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by thaumasiotes
3973 days ago
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The best Go bot approach (as of some years ago, but it's not like neural networks are a new idea) uses a very different strategy. Specifically, the strategy of "identify a few possible moves, simulate the game for several steps after each move using a very stupid move-making heuristic instead of using this actual strategy recursively, and then pick the move that yielded the best simulated board state". |
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This is likely due to the way Go works , random playout provides a rough estimate of who controls what territory ( this is how Go is scored ).
Recently two deep-learning papers showed very impressive results.
http://arxiv.org/abs/1412.3409
http://arxiv.org/abs/1412.6564
The neural networks were tasked with predicting what move an expert would make given a position.
The MCTS takes a long time 100,000 playouts are typical - once trained the neural nets are orders of magnitude faster.
The neural nets output a probability for each move ( that an expert would make that move ) - all positions are evauluated in a single forward pass.
Current work centers around combining the two approaches, MCTS evaluates the best suggestions from the neural net.
Expert Human players are still unbeatable by computer Go.