Rubik cube New Deep Learning Algorithm Solves Rubik’s Cube

Solving (and attempting to solve) Rubik’s Cube has delighted millions of puzzle lovers since 1974 when the cube was invented by Hungarian sculptor and architecture professor Erno Rubik. Now, a group of UC Irvine researchers has developed a new algorithm – Autodidactic Iteration – able to solve Rubik’s Cube with no human assistance. The work is an advance in what’s called deep reinforcement learning (DRL), a form of DL that combines classic reinforcement learning, deep learning, and Monte Carlo Tree Search (MCTS).

“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,” write the researchers in their – Solving the Rubik’s Cube Without Human Knowledge – published in May on arXiv.com. They called the resulting solver, appropriately, DeepCube.

Rubik’s Cube is a member of a class of problems whose solution has proven difficult for DRL because there are a large number of states and only one reward state. In this instance, Rubik’s Cube has a large state space, with approximately 4.3 × 10¹⁹different possible configurations. The lack of many ‘reward states’ makes it difficult to develop a solving strategy.