不需要标签，因此可以被认为是长期寻求的、准确性有竞争力的无监督学习方法的自然候选者，以及 b．相比之下，当强化学习应用于模拟或符号处理环境（例如计算机程序）时，它本质上确实需要标签，因此通常应将其归类为监督学习，但有一些例外情况。这些观察结果的必然结果是，进一步寻找可以在模拟环境中训练的无监督学习竞争范式可能是徒劳的。一个计算机程序）它本质上确实需要标签，因此通常应该归类为监督学习，但有一些例外。这些观察结果的必然结果是，进一步寻找可以在模拟环境中训练的无监督学习竞争范式可能是徒劳的。一个计算机程序）它本质上确实需要标签，因此通常应该归类为监督学习，但有一些例外。这些观察结果的必然结果是，进一步寻找可以在模拟环境中训练的无监督学习竞争范式可能是徒劳的。