Anyone who has lived through the 1980s knows how maddeningly difficult it is to solve a Rubik's Cube, and to accomplish the feat without peeling the stickers off and rearranging them. Apparently the six-sided contraption presents a special kind of challenge to modern deep learning techniques that makes it more difficult than, say, learning to play chess or Go. That used to be the case, anyway. Researchers from the University of California, Irvine, have developed a new deep learning technique that can teach itself to solve the Rubik's Cube. What they come up with is very different than an algorithm designed to solve the toy from any position. It's easy enough to take such an algorithm and then automate the process, but that kind of solution is limited in scope and utility.

Deep-learning machines have figured out how to master games like chess or Mortal Kombat. Now, computer scientists at the University of California, Irvine taken things to the third dimension by creating an algorithm that can figure out how to solve a Rubik's Cube, a surprisingly difficult change. "Our algorithm is able to solve 100 percent of randomly scrambled cubes while achieving a median solve length of 30 moves - less than or equal to solvers that employ human domain knowledge," say the scientists in the abstract to their paper, up on Arvix. The algorithm, called DeepCube, uses what's known as "autodidactic iteration," a form of machine learning developed by the authors of the paper. The big challenge of autodidactic iteration was to allow machines to find their own rewards in solving a puzzle, a goal they can reach.

Deep-learning machines have figured out how to master games like chess or Mortal Kombat. Now, computer scientists at the University of California, Irvine taken things to the third dimension by creating an algorithm that can figure out how to solve a Rubik's Cube, a surprisingly difficult change. "Our algorithm is able to solve 100 percent of randomly scrambled cubes while achieving a median solve length of 30 moves -- less than or equal to solvers that employ human domain knowledge," say the scientists in the abstract to their paper, up on Arvix. The algorithm, called DeepCube, uses what's known as "autodidactic iteration," a form of machine learning developed by the authors of the paper. The big challenge of autodidactic iteration was to allow machines to find their own rewards in solving a puzzle, a goal they can reach.

A generally intelligent agent must be able to teach itself how to solve problems in complex domains with minimal human supervision. Recently, deep reinforcement learning algorithms combined with self-play have achieved superhuman proficiency in Go, Chess, and Shogi without human data or domain knowledge. In these environments, a reward is always received at the end of the game; however, for many combinatorial optimization environments, rewards are sparse and episodes are not guaranteed to terminate. We introduce Autodidactic Iteration: a novel reinforcement learning algorithm that is able to teach itself how to solve the Rubik's Cube with no human assistance. Our algorithm is able to solve 100% of randomly scrambled cubes while achieving a median solve length of 30 moves -- less than or equal to solvers that employ human domain knowledge.