TC 15s+100ms
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Score of Deuterium_aw vs Deuterium: 89 - 60 - 155 [0.548] 304
... Deuterium_aw playing White: 49 - 23 - 80 [0.586] 152
... Deuterium_aw playing Black: 40 - 37 - 75 [0.510] 152
... White vs Black: 86 - 63 - 155 [0.538] 304
Elo difference: 33.2 +/- 27.4, LOS: 99.1 %, DrawRatio: 51.0 %
It started with alpha = -inf, beta = +inf
* If it fails low, set alpha to score - 100, beta = score, but if the score is already losing or winning reset alpha/beta to -inf/+inf, then research meaning use the previous iteration depth.
* If it fails high, set beta to score + 100, alpha = score, but if the score is already losing or winning reset alpha/beta to -inf/+inf, then research.
* Otherwise, set alpha = score - 30, beta = score + 30, no research just continue with the next iteration depth.
Next:
* If it fails low and previous score was low or high then reset alpha/beta to -inf/+inf, then research. However if the score is already losing or winning reset alpha/beta to -inf/+inf, then research.
* If it fails high and previous score was low or high then reset alpha/beta to -inf/+inf, then research. However if the score is already losing or winning reset alpha/beta to -inf/+inf, then research.
So in summary if there is successive lows low/low or successive highs high/high, or alternate high/low or low/high, then reset alpha/beta to -inf/+inf.
I call 100 as BadWindow and 30 as GoodWindow.
I am trying to tune these two params with optuna optimizer at 100 games per trial for 100 trials at TC 15s+50ms to see if the optimizer can improve it.
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python -u tuner.py --study-name deu_aspwindow_opt --sampler name=tpe --engine ./engines/deuterium/deuterium_17.exe --initial-best-value 0.55 --concurrency 6 --opening-file ./start_opening/ogpt_chess_startpos.epd --opening-format epd --input-param "{'AspWindowGood': {'default':30, 'min':5, 'max':100, 'step':1}, 'AspWindowBad': {'default':100, 'min':5, 'max':200, 'step':1}}" --games-per-trial 100 --trials 100 --base-time-sec 15 --inc-time-sec 0.05 --pgn-output deu_aspwindow_opt.pgn --threshold-pruner result=0.25 --plot
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2020-12-27 14:58:05,825 | INFO | init param: {'AspWindowBad': 100, 'AspWindowGood': 30}
2020-12-27 15:05:29,205 | INFO | study best param: {'AspWindowBad': 171, 'AspWindowGood': 42}
2020-12-27 15:05:29,206 | INFO | study best value: 0.53