In the previous article , we explored the limitations of backpropagation and why it is not ideal when the correct output values are unknown. In this article, we will begin exploring the core ideas behind reinforcement learning. Starting Example Let us begin by assuming that we are not hungry . We will feed the value 0.0 into the neural network. The neural network outputs a probability of 0.5 for going to Place B . So: Probability of going to Place B = p(B) = 0.5 Probability of going to Place A = 1 - p(B) = 0.5 Visualizing the Probabilities We can represent these probabilities using a line. First, we draw a line segment with length 0.5 to represent the probability of going to Place A . Then, we append another line segment to represent the probability of going to Place B . Together, these form a line ranging from 0 to 1 . Choosing an Action To decide which place to go for a snack, we randomly pick a number between 0 and 1 . Let us pick 0.2 .…