DevPage: Moritz Willig
CrazyAra
CrazyAra is an open-source neural network based engine for the chess variant Crazyhouse
[1]. The engine was written by Johannes Czech, Alena Beyer and me during a semester
project at the Technische Universität Darmstadt as part of course "Deep Learning: Architectures & Methods" in the summer semester 2018 . At the time of writing this article the engine stays at an elo of 2594 at lichess.org
[2].
The engine is inspired by the Alpha-(Go)-Zero papers of Silver, Hubert, Schrittwieser et al
[3].
The neural network got trained in a superived fashion using the lichess.org data set
[4]. Out of the games played between January 2016 to June 2018, we selected all matches where both players had an elo of 2000 or above, resulting in 569,537 games to train on. Further details about the training can be found in the project wiki
[5].
Additionally the model architecture from the original paper was adapted to get a better overall performance and improve training convergence. We combined multiple well-known techniques to fit the requirements for the problem of predicting crazyhouse chess moves. The exact architecture of our 'RISE'-Network (ResneXt - Inception - Squeeze-Excitation) is described on the wiki
[6].
Additional Links:
[1]
Repository
https://github.com/QueensGambit/CrazyAra/
[2]
CrazyAra on lichess
https://lichess.org/@/CrazyAra
References
[3] Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm, David Silver, Thomas Hubert, Julian Schrittwieser et al. (bibtex)
[4] database.lichess.org/
[5] https://github.com/QueensGambit/CrazyAra/wiki/Supervised-training
[6] https://github.com/QueensGambit/CrazyAra/wiki/Model-architecture
BibTex
@article{DBLP:journals/corr/abs-1712-01815,
author = {David Silver and
Thomas Hubert and
Julian Schrittwieser and
Ioannis Antonoglou and
Matthew Lai and
Arthur Guez and
Marc Lanctot and
Laurent Sifre and
Dharshan Kumaran and
Thore Graepel and
Timothy P. Lillicrap and
Karen Simonyan and
Demis Hassabis},
title = {Mastering Chess and Shogi by Self-Play with a General Reinforcement
Learning Algorithm},
journal = {CoRR},
volume = {abs/1712.01815},
year = {2017},
url = {http://arxiv.org/abs/1712.01815},
archivePrefix = {arXiv},
eprint = {1712.01815},
timestamp = {Mon, 13 Aug 2018 16:46:01 +0200},
biburl = {https://dblp.org/rec/bib/journals/corr/abs-1712-01815},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
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