rubiks-cube-gym 0.5.2

OpenAI Gym environments for various twisty puzzles

RubiksCubeGym

An OpenAI Gym environment for various twisty puzzles.

Currently available environments:

• 2x2x2 Pocket Rubik's Cube
• Pyraminx
• Skewb

Citation

@article{hukmani2021solving,
  title={Solving Twisty Puzzles Using Parallel Q-learning.},
  author={Hukmani, Kavish and Kolekar, Sucheta and Vobugari, Sreekumar},
  journal={Engineering Letters},
  volume={29},
  number={4},
  year={2021}
}

Details:

2x2x2 Pocket Rubik's Cube

Action Space	Discrete(3)
Observation Space	Discrete(3674160)
Actions	F, R, U
Rewards	(-inf, 100]
Max steps	250
Reward Types	Base, Layer By Layer Method, Ortega Method
Render Modes	'human', 'rgb_array', 'ansi'

Pyraminx without tips

Action Space	Discrete(4)
Observation Space	Discrete(933120)
Actions	L, R, U, B
Rewards	(-inf, 100]
Max steps	250
Reward Types	Base, Layer by Layer Method
Render Modes	'human', 'rgb_array', 'ansi'

Skewb

Action Space	Discrete(4)
Observation Space	Discrete(3149280)
Actions	L, R, U, B
Rewards	(-inf, 100]
Max steps	250
Reward Types	Base, Sarah's Method(Advanced)
Render Modes	'human', 'rgb_array', 'ansi'

Installation

Via PyPI

pip install rubiks-cube-gym

Or build from source

git clone https://github.com/DoubleGremlin181/RubiksCubeGym.git
cd RubiksCubeGym
pip install -e .

Requirements

• gym
• numpy
• opencv-python
• wget

Scrambling

You can pass the scramble as a parameter for the reset function self.reset(scramble="R U R' U'")

The scramble should follow the WCA Notation

Example

import gym  
import rubiks_cube_gym  
  
env = gym.make('rubiks-cube-222-lbl-v0')  
env.reset(scramble="R U R' U' R' F R2 U' R' U' R U R' F'")  
  
for _ in range(4):  
    env.render()  
    print(env.step(1))  
env.render(render_time=0)  
env.close()

(3178426, -26, False, {'cube': array([ 0,  9,  2, 15,  4,  5,  6, 21, 16, 10,  1, 11, 12, 13, 14, 23, 17, 7,  3, 19, 20, 18, 22,  8], dtype=uint8), 'cube_reduced': 'WRWGOOGYRBWBOOGYRGWBYBYR'})
(1542962, -1, False, {'cube': array([ 0, 21,  2, 23,  4,  5,  6, 18, 17, 16, 15, 11, 12, 13, 14,  8,  7, 10,  9, 19, 20,  3, 22,  1], dtype=uint8), 'cube_reduced': 'WYWYOOGBRRGBOOGRGBRBYWYW'})
(1682970, -1, False, {'cube': array([ 0, 18,  2,  8,  4,  5,  6,  3,  7, 17, 23, 11, 12, 13, 14,  1, 10, 16, 21, 19, 20,  9, 22, 15], dtype=uint8), 'cube_reduced': 'WBWROOGWGRYBOOGWBRYBYRYG'})
(2220193, 25, False, {'cube': array([ 0,  3,  2,  1,  4,  5,  6,  9, 10,  7,  8, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23], dtype=uint8), 'cube_reduced': 'WWWWOOGRBGRBOOGGRRBBYYYY'})

You can find my implementation and results using Parallel Q-learning here.