README.md

metadata

license: mit
library_name: pytorch
tags:
  - rubiks-cube
  - reinforcement-learning
  - deepcubea
  - pytorch
pipeline_tag: other

DeepCube — Cube3 (3×3×3) cost-to-go network

PyTorch weights for a DeepCubeA-style cost-to-go network that solves the 3×3×3 Rubik's cube via weighted A*.

• Input: one-hot encoded cube state (324 dims = 54 stickers × 6 colors)
• Output: scalar cost-to-go estimate (predicted moves to solved state)
• Architecture: MLP — see deepcube/model.py in the source repo
• Training: Approximate Value Iteration on random scrambles (see train.ipynb)
• Source code: https://github.com/ac1982/deepcube

Files

• deepcube_cube3.pt — final weights (keys: net, cfg, loss_hist, elapsed)

Usage

from huggingface_hub import hf_hub_download
from deepcube.model import load_checkpoint

ckpt_path = hf_hub_download("alexever/deepcube-cube3", "deepcube_cube3.pt")
model, cfg = load_checkpoint(ckpt_path)

Or via the bundled server, which auto-loads from checkpoints/deepcube_cube3.pt:

huggingface-cli download alexever/deepcube-cube3 deepcube_cube3.pt \
    --local-dir checkpoints
python -m deepcube.server

License

MIT — see source repository.