[arbre]

I disagree. If you read the atari paper you will get plenty of details and you can infer how it is applied to electricity consumption. They were using reinforcement learning. The algorithms would learn to get a better score by looking at the screen and sending actions accordingly. Here you could imagine the same algorithm with energy consumption as a score, a set of datacenter metrics as the screen (state) and change of metrics as actions.

[argonaut]

Errr... No. Just no. Deep reinforcement learning is not some pixie dust that magically works for any problem with a reward function that you throw it at. It's astounding how commenters on HN think this is all "easy".

[dmos62]

This quickly went to you say this, I say this. These are very interesting statements, it would be nice if they were supported by citations.

[argonaut]

The poster I was replying to is really the one who needs to prove that deep RL is super easy like they were claiming.

[arcanus]

> Here you could imagine the same algorithm with energy consumption as a score, a set of datacenter metrics as the screen (state) and change of metrics as actions.

What you have described is easily instantiated with any numerical optimization technique of the last 40 years. The devil for any of these problems is in the details.