Introduction

In machine learning, there are three different learning paradigms: supervised learning, unsupervised learning, and reinforcement learning.

In supervised learning, also known as learning with a teacher, the network is provided with both the inputs and the respective desired outputs. For example, in the MNIST dataset, each image of the handwritten digit has a label signifying the digit value associated with it.

In reinforcement learning, also known as learning with a critic, the network is not provided with the desired output; instead, the environment provides a feedback in terms of reward or punishment. When its output is correct, the environment rewards the network, and when the output is not correct, the environment punishes it.

In unsupervised learning, also known as learning without a teacher, no information is provided to the network about its output. The network...