AI solution

Let's start by reminding ourselves of the whole deep Q-learning model, while adapting it to this case study, so that you don't have to scroll or turn many pages back into the previous chapters. Repetition is never bad; it sticks the knowledge into our heads more firmly. Here's the deep Q-learning algorithm for you again:

Initialization:

1. The memory of the experience replay is initialized to an empty list, called memory in the code (the dqn.py Python file in the Chapter 11 folder of the GitHub repo).
2. We choose a maximum size for the memory, called max_memory in the code (the dqn.py Python file in the Chapter 11 folder of the GitHub repo).

At each time t (each minute), we repeat the following process, until the end of the epoch:

1. We predict the Q-values of the current state . Since five actions can be performed (0 == Cooling 3°C, 1 == Cooling 1.5°C, 2 == No Heat Transfer, 3 == Heating 1.5°C, 4 == Heating...