This is super cool! As soon as I read the AlphaGo Zero paper I wanted to do a reimplementation as well, but apply the strategy to train a chess engine instead.

This is super cool! As soon as I read the AlphaGo Zero paper I wanted to do a reimplementation as well, but apply the strategy to train a chess engine instead. Performance turned out to be way more important than I anticipated (I haven't built many big projects before) and I've been less inclined to work on it recently since I realized the necessary computing resources to train it properly would cost hundreds if not thousands of dollars in Google Cloud GPU time. I also wrote it in Python because I've done all of my TensorFlow work in Python so far. https://github.com/emdoyle/chess_ai https://github.com/emdoyle/chess_ai That's my repo, if anyone wants to help me make it faster or possibly fix parts of it (hard to be sure the MCTS and NN are faithfully replicated) I would welcome pull requests! Also I gitignored my tensorflow serving stuff but it's basically required to be able to serve the models and communicate via RPC to get predictions (the RPC client is visible in the repo).