BabyAI is a platform used to study the sample efficiency of grounded language acquisition, created at Mila.
To replicate or compare against our current baseline results, we recommend you use the BabyAI 1.1 branch and cite both:
TODO: technical report details
and the ICLR19 paper, which details the experimental setup and BabyAI 1.0 baseline results. Its source code is in the iclr19 branch:
@inproceedings{
babyai_iclr19,
title={Baby{AI}: First Steps Towards Grounded Language Learning With a Human In the Loop},
author={Maxime Chevalier-Boisvert and Dzmitry Bahdanau and Salem Lahlou and Lucas Willems and Chitwan Saharia and Thien Huu Nguyen and Yoshua Bengio},
booktitle={International Conference on Learning Representations},
year={2019},
url={https://openreview.net/forum?id=rJeXCo0cYX},
}
This README covers instructions for installation and troubleshooting. Other instructions are:
- Instructions on how to contribute
- Codebase Structure
- Training, Evaluation and Reproducing Baseline Results
- BabyAI 1.0+ levels and older levels.
If you are using conda, you can create a babyai environment with all the dependencies by running:
git clone https://github.com/mila-iqia/babyai.git
cd babyai
conda env create -f environment.yaml
source activate babyai
After that, execute the following commands to setup the environment.
cd ..
git clone https://github.com/maximecb/gym-minigrid.git
cd gym-minigrid
pip install --editable .
The last command installs the repository in editable mode. Move back to the babyai repository and install that in editable mode as well.
cd ../babyai
pip install --editable .
Finally, follow these instructions
Requirements:
- Python 3.6+
- OpenAI Gym
- NumPy
- PyTorch 0.4.1+
- blosc
First install PyTorch for on your platform.
Then, clone this repository and install the other dependencies with pip3:
git clone https://github.com/mila-iqia/babyai.git
cd babyai
pip3 install --editable .
Finally, follow these instructions
Add this line to .bashrc (Linux), or .bash_profile (Mac).
export BABYAI_STORAGE='/<PATH>/<TO>/<BABYAI>/<REPOSITORY>/<PARENT>'
where /<PATH>/<TO>/<BABYAI>/<REPOSITORY>/<PARENT> is the folder where you typed git clone https://github.com/mila-iqia/babyai.git earlier.
Models, logs and demos will be produced in this directory, in the folders models, logs and demos respectively.
If you run into error messages relating to OpenAI gym or PyQT, it may be that the version of those libraries that you have installed is incompatible. You can try upgrading specific libraries with pip3, eg: pip3 install --upgrade gym. If the problem persists, please open an issue on this repository and paste a complete error message, along with some information about your platform (are you running Windows, Mac, Linux? Are you running this on a Mila machine?).
If you cannot install PyQT using pip, another option is to install it using conda instead:
conda install -c anaconda pyqt
Alternatively, it is also possible to install PyQT5 manually:
wget https://files.pythonhosted.org/packages/98/61/fcd53201a23dd94a1264c29095821fdd55c58b4cd388dc7115e5288866db/PyQt5-5.12.1-5.12.2-cp35.cp36.cp37.cp38-abi3-manylinux1_x86_64.whl
PYTHONPATH=""
pip3 install --user PyQt5-5.12.1-5.12.2-cp35.cp36.cp37.cp38-abi3-manylinux1_x86_64.whl
Finally, if none of the above options work, note that PyQT is only needed to produce graphics for human viewing, and isn't needed during training. As such, it's possible to install BabyAI without PyQT and train a policy. To do so, you can comment out the gym_minigrid dependency in setup.py, clone the gym-minigrid repository manually, and comment out the pyqt5 dependency in the setup.py of the minigrid repository.
Please note that the default observation format is a partially observable view of the environment using a compact encoding, with 3 input values per visible grid cell, 7x7x3 values total. These values are not pixels. If you want to obtain an array of RGB pixels as observations instead, use the RGBImgPartialObsWrapper. You can use it as follows:
import babyai
from gym_minigrid.wrappers import *
env = gym.make('BabyAI-GoToRedBall-v0')
env = RGBImgPartialObsWrapper(env)
This wrapper, as well as other wrappers to change the observation format can be found here.