2. There was already a Google engineer back in 2012 who applied neural networks to this problem and saw huge gains (There is a blog post about this somewhere). In the deepmind blog post, they don't compare to this system, do for all we know it could just be a small refinement of that system. It is actually not clear whether deep RL was actually used or not from the deepminds blog post: it is only kind of implied.
"Say you’re a data center architect working at Google. It’s your job to make sure everything runs efficiently and coolly. To date, you’ve achieved that by designing the system so that you’re running as few pieces of cooling equipment at once as possible — you turn on the second piece only after the first is maxed out, and so on. This makes sense, right? Well, a variant of AlphaGo named Dr. Data disagreed.
“What Dr. Data decided to do was actually turn on as many units as possible and run them at a very low level,” Hassabis says. “Because of the switching and the pumps and the other things, that turned out to be better — and I think they’re now taking that into new data center designs, potentially. They’re taking some of those ideas and reincorporating them into the new designs, which obviously the AI system can’t do. So the human designers are looking at what the AlphaGo variant was doing, and then that’s informing their next decisions.” Dr. Data is at work right now in Google’s data centers, saving the company 40 percent in electricity required for cooling and resulting in 15 percent overall less energy usage."
I am somewhat convinced that something resembling RL was used based on your argument in the other thread, but I think even you would agree that calling it "a variant of alphago" is a pretty big stretch.
Where would that be? As far as I know, the DM blog post and Hassabis's occasional discussions are the most detailed public information available. And they don't mention that it's just a brief demo.
> 2. There was already a Google engineer back in 2012 who applied neural networks to this problem and saw huge gains (There is a blog post about this somewhere)
I don't remember this.
https://blog.google/topics/environment/deepmind-ai-reduces-e...
(Just nitpicking here.)
They haven't gone into detail as to how exactly this is done, but my guess is that they use their historical data series on cooling/electricity consumption to bootstrap and then it learns online normally with policy gradients (since cooling and temperature control seem like something with continuous actions rather than a few discrete ones, so an actor-critic rather than DQN).