“Solving the Rubik’s Cube Without Human Knowledge”, Stephen McAleer, Forest Agostinelli, Alexander Shmakov, Pierre Baldi2018-05-18 (AlphaGo; similar)⁠:

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.

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