Understanding Reinforcement Learning with Neural Networks Part 3: Guessing the Ideal Output

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 =