CrazyAra
A Deep Learning UCI-Chess Variant Engine written in C++ & Python :parrot:
Install / Use
/learn @QueensGambit/CrazyAraREADME
Contents
- Description
- Links
- Download
- Artworks
- Variants
- Documentation
- Compilation
- Acknowledgments
- Players
- Related
- Licence
- Publications
Description
CrazyAra is an open-source neural network chess variant engine, initially developed in pure python by Johannes Czech, Moritz Willig and Alena Beyer in 2018. It started as a semester project at the TU Darmstadt with the goal to train a neural network to play the chess variant crazyhouse via supervised learning on human data. The project was part of the course "Deep Learning: Architectures & Methods" held by Kristian Kersting, Johannes Fürnkranz et al. in summer 2018.
The development was continued and the engine ported to C++ by Johannes Czech. In the course of a master thesis supervised by Karl Stelzner and Kristian Kersting, the engine learned crazyhouse in a reinforcement learning setting and was trained on other chess variants including chess960, King of the Hill and Three-Check.
The project is mainly inspired by the techniques described in the Alpha-(Go)-Zero papers by David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timothy Lillicrap, Karen Simonyan, Demis Hassabis.
The training scripts, preprocessing and neural network definition source files are written in python and located at DeepCrazyhouse/src. There are two version of the search engine available: The initial version is written in python and located at DeepCrazyhouse/src/domain/agent. The newer version is written in C++ and located at engine/src.
CrazyAra is an UCI chess engine and requires a GUI (e.g. Cute Chess, XBoard, WinBoard) for convinient usage.
Links
- :fire: C++ engine
- :snake: Python engine
- :notebook_with_decorative_cover: CrazyAra paper
- :orange_book: Master thesis
- :earth_africa: Project website
- ♞ CrazyAra@lichess.org
- ♞ CrazyAraFish@lichess.org
- :cyclone: Neural network
- :wrench: Supervised learning
- :hammer_and_wrench: Reinforcement learning
Download
Binaries
We provide binary releases for the following plattforms:
Operating System | Backend | Compatible with --- | --- | --- Linux | CUDA 11.3, cuDNN 8.2.1, TensorRT-8.0.1 | NVIDIA GPUs Linux | MXNet 1.8.0, Intel oneAPI MKL 2021.2.0 | Intel CPUs Windows | CUDA 11.3, cuDNN 8.2.1, TensorRT-8.0.1 | NVIDIA GPUs Windows | MXNet 1.8.0, Intel oneAPI MKL 2021.2.0 | Intel CPUs Mac | MXNet 1.8.0, Intel oneAPI MKL 2021.2.0 | Mac-Books
The current CrazyAra release and all its previous versions can also be found at releases.
Models
The extracted model should be placed in the directory reltative to the engine executable.
The default directory is indicated and can be changed by adjusting the UCI-parameter Model_Directory.
More information about the different models can be found in the wiki.
Artworks
Drawn by Hanna Czech (2023).
Generated with custom stable diffusion model of professor Kristian Kersting.
Variants
Binaries and models are available for the following chess variants:
Documentation
For more details about the initial python version visit the wiki pages:
- Introduction
- Installation guide for python MCTS
- Supervised-training
- Model architecture
- Input representation
- Output representation
- Network visualization
- Engine settings
- Programmer's guide
- FAQ
- Stockfish-10: Crazyhouse-Self-Play
Compilation
Ins
