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an adaptation of Gian-Carlo Pascutto's Leela Zero Go project [1] to Chess, using Stockfish's board representation and move generation. No heuristics or prior knowledge are carried over from Stockfish. The goal to build a strong UCT chess AI following the same type of deep learning techniques of AlphaZero as described in DeepMind's paper [2], but using distributed training for the weights of the deep residual convolutional neural network. The training process requires CUDA and a GPU accelerated version of Tensorflow installed [3].

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  1. ^ GitHub - gcp/leela-zero: Go engine with no human-provided knowledge, modeled after the AlphaGo Zero paper
  2. ^ 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. arXiv:1712.01815
  3. ^ leela-chess/README.md at master · glinscott/leela-chess · GitHub

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