I tried a new input for contact & crashed: like the hitting shots input, but where the opponent covers the point with two (or more) checkers when hitting, without leaving another blot elsewhere.
I actually broke it into two pieces: one for the odds of being hit & covered in the opponent's home board, and one for everywhere else. (And of course, both inputs were applied to each player, so four new inputs.)
I started with a random-weights version of Player 3.2 and added the new inputs, and trained it using supervised learning on the GNUbg training set.
The player converged after 150 epochs to Contact ER 14.0, Crashed ER 12.4, and Race ER 2.08. This compares to 14.0, 12.6, and 2.08 for Player 3.2. So marginally better on Crashed ER but otherwise identical.
Since we know that Crashed ER does not matter much in terms of game play, this new player really is almost identical to Player 3.2.
The conclusion must be that these new inputs do not add noticeable value. So I will not include them going forward. The hunt continues!
I actually broke it into two pieces: one for the odds of being hit & covered in the opponent's home board, and one for everywhere else. (And of course, both inputs were applied to each player, so four new inputs.)
I started with a random-weights version of Player 3.2 and added the new inputs, and trained it using supervised learning on the GNUbg training set.
The player converged after 150 epochs to Contact ER 14.0, Crashed ER 12.4, and Race ER 2.08. This compares to 14.0, 12.6, and 2.08 for Player 3.2. So marginally better on Crashed ER but otherwise identical.
Since we know that Crashed ER does not matter much in terms of game play, this new player really is almost identical to Player 3.2.
The conclusion must be that these new inputs do not add noticeable value. So I will not include them going forward. The hunt continues!
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