Establishing a player's identity

[pnprog]

Step 1 : Start by training a level x policy network on games of players with level x. This network is reusable.

Step 2 : Add another couple layers at the end and train the last couple layers to discriminate between player A and not player A (you can further train the whole network when your last layers are well trained

Well, this is out of my league in fact

I gave the previous run, it was stuck at the same error levels for the whole day. I had a few more try with different network size, but it's all the same, the control group hardly get past 39% error.

I guess that's it for me, I will upload the script I used to prepare the training data for other to try.

Just for fun: I noticed the SGF games I downloaded include the ELO rating of the bots, so I'm thinking trying to build again a training set with bots that are more or less at the same level (in a given ELO interval), and have try again to see if it makes a difference (I will probably download more archives from CGOS for that).

[Bill Spight]

A possible idea :

Step 1 : Start by training a level x policy network on games of players with level x. This network is reusable.

Step 2 : Add another couple layers at the end and train the last couple layers to discriminate between player A and not player A (you can further train the whole network when your last layers are well trained)


By building on a network that already "know" something, it may take much less training data than by starting with a fresh network

Obviously, this is just an idea and testing is needed (and I'm not able to try this experiment before a couple weeks)

Distinguishing between player A and player B should in general be easier than distinguishing between player A and not player A.

Training policy networks at different levels is an important first step in rating the level of a player's play in a specific game, something which is now done in chess.

[Bill Spight]

I gave the previous run, it was stuck at the same error levels for the whole day. I had a few more try with different network size, but it's all the same, the control group hardly get past 39% error.

I guess that's it for me, I will upload the script I used to prepare the training data for other to try.

That's actually an important result.

Many thanks for your efforts.