========== Cribbage

.. image:: https://travis-ci.org/wroberts/cribbage.svg?branch=master :target: https://travis-ci.org/wroberts/cribbage

.. image:: https://coveralls.io/repos/wroberts/cribbage/badge.svg?branch=master :target: https://coveralls.io/r/wroberts/cribbage?branch=master :alt: Test code coverage

Python library for experimenting with the game of cribbage.

C Extension

There is a C function which can score a cribbage hand 100 times faster than doing it in pure Python.

To compile the Cython extension::

python setup.py build_ext --inplace

Then::

from cribbage import _cribbage_score
_cribbage_score.score_hand(hand, draw)

Testing

In the base directory of the project, do::

py.test

References

1. Mnih, Volodymyr, et al. Human-level control through deep reinforcement learning_. Nature 518, no. 7540 (2015): 529-533.
2. van Hasselt, Hado. Double Q-learning_. Poster at Advances in Neural Information Processing Systems 23 (NIPS 2010), Vancouver, British Columbia, Canada.

.. _Human-level control through deep reinforcement learning: http://www.davidqiu.com:8888/research/nature14236.pdf .. _Double Q-learning: https://hadovanhasselt.files.wordpress.com/2015/12/doubleqposter.pdf

Links

• https://github.com/asrivat1/DeepLearningVideoGames/blob/master/Raw%20Game%20Code/tetris.py
• https://www.nervanasys.com/demystifying-deep-reinforcement-learning/
• https://www.nervanasys.com/deep-reinforcement-learning-with-neon/