Solving the Rubik's Cube with Deep Reinforcement Learning and Search

Forest Agostinelli

Abstract:

The Rubik’s cube is a prototypical combinatorial puzzle that has a large state space with a single goal state. The goal state is
unlikely to be accessed using sequences of randomly generated moves, posing unique challenges for machine learning. We
solve the Rubik’s cube with DeepCubeA, a deep reinforcement learning approach that learns how to solve increasingly difficult
states in reverse from the goal state without any specific domain knowledge. DeepCubeA solves 100% of all test configurations,
finding a shortest path to the goal state 60.3% of the time. DeepCubeA generalizes to other combinatorial puzzles and
is able to solve the 15 puzzle, 24 puzzle, 35 puzzle, 48 puzzle, Lights Out and Sokoban, finding a shortest path in the majority
of verifiable cases.