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Algorithms such as Deep-Q-Network (DQN) use Convolutional Neural Networks (CNNs) to help the agent select the best action [9]. While these formulas are very complicated, these are usually the fundamental steps (Figure 1.4):

Figure 1.4 Value based learning.

1 Take the status picture, transform it to grayscale, and excessive parts are cropped.

2 Run the picture through a series of contortions and pooling in order to extract the important features that will help the agent make the decision.

3 Calculate each possible action’s Q-Value.

4 To find the most accurate Q-Values, conduct back-propagation.