training rework, best weights so far*

After quite a bit of experimentation it would appear that a far better way to train on self-play data in this setup is to randomise not the moves but the opening. The new selfplay and match binaries have lost the epsilon parameter and now play a random opening of some length before switching to pure, deterministic engine inference. This also resulted in a cleanup of nn_train.py.

As an added bonus, in this configuration it makes sense to harvest training data from both sides in self-play and consequently the two sides are given distinct game ids (this was almost surely an oversight in the original version of TD(lambda) that i had). I also noticed that rand32 was not using the top 32bits of xorshitf*.

*Under default settings nn_train.py produces a 5x5 weight set that doesn't lose a single game to any other cttei that i cared to look at, when measured using `racetrack` (which has a diverse balanced opening book).

1 files changed, 1 insertions, 1 deletions

diff --git a/Makefile b/Makefile
index 37f8428..0060fb9 100644
--- a/Makefile
+++ b/Makefile
@@ -31,7 +31,7 @@ clean:
 	rm -f src/*.o include/*.o
 
 native: CFLAGS += $(NATIVE_FLAGS)
-native: clean ctlm cttei geminict
+native: ctlm cttei geminict
 
 .PHONY: selfplay
 selfplay: