A deep learning Crazyhouse chess program that uses a Monte Carlo Tree Search (MCTS) based evaluation system and reinforcement to enhance its play style. Created as an individual project by a high school student.
Information on the inner workings and the code breakdown can be found on the blog post here.
Side Note: Parts of the code are yet to be optimized since project is still in progress.
This is a framework of a neural network-based Crazyhouse Chess Engine inspired by the procedures specified by Google DeepMind and their multiple papers on AlphaGo and AlpahZero. This project was initiated in June 15, 2018, following the successful framework of a self-learning Tic Tac Toe engine that managed to solve the game in an hour. You may find the repository for that here; however, that project has yet to be annotated for the general public to use.
Currently, this repository is designed for three processes: supervised learning (training a network model based on PGN master games), reinforcement learning (having the best network play itself to generate training games, from which this data can be used to train better networks), as well as self-learning (start with a randomly initialized or even a pre-trained Pytorch model if desired, from which training games and new networks will be created for however long the user desires.)
As of now, due to the lack of computational resources readily available for me, the focus is on creating a trained model based on top available games. From this documentation, I will explain the whole process of training a model and sending it back for further testing.
Your name will be added on the list of contributors once you have uploaded a relevant file. :)
The latest training dataset contains 11.2 million moves played by Stockfish in 124,872 games. More will be generated soon. The total database of 11.2 million moves are saved as binary arrays and require only 65 MB of storage! They can be found on here
It will be necessary to download the following packages:
python-chess
scipy
numpy (if that's somehow not on your computer)
torch (pytorch)
torchvision
pathlib
As this program was written, tested, and edited on the PyCharm CE IDE, I also recommend that users also download the interpreter and run the code on it. However, I acknowledge that it is possible to run any of the attached files through terminal/other IDEs, provided that all the necessary libraries are downloaded. Python 3 (3.6) is strongly recommended.
Once these are all running on your computer, then you will be able to continue to the next step!
Start off by downloading the repository through terminal...
git clone https://github.com/FTdiscovery/64CrazyhouseDeepLearning.git
... or by downloading the ZIP file on the top right hand corner.
You can learn more about the training checkpoints, parameters, and others on the wiki!
The input for the neural network is a basic (raw) representation of the board. It is an array of 960 values (15 planes of 8×8 boards), which determines the player turn, position of each piece type, captive pieces held by a player, and whether the piece is a promotion. The last part is relevant since promoted pieces can only be placed down as pawns after being captured.
The output for the neural network is a 1D representation of multiple move planes (8×8 boards). The current action is represented by an array of (5+1+56+8) x (8×8) + 3×8 = 4504 values but is currently being edited and tested to work with a vector of 2308 values (deleting 2196 useless entries). This can still be theoretically edited.
The engine is now UCI Compatible and online on https://lichess.org/@/SixtyFourEngine! It has reached a top rating of 2278 and is welcome to play with all of you. :)
GAME 1: 1. e4 e6 2. d4 d5 3. e5 Ne7 4. Nf3 Nf5 5. Bd3 Nc6 6. O-O Be7 7. c3 O-O 8. Bf4 f6 9. Nbd2 fxe5 10. dxe5 P@e4 11. P@d6 exf3 12. Nxf3 Bxd6 13. exd6 P@e4 14. Bxe4 dxe4 15. P@g5 exf3 16. gxf3 N@h4 17. B@h3 N@e2+ 18. Kh1 B@g2+ 19. Bxg2 Nxg2 20. Kxg2 Nh4+ 21. Kh1 B@g2# 0-1
GAME 2: 1. e4 Nc6 2. Nc3 Nf6 3. Nf3 d5 4. exd5 Nxd5 5. Bc4 e6 6. O-O Nb6 7. Bb5 Bd7 8. Re1 Be7 9. d4 O-O 10. P@h6 gxh6 11. Bxh6 P@e4 12. P@g7 exf3 13. Qxf3 P@g4 14. Qxg4 N@f5 15. gxf8=Q# 1-0
GAME 3: 1. e4 Nc6 2. Nc3 Nf6 3. Nf3 d5 4. exd5 Nxd5 5. d4 e6 6. Bb5 Bb4 7. Bd2 Bxc3 8. bxc3 N@e4 9. @b2 Nxd2 10. Qxd2 B@h5 11. O-O-O O-O 12. Kb1 @g4 13. Ng5 Bg6 14. Nxf7 Rxf7 15. Bxc6 bxc6 16. N@h6+ gxh6 17. Qxh6 Rf6 18. N@e7+ Kh8 19. B@g7# 1-0
Below is a list of contributors to the project: