Chess AI fully based on artificial neural networks. Intended to be trained against itself.
Stockcheese is pure artifical neural network following the actor-critic paradigm and doesn't rely on explicit/brute-force/"heuristic" monte carlo tree search. Since the critic not only learns the value function of the current state, but also the value function for a sequence of states: aka the monte carlo tree search function, thanks to game sequence processing layers within the architecture among other details.
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Contains both dense and sparse rewards.
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Unlike the standard actor-critic, the critic sents its ouput to the actor as a feedback in both training and inference, as the actor doesn't learn the the monte carlo tree search function, only to match state to action.
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Draw is punished proportionally to how high is win rate, if wins are scarce, draw is seen as progress. With high win rates, the opposite.
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Exploration is rewarding for wins, allowing SC to learn to explore better trees.
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Faster wins are more rewarding. However, slow losses and draws are not more punishing.
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Loss function transitions from squared to absolute error as lower error is achieved.
- git clone
- pip install -r requirements.txt
(Not yet) See save_path = "source/Stockcheese_weights.hd5" for trained weights.
Tensorflow/Keras is very easy to customize and extend, there are examples covering almost every case and the documentation is clearer and wider.