Problem to solve

When I said we were going to solve a real-world business problem, I didn't overstate the problem; the problem we're about to tackle with deep Q-learning is very similar to the following, which was solved in the real world via deep Q-learning.

In 2016, DeepMind AI minimized a big part of Google's yearly costs by reducing the Google Data Center's cooling bill by 40% using their DQN AI model (deep Q-learning). Check the link here:

https://deepmind.com/blog/deepmind-ai-reduces-google-data-centre-cooling-bill-40

In this case study, we'll do something very similar. We'll set up our own server environment, and we'll build an AI that controls the cooling and heating of the server so that it stays in an optimal range of temperatures while using the minimum of energy, therefore minimizing the costs.

Just as the DeepMind AI did, our goal will be to achieve at least 40% energy savings! Are you ready for this? Let's bring...