# Minimalistic Grid World Environment (MiniGrid)

Simple and minimailistic grid world environment for OpenAI Gym.

## Installation

Clone this repository and install the other dependencies with `pip3`:

```
git clone https://github.com/maximecb/gym-minigrid.git
cd gym-minigrid
pip3 install -e .
```

Optionally, if you wish use the reinforcement learning code included
under [/basicrl](/basicrl), you can install its dependencies as follows:

```
cd basicrl

# PyTorch
conda install pytorch torchvision -c soumith

# OpenAI baselines
git clone https://github.com/openai/baselines.git
cd baselines
pip install -e .

# Other requirements
pip install -r requirements.txt
```

Note: the basicrl code is a custom fork of [this repository](https://github.com/ikostrikov/pytorch-a2c-ppo-acktr),
which was modified to work with this environment.

## Usage

To run the standalone UI application, which allows you to manually control the agent with the arrow keys:

```
./standalone.py
```

The environment being run can be selected with the `--env-name` option, eg:

```
./standalone.py --env-name MiniGrid-Fetch-8x8-v0
```

To see available environments and their implementation, look at [simple_envs.py](gym_minigrid/envs/simple_envs.py).

Basic reinforcement learning code is provided in the `basicrl` subdirectory.
You can perform training using the ACKTR algorithm with:

```
python3 basicrl/main.py --env-name MiniGrid-Empty-8x8-v0 --no-vis --num-processes 32 --algo acktr
```

You can view the result of training using the `enjoy.py` script:

```
python3 basicrl/enjoy.py --env-name MiniGrid-Empty-8x8-v0 --load-dir ./trained_models/acktr
```