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Description
When I converted to one-hot game states, it seemed easy to save a lot of space with numpy's packbits()
, but I'm worried that it will take a lot of time packing and unpacking. I think the real storage problem is for search trees and for some games' move histories.
A related problem is that I moved all of the extra settings out of the game state arrays to keep them purely ones and zeroes. That will cause a problem when I try to build neural networks, and they don't have access to all the features of a game state.
Suggested solution:
- Add
create_snapshot()
methods, similar to the one for Spargo, and store those in the search tree. Add arestore_snapshot()
method. - Add
create_data()
method that adds extra settings, maybe in another layer of the array, then use that for training data.
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