New deep learning technique solves Rubik's Cube without assistance
technologyreview.com
technologyreview.com
They probably were referring to the "quarter turn metric" which is 26 quarter-turn moves, but "moves" in speedcubing almost always refers to face turns. And this is made more confusing by the article since the article they linked to was regarding face turns as well.
Both of course are mentioned at cube20.org [2]...
> In chess, by contrast, there is a relatively large search space but each move can be evaluated and rewarded accordingly. That just isn’t the case for the Rubik’s Cube.
I don't follow this at all -- how do you reward an individual chess move other than by using a reward system that may or may not be anywhere near fully accurate like counting material? Seems pretty similar to solving a Rubik's cube.
That said, I think I agree with you. The Rubik's cube is amusing because it literally is just permuting numbers. You can build a transversal set where you quite literally just pick the permutations in order to get back to sorted. I haven't gone through the effort of building up the transversal set, yet, but http://taeric.github.io/cube-permutations-1.html was where I started playing with the idea.
I mean, yes, you can get more complicated. And no, I have not gone through the effort yet to see how much time the set takes to build up. Pretty sure it won't take long, though.
https://en.wikipedia.org/wiki/Computer_chess#Leaf_evaluation
If you just count material and look 30 moves ahead you will not get outvalued by a human. The reward system does not need to be perfect to be useful, but there does need to be one.
As an avid speedcuber I'd be very interested in the datasets.
Results are compared with known optimal algorithms for the Rubik's cube: Kociemba's algorithm, Korf's algorithm.
https://en.wikipedia.org/wiki/Optimal_solutions_for_Rubik%27...
I logged in on a desktop just to respond to this comment in particular, even though I almost always use my phone, if that helps put this in perspective.
Other than that, awesome paper.
This exact same article / link was already posted a day ago: https://news.ycombinator.com/item?id=17320218
Plus another link, plus the original research link.