A set of games designed for testing deep RL agents.
If you use this code, or otherwise are inspired by our white-box testing approach, please cite our NeurIPS workshop paper:
@inproceedings{foley2018toybox,
title={{Toybox: Better Atari Environments for Testing Reinforcement Learning Agents}},
author={Foley, John and Tosch, Emma and Clary, Kaleigh and Jensen, David},
booktitle={{NeurIPS 2018 Workshop on Systems for ML}},
year={2018}
}
Watch four minutes of agents playing each game. Both ALE implementations and Toybox implementations have their idiosyncracies, but the core gameplay and concepts have been captured. Pull requests always welcome to improve fidelity.
The rust implementations of the games have moved to a different repository: toybox-rs/toybox-rs
Go into the ctoybox
directory, and use the start_python
script. This will help set up your path and virtual-environments.
pip install ctoybox pygame
python -m ctoybox.human_play breakout
python -m ctoybox.human_play amidar
python -m ctoybox.human_play space_invaders
- Navigate to
ctoybox
. - Run
pip3 install -r REQUIREMENTS.txt
- Run
PYTHONPATH=baselines:toybox python3 -m unittest toybox.sample_tests.test_${GAME}.${TEST_NAME}
Tensorflow, OpenAI Gym, OpenCV, and other libraries may or may not break with various Python versions. We have confirmed that the code in this repository will work with the following Python versions:
- 3.5
./scripts/utils/start_images --help