Changes

The goal is to build a strong [[UCT]] chess AI following the same type of [[Deep Learning|deep learning]] techniques of [[AlphaZero]] as described in [[DeepMind|DeepMind's]] paper <ref>[[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]] ('''2017'''). ''Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm''. [https://arxiv.org/abs/1712.01815 arXiv:1712.01815]</ref>, but using distributed training for the weights of the [[Neural Networks#Deep|deep]] [[Neural Networks#Residual|residual]] [[Neural Networks#Convolutional|convolutional neural network]]. The training process requires [https://en.wikipedia.org/wiki/CUDA CUDA] and a [[GPU]] accelerated version of [https://en.wikipedia.org/wiki/TensorFlow Tensorflow] installed <ref>[https://github.com/glinscott/leela-chess/blob/master/README.md leela-chess/README.md at master · glinscott/leela-chess · GitHub]</ref>. As  =lc0=Soon after the start of July project, some of the team led by [[Alexander Lyashuk]] started to rewrite the engine from scratch. The new engine dubbed '''lc0'''. was able to search 4-8 times faster than lczero on NVIDIA GPU’s <ref>[https://blog.lczero.org/2018, training /06/18/2-the-way-forward/ The Way Forward · Leela Chess Zero]</ref>. Training has changed to require lc0 instead of lczero <ref>[http://lczero.org/ LCZero]</ref>.