Machine Figures Out Rubik's Cube Without Human Assistance

[Stephen McAleer and colleagues from the University of California, Irvine] have pioneered a new kind of deep-learning technique, called "autodidactic iteration," that can teach itself to solve a Rubik's Cube with no human assistance. The trick that McAleer and co have mastered is to find a way for the machine to create its own system of rewards. Here's how it works. Given an unsolved cube, the machine must decide whether a specific move is an improvement on the existing configuration. To do this, it must be able to evaluate the move. Autodidactic iteration does this by starting with the finished cube and working backwards to find a configuration that is similar to the proposed move. This process is not perfect, but deep learning helps the system figure out which moves are generally better than others. Having been trained, the network then uses a standard search tree to hunt for suggested moves for each configuration. 

The result is an algorithm that performs remarkably well. "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," say McAleer and co. That's interesting because it has implications for a variety of other tasks that deep learning has struggled with, including puzzles like Sokoban, games like Montezuma's Revenge, and problems like prime number factorization.