drl-rubiks-cube

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drl-rubiks-cube

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Solving Rubik's Cube with Deep Reinforcement Learning, A* and visualize with PyQt5.

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# Solving Rubik's Cube with Deep Reinforcement Learning and A*.

<details>
  <summary>Table of Contents</summary>
  <ol>
    <li>
      <a href="#overview">Overview</a>
    </li>
    <li>
      <a href="#motivation">Motivation</a>
    </li>
    <li><a href="#install">Installation</a></li>
    <li><a href="#docker">Running Via Docker</a></li>
    <li><a href="#usage">Usage</a></li>
    <li><a href="#techs">Technologies Used</a></li>
    <li>
      <a href="#results">Results</a>
    </li>
    <li><a href="#aspect">Technical Aspect</a></li>
    <li><a href="#bug-feature">Bug / Feature Request</a></li>
    <li><a href="#to-do">To Do</a></li>
    <li><a href="#license">Lıcense</a></li>
    <li><a href="#reference">References</a></li>
  </ol>
</details>

## Overview
This project aimed to solve Rubik's Cube succesfully, using only AI methods.

For any question, please don't hesitate to contact me (bilenyakup97@gmail.com)

## Motivation

The Rubik's Cube has ***43,252,003,274,489,856,000*** different state spaces. Only one of them is the solved state. So this is an NP problem.

The motivation of this project is to solve a puzzle with such a large state space with the help of Neural Networks.

## Installation

This project is written in Python 3.9.

To download this repository, run this command in git bash.
```bash 
  git clone https://github.com/yakupbilen/drl-rubiks-cube
```

To install the required packages and libraries, run this command in the project directory after cloning the repository.

```bash 
  pip install -r requirements.txt 
```

## Running Via Docker
<div id="docker"></div>

```bash 
  docker build -t drl-rubiks-cube .
```
#### *Windows*
Install  <a href="https://sourceforge.net/projects/vcxsrv/" target="_blank">VcXsrv Windows X Server</a>. After you install X Server, launch X Server. 
In Extra Settings set checked 'Disable access control'.

```bash 
  docker run --rm -it -e DISPLAY=YourIpAdress:0.0 drl-rubiks-cube
```
#### *Lınux*
```bash 
  docker run --rm -e DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix drl-rubiks-cube
```
#### *Mac*
Install <a href="https://www.xquartz.org/" target="_blank">xQuartz</a>. Launch xQuartz, preferences->security set checked "Allow connections from network clients".
```bash 
  xhost + YourIpAddress
```
```bash 
  docker run --rm -e DISPLAY=YourIpAddress:0 -v /tmp/.X11-unix:/tmp/.X11-unix drl-rubiks-cube
```

## Usage

Before training the model, you can make changes in the config(.ini) file that you will send to the program 
as a parameter or you can add new model architecture to models file.

You must to run all of the below codes in this section from project directory.

To train the neural network, you need to run the "run_train.py" file. ***To avoid long hours running***: 
after each run, trained neural network is saved to models/modelname/tmp/model_last.dat.
In each run, program read the trained neural network model and continue to train the model. 
***This process provide us to train the neural network multiple times with short times.***

To train neural network model
```bash 
  python run_train.py -p "path/train_config.ini"
```

To analyze checkpoints
```bash 
  python run_checkpoints_evaluate.py -p "path/analysis_config.ini"
```

To find best parameter for A*
```bash 
  python run_search_tune.py -p "path/analysis_config.ini"
```

To solve Rubik's cube.
```bash 
  python run_solve.py -p "path/solve_config.ini"
```

## Technologies Used

PyTorch             |  NumPy
:-------------------------:|:-------------------------:
[![torch](https://miro.medium.com/max/160/1*IMGOKBIN8qkOBt5CH55NSw.png)](https://pytorch.org)  |  [![numpy](https://upload.wikimedia.org/wikipedia/commons/thumb/3/31/NumPy_logo_2020.svg/160px-NumPy_logo_2020.svg.png)](https://numpy.org)

## Technical Aspect

This project is divided into three part:

-  Rubik's Cube
    - Defining Rubik's Cube with numpy.
    - Defining moves with numpy indexing.
-  Neural Network Model
    - Training Neural Network.
    - Analyzing and Finding best model among the checkpoints.
-  Solve
    - Solving Rubik's Cube with A*

## Results

Neural network trained on Colab with free Tesla GPUs.

Scrambled cubes are solved with i5-8250u.

![15result](https://drive.google.com/uc?export=view&id=1Yyo1GVGCdqA2PkKRu8KT_u5Rh2qbKMsC)

![100result](https://drive.google.com/uc?export=view&id=1ATr6Yktqr04_PMWPtLwOqjBUzgfMppTc)

## Bug / Feature Request

If you find a bug kindly open an issue <a href="https://github.com/yakupbilen/drl-rubiks-cube/issues/new" target="_blank">here</a> by the expected result.

If you'd like to request a new function, feel free to do so by opening an issue <a href="https://github.com/yakupbilen/drl-rubiks-cube/issues/new" target="_blank">here</a>.

## To Do

- Train neural network with more data.
- Try different neural network architecture and parameters for better results.
- Implement 3D rubik's cube input from webcam

## License

Distributed under the MIT License. See LICENSE.txt for more information.

## References

- *McAleer, S., Agostinelli, F., Shmakov, A., & Baldi, P. (2018). Solving the Rubik's cube without human knowledge.* arXiv preprint arXiv:1805.07470
- *Agostinelli, F., McAleer, S., Shmakov, A., & Baldi, P.(2019). Solving The Rubik’s Cube With Deep Reinforcement Learning And Search. Nature Machine Intelligence Vol 1 August 2019 356-363*

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