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Using Deep Reinforcement Learning and Search for the Rubik's cube

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# Solving Rubik's cube using Deep Reinforcement Learning

###### Second year project for Artificial Intelligence and Data, Technical University of Denmark.
![pytest_master](https://github.com/peleiden/rl-rubiks/workflows/pytest_master/badge.svg?branch=master)

## Demo and explanation
Visit the [Demo page](https://peleiden.github.io/rl-rubiks/) to see the trained Reinforcement Learning agents in action.
![](https://raw.githubusercontent.com/peleiden/rubiks-models/master/rubiks_eks.gif)
This page also includes  brief explanations of the methods and a short guide to set up the solver backend.

## Quick start
For the standard setup and use case, this should reproduce our results.
```
git clone https://github.com/peleiden/rl-rubiks
cd rl-rubiks
pip install -r rlrequirements.txt
python runtrain.py --config configs/main_train.ini
python runeval.py --config configs/main_eval.ini
```
For more instructions on reproducing the training and evaluation, read Installation and Usage below.

## Experiments
To limit the size of this repository, we have kept no models on it. Our trained models are available on the [model repository](https://github.com/peleiden/rubiks-models).

## Installation
1) Clone the repo:
    ```
    git clone https://github.com/peleiden/rl-rubiks
    cd rl-rubiks
    ```

2) Install requirements (use `pip3` if `pip` is not bound to your Python 3 binary):
    ```
    pip install -r rlrequirements.txt
    ```
    (There are also some optional dependencies: `GitPython` for saving commits in logs and `networkx` and `imageio` for visualizing values of initial states over time when running with `--analysis True`.)

3) (Only needed if you get `ImportError`) For the modules to work, you *might* need to add the main repo folder path to your `PYTHONPATH` environment variable.
    In \*nix-systems, you can run the following to achieve this:
    ```
    export PYTHONPATH=$PYTHONPATH:<path where you cloned>/rl-rubiks
    ```
    If your shell is `bash` with standard setup, this can be made permenant by running.
    ```
    echo "export PYTHONPATH=\$PYTHONPATH:<path where you cloned>/rl-rubiks" >> ~/.bashrc
    ```
    On Windows, you can follow some of the instructions in [this Stack Overflow thread](https://stackoverflow.com/questions/3701646/how-to-add-to-the-pythonpath-in-windows-so-it-finds-my-modules-packages).

## Usage
### Training the Deep Neural Network
The training can be started using `runtrain.py`. From the main repo folder, run
```
python runtrain.py --help
```
to show the possible options. In many cases, using the parameters from a configuration file is preferable.
```
python runtrain.py --config configs/main_train.ini
```

### Evaluation of Agents

To evaluate the performance of one or more agents, a solving experiment can be started using `runeval.py`. From the main repo folder, run
```
python runeval.py --help
```
to show options. This also works with configuration files; an example of which can be seen by running
```
python runeval.py --config configs/main_eval.ini
```

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