Free Book about Deep-Learning approaches for Chess (like AlphaZero, Leela Chess Zero and Stockfish NNUE)

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Deep Learningchessdeep-learningalphazeronnue
Overview

Neural Networks For Chess

cover

Free Book

  • Grab your free PDF copy HERE
  • Buy a printed copy at HERE or HERE

Donations are welcome:

paypal

Contents

AlphaZero, Leela Chess Zero and Stockfish NNUE revolutionized Computer Chess. This book gives a complete introduction into the technical inner workings of such engines.

The book is split into four chapters:

  1. The first chapter introduces neural networks and covers all the basic building blocks that are used to build deep networks such as those used by AlphaZero. Contents include the perceptron, back-propagation and gradient descent, classification, regression, multilayer perpectron, vectorization techniques, convolutional netowrks, squeeze and exciation networks, fully connected networks, batch normalization and rectified linear units, residual layers, overfitting and underfitting.

  2. The second chapter introduces classical search techniques used for chess engines as well as those used by AlphaZero. Contents include minimax, alpha-beta search, and Monte Carlo tree search.

  3. The third chapter shows how modern chess engines are designed. Aside from the ground-breaking AlphaGo, AlphaGo Zero and AlphaZero we cover Leela Chess Zero, Fat Fritz, Fat Fritz 2 and Effectively Updateable Neural Networks (NNUE) as well as Maia.

  4. The fourth chapter is about implementing a miniaturized AlphaZero. Hexapawn, a minimalistic version of chess, is used as an example for that. Hexapawn is solved by minimax search and training positions for supervised learning are generated. Then as a comparison, an AlphaZero-like training loop is implemented where training is done via self-play combined with reinforcement learning. Finally, AlphaZero-like training and supervised training are compared.

Source Code

Just clone this repository or directly browse the files. You will find here all sources of the examples of the book.

About

During COVID, I worked a lot from home and saved approximately 1.5 hours of commuting time each day. I decided to use that time to do something useful (?) and wrote a book about computer chess. In the end I decided to release the book for free.

Profits

To be completely transparent, here is what I make from every paper copy sold on Amazon. The book retails for $16.95 (about 15 Euro).

  • printing costs $4.04
  • Amazon takes $6.78
  • my royalties are $6.13

Errata

If you find mistakes, please report them here - your help is appreciated!

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Comments
  • 'Board' object has no attribute 'outcome'

    'Board' object has no attribute 'outcome'

    I just executed python mcts.py and received an error message: 34 0 Traceback (most recent call last): File "mcts.py", line 134, in payout = simulate(node) File "mcts.py", line 63, in simulate while(board.outcome(claim_draw = True) == None): AttributeError: 'Board' object has no attribute 'outcome'

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  • Invalid Reduction Key auto.

    Invalid Reduction Key auto.

    Thank you for the source code of Chapter 5. I executed python mnx_generateTrainingData.py - OK Then python sup_network.py - OK

    Then I executed python sup_eval.py and got the error :

    Traceback (most recent call last): File "sup_eval.py", line 6, in model = keras.models.load_model("supervised_model.keras") File "/home/barvinog/anaconda3/lib/python3.7/site-packages/keras/engine/saving.py", line 492, in load_wrapper return load_function(*args, **kwargs) File "/home/barvinog/anaconda3/lib/python3.7/site-packages/keras/engine/saving.py", line 584, in load_model model = _deserialize_model(h5dict, custom_objects, compile) File "/home/barvinog/anaconda3/lib/python3.7/site-packages/keras/engine/saving.py", line 369, in _deserialize_model sample_weight_mode=sample_weight_mode) File "/home/barvinog/anaconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py", line 75, in symbolic_fn_wrapper return func(*args, **kwargs) File "/home/barvinog/anaconda3/lib/python3.7/site-packages/keras/engine/training.py", line 229, in compile self.total_loss = self._prepare_total_loss(masks) File "/home/barvinog/anaconda3/lib/python3.7/site-packages/keras/engine/training.py", line 692, in _prepare_total_loss y_true, y_pred, sample_weight=sample_weight) File "/home/barvinog/anaconda3/lib/python3.7/site-packages/keras/losses.py", line 73, in call losses, sample_weight, reduction=self.reduction) File "/home/barvinog/anaconda3/lib/python3.7/site-packages/keras/utils/losses_utils.py", line 156, in compute_weighted_loss Reduction.validate(reduction) File "/home/barvinog/anaconda3/lib/python3.7/site-packages/keras/utils/losses_utils.py", line 35, in validate raise ValueError('Invalid Reduction Key %s.' % key) ValueError: Invalid Reduction Key auto.

    opened by barvinog 2
  • Chapter 2 convolution.py

    Chapter 2 convolution.py

    Hello Dominik, I'm a Python novice, but an experienced chess player and long ago a developer of software for infinite dimensional optimization. I've installed the latest Python on a 64 cores Ryzen Threadripper with two NVIDIA 3090 graphic cards. I study your very helpful overview of modern chess engine programming and started with Chapter 2 where except convolution.py all examples work fine. I have installed module scikit-image as skimage doesn't load correctly. Then (without changing the source of convolution.py) I get the following warning

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