Summary of Solving Rubik’s Cube Without Tricky Sampling, by Yicheng Lin and Siyu Liang
Solving Rubik’s Cube Without Tricky Sampling
by Yicheng Lin, Siyu Liang
First submitted to arxiv on: 29 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a novel reinforcement learning (RL) algorithm that solves the Rubiks Cube from fully scrambled states without relying on near-solved-state sampling. The algorithm uses policy gradient methods and employs a neural network to predict cost patterns between states, allowing the agent to learn directly from scrambled states. This approach differs from previous RL methods that start with a partially solved state. The model was tested on the 2x2x2 Rubiks Cube and successfully solved it in over 99.4% of cases using only the policy network without relying on tree search. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves the Rubiks Cube using a machine learning algorithm. It’s hard to solve the cube because there are many possible states and not all of them lead to solving the puzzle. The algorithm uses a neural network to figure out which moves are most important for solving the cube, even if it starts with a completely scrambled state. This is different from how humans usually solve the cube, which involves using techniques like “F2L” (first two layers) and searching for specific pieces. The algorithm was tested on a smaller version of the Rubiks Cube and worked over 99% of the time. |
Keywords
» Artificial intelligence » Machine learning » Neural network » Reinforcement learning
