the differences are between the three types of learning（supervised, unsupervised and reinforcement）
监督学习、无监督学习和强化学习的区别
supervised learning sort of takes the form of function approximation where you‘re given a bunch of x, y pairs And your goal is to finda function f that will map some new x to a proper y
监督学习是通过对有标签数据进行学习，找到一个能很好拟合函数，对新样本x能得到一个最准确的y（以尽可能正确地对训练集以外的示例标签进行预测）
Unsupervised learning is very similar to supervised learning except that it turns out that you‘re given a bunch of x‘s and your goal is to find some f. That gives you a compact description of the set of x‘s that you‘ve seen. So we call this clustering, or description as opposed to function approximation
无监督学习和监督学习类似，根据大量的无标签训练样本找到最佳拟合函数
reinforcement learning looks a lot like Supervised learning, in that we‘re going to be given a string of pairs of data, and we‘re going to try to learn some functions. But in the function approximation case, a supervized learning case, we were given a bunch of X and Y pairs. We were asked to learn F, but in reinforcement learning, we were given something totally different.Were instead going to be given x‘s and z‘s, and reinforcement learning is one mechanism for doing decision making.
强化学习看起来和监督学习类似，我们试图从一些数据对中学习一些函数。但监督学习的逼近函数是对x,y对而言，而强化学习是一些决策机制。