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nn_train.py
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I've noticed high variability in training efficacy, part of that i think is due to the fact that the dataset per iteration is not really that large. Sure, it has many rows, but it's fundamentally given by the same engine playing itself with some entropy injection. To remedy this i've expanded the input data for any given iteration to contain --replay-iters many previous datasets as input too. Alongside that decreased the epsilon decay rate for slower "cooling", and included much more of the early game in the training set (min ply 8 -> 4).
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Same network architecture, same training principle. Predictably this is
too slow.
Also statically allocate state in driver programmes.
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Previously the code base assumed that there was a single, global game
state which was the implicit target of all actions taken. Looking
ahead at architectural improvements, this has now been (almost
entirely) made explicit and functions take tak_state_p where
necessary (and also where unnecessary).
Two important fixes to actions.c were made:
- Previously when generating the possible stack moves, stack height
overflows (> 15) were not taken into account and this resulted in the
tree search corrupting the board state. Now action search does not
list all legal actions, rather the subset of these encodeable by the
implementation.
- The check for crushing on a stack move was incorrect (too strict),
and this resulted in many legitimate moves being igonored.
Finally, in other changes, weights have also been improved by training
all games instead of some subset for chosen players, and clang-format
was run on the codebase.
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This is a special interface to negamax_cnn1986 which is designed to
generate output for use in a CGI tak interface to be used over gemini.
Also in this commit is a reformating of the various source files to
use the traditional tab width of 8 spaces.
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