Pytorch models based on the book "Neural networks for Chess". Please go to the repo https://github.com/asdfjkl/neural_network_chess to check further details and where you can get the book (and you may also give a donation to the author). The models in the book are implemented in tensorflow, the aim of this repo is to provide an implementation of the models in chapter 5 in pytorch.

• The input files positions.npy, moveProbs.npy and óutcomes.npy are direct copies from the above mentioned repo (see details how to create them and their underlying logic there)
• the files rnf_mcts.pyand game.py are also directly copied, as they are needed in the training process.
• All other files have corresponding files in the original repo, but have been modified to have pytorch models instead of tensorflow models

To execute the code for the supervised learning:

• run sup_network.py for training and then sup_eval.py for evaluation

For the MCTS approach:

• initialise a random model with common/init_random_model.py
• train the model with rnf_train.py
• evaluate the model with rnf_eval.py