move from single candidate td-leaf with gating to tournament of a population

The goal in this change was to overcome the rock-paper-scissors phenomenon that was happening in capability space by having on a single candidate learn to beat the most recent version of itself. In some sense this lead to rotating around in capability space instead of chasing monotonic improvements.

I hope that flipping to a system in which many (N=5) candidates play each other and learn from their own set of diverse games in this pool, combined with --replay-iters, will lead to a given network having to have a much more comprehensive understanding of the space of tactics. I haven't yet had time to train this thorougly, will run those experiments on my desktop computer.

1 files changed, 1 insertions, 4 deletions

diff --git a/.gitignore b/.gitignore
index b013cd7..0d00588 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,15 +1,12 @@
 *.o
 data/
-include/cand_weights_*.c
+include/cand_*
 ct1986*
 ctlm*
 pptdb*
 cttei*
 geminict*
 selfplay*
-match*
 __pycache__/
-production-*.log
 buildroot*
-test/
 .cache