joatmon 1.0.1rc1

General Purpose Python Library

joatmon (jack of all trades, master of none)

What is included?

As of today, the following algorithms have been implemented:

As of today, the following environments have been implemented:

• Rubick's Cube
• 2048 Puzzle
• Sokoban
• Game of 15
• Chess

As of today, the following networks have been implemented:

• DQN [1]
• DDPG [2]

You can find more information in the doc.

Installation

• Install joatmon from Pypi (recommended):

pip install joatmon

Install from Github source:

git clone https://github.com/malkoch/joatmon.git
cd joatmon
python setup.py install

Examples

If you want to run the examples, you'll also have to install:

• gym by OpenAI: Installation instruction

Once you have installed everything, you can try out a simple example:

python examples/sokoban_dqn.py
python examples/sokoban_ddpg.py

How to run the tests

To run the tests locally, you'll first have to install the following dependencies:

pip install pytest pytest-xdist pep8 pytest-pep8 pytest-cov python-coveralls

You can then run all tests using this command:

py.test tests/.

If you want to check if the files conform to the PEP8 style guidelines, run the following command:

py.test --pep8

If you want to check the code coverage, run the following command:

py.test --cov=joatmon tests/

References

1. Mnih, Volodymyr, et al. "Playing atari with deep reinforcement learning." arXiv preprint arXiv:1312.5602 (2013).
2. Lillicrap, Timothy P., et al. "Continuous control with deep reinforcement learning." arXiv preprint arXiv:1509.02971 (2015).
3. Fujimoto, Scott, Herke van Hoof, and David Meger. "Addressing function approximation error in actor-critic methods." arXiv preprint arXiv:1802.09477 (2018).
4. Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems. 2012.
5. Goodfellow, Ian, et al. "Generative adversarial nets." Advances in neural information processing systems. 2014.
6. Mirza, Mehdi, and Simon Osindero. "Conditional generative adversarial nets." arXiv preprint arXiv:1411.1784 (2014).
7. Karras, Tero, et al. "Progressive growing of gans for improved quality, stability, and variation." arXiv preprint arXiv:1710.10196 (2017).
8. He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
9. Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. "U-net: Convolutional networks for biomedical image segmentation." International Conference on Medical image computing and computer-assisted intervention. Springer, Cham, 2015.
10. Simonyan, Karen, and Andrew Zisserman. "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556 (2014).