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- W4285809980 abstract "Rubik’s cube is a famous mathematical puzzle invented by Ern Rubik in 1974. The goal of the puzzle is to restore the cube from a scrambled state to its original state, where each face is occupied by a single color. Human players focus on solving the Rubik’s cube in the shortest time using memorized algorithms, while researchers are more interested in the optimal solution, i.e., the fewest moves to solve the cube. Recently, reinforcement learning methods have been widely used to solve mathematical puzzles, including the Rubik’s cube. However, the benefits and drawbacks of using a learning method are unclear. This paper fills this gap by comprehensively comparing a reinforcement learning method with three traditional methods, namely breadth-first search, linear programming, and Dijkstra’s algorithm, for solving the pocket Rubik’s cube. Computational results show that Q-learning with fine-tuned parameters learns to solve the problem after 7.3 hours of training. In contrast, two traditional methods are found to be more efficient in solving the pocket cube. The proposed breadth-first search algorithm solves all possible states in 2 minutes with optimality guaranteed. The linear programming formulation solves all states in 12 minutes. In conclusion, the advantage of Q-learning is that it does not require domain knowledge and can scale to solve larger problems. However, traditional methods work better for solving the pocket Rubik’s cube." @default.
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- W4285809980 date "2022-09-01" @default.
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- W4285809980 title "Q-learning and traditional methods on solving the pocket Rubik’s cube" @default.
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- W4285809980 doi "https://doi.org/10.1016/j.cie.2022.108452" @default.
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