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env

Rubik's Cube Environment

This module defines the Cube3 class representing a 3x3x3 Rubik's Cube in the Half-Turn Metric.

Class: Cube3: A class for 3x3x3 Rubik's Cube in Half-Turn Metric.

Cube3​

class Cube3()

A class for 3x3x3 Rubik's Cube in Half-Turn Metric (HTM).

This class provides methods to manipulate and solve a 3x3x3 Rubik's Cube using the half-turn metric. It defines the cube's initial and goal states, available moves, and methods for cube manipulation.

Representation:

Order of faces:

   0
 2 5 3 4
   1

Order of stickers on each face:

  2   5   8
  1   4   7
 [0]  3   6

Indices of state (each starting with 9 * (n-1)):

                2   5   8
                1   4   7
               [0]  3   6
  20  23  26   47  50  53  29  32  35  38  41  44
  19  22  25   46  49  52  28  31  34  37  40  43
 [18] 21  24  [45] 48  51 [27] 30  33 [36] 39  42
               11  14  17
               10  13  16
               [9] 12  15

Colors (indices // 9):

                0   0   0
                0   0   0
                0   0   0
   2   2   2    5   5   5   3   3   3   4   4   4
   2   2   2    5   5   5   3   3   3   4   4   4
   2   2   2    5   5   5   3   3   3   4   4   4
                1   1   1
                1   1   1
                1   1   1

Attributes:

  • state ndarray - Current cube state represented as an array of sticker colors.
  • GOAL ndarray - Fixed goal state represented as an array of sticker colors.
  • moves list - List of possible cube moves (face and direction).
  • allow_wide bool - Flag indicating whether wide moves are allowed.
  • sticker_target dict - A dictionary mapping move strings to lists of target sticker indices.
  • sticker_source dict - A dictionary mapping move strings to lists of source sticker indices.
  • sticker_target_ix ndarray - A 2D numpy array mapping move indices to target sticker indices for normal moves.
  • sticker_source_ix ndarray - A 2D numpy array mapping move indices to source sticker indices for normal moves.
  • sticker_target_ix_wide ndarray - A 2D numpy array mapping move indices to target sticker indices for wide moves.
  • sticker_source_ix_wide ndarray - A 2D numpy array mapping move indices to source sticker indices for wide moves.

show​

def show(flat=False, palette=["white", "yellow", "orange1", "red", "blue", "green"])

Display the cube's current state.

Arguments:

  • flat bool - Whether to display the state in flat form.
  • palette list - List of colors for representing stickers.

validate​

def validate(state=None, centered=True)

Validate the cube's state and arrangement.

Arguments:

  • centered bool - Whether centers should be centered or not.

Raises:

  • ValueError - If the cube's state or arrangement is invalid.

reset​

def reset()

Resets the cube state to the solved state.

reset_axes​

def reset_axes()

Reset color indices according to the given center colors. Useful when fat moves are applied or when an unexpected perspective is specified.

is_solved​

def is_solved()

Checks if the cube is in the solved state.

finger​

def finger(move)

Apply a single move on the cube state using move string.

Arguments:

  • move str - Move string in HTM notation.

finger_ix​

def finger_ix(ix)

Apply a single move using move index for faster execution.

Arguments:

  • ix int - Index of the move.

apply_scramble​

def apply_scramble(scramble)

Applies a sequence of moves (scramble) to the cube state.

Arguments:

  • scramble str or list - Sequence of moves in HTM notation or list.

__vectorize_moves​

def __vectorize_moves()

Vectorizes the sticker group replacement operations for faster computation. This method defines self.sticker_target and self.sticker_source to manage sticker colors (target is replaced by source). They define indices of target and source stickers so that the moves can be vectorized.

Dataset​

class Dataset(torch.utils.data.Dataset)

Pseudo dataset class to infinitely yield random scrambles.

Example
batch_size = 1024
dl = get_dataloader(batch_size)
for i, (batch_x, batch_y) in zip(range(1000), dl):
    batch_x, batch_y = batch_x.to(device), batch_y.device().reshape(-1)

get_dataloader​

def get_dataloader(batch_size, num_workers=min(os.cpu_count(), 32), max_depth=20, **dl_kwargs)

Create a DataLoader instance for generating random Rubik's Cube scrambles.

Arguments:

  • batch_size int - The number of samples per batch.
  • num_workers int, optional - The number of worker processes to use for data loading. Defaults to the number of CPU cores or 32 (beyond which the return will diminish), whichever is smaller.
  • max_depth int, optional - The maximum depth of the scrambles. Defaults to 20.
  • **dl_kwargs - Additional keyword arguments to pass to the DataLoader constructor.

Returns:

  • torch.utils.data.DataLoader - A DataLoader instance that yields batches of random scrambles.