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- W4287124695 abstract "Existing combinatorial search methods are often complex and require some level of expertise. This work introduces a simple and efficient deep learning method for solving combinatorial problems with a predefined goal, represented by Rubik's Cube. We demonstrate that, for such problems, training a deep neural network on random scrambles branching from the goal state is sufficient to achieve near-optimal solutions. When tested on Rubik's Cube, 15 Puzzle, and 7$times$7 Lights Out, our method outperformed the previous state-of-the-art method DeepCubeA, improving the trade-off between solution optimality and computational cost, despite significantly less training data. Furthermore, we investigate the scaling law of our Rubik's Cube solver with respect to model size and training data volume." @default.
- W4287124695 created "2022-07-25" @default.
- W4287124695 creator A5071949014 @default.
- W4287124695 date "2021-06-06" @default.
- W4287124695 modified "2023-09-30" @default.
- W4287124695 title "Self-Supervision is All You Need for Solving Rubik's Cube" @default.
- W4287124695 doi "https://doi.org/10.48550/arxiv.2106.03157" @default.
- W4287124695 hasPublicationYear "2021" @default.
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