GitHub Source and Windows binary. GitHub(Japanese top page)
2021-12-04 v26. include latest weight w1250.
2021-12-04 Training has finished. 13 million games was generated for 6 months. Lance handicap is Double Ranging Rook(both File 7), Bishop is Feint Ranging Rook(File 7), 2-Piece is similar to Silver Tandem, In 4-Piece and 6-Piece, Shitate does not make castle. Paper and slide. (In Japanese). Thank you!
2021-10-14 Drop the learning rate to 0.000001. (from 10044k games, w955).
2021-09-22 v24 Bug fix. Update reqiured. Restart from w744, 7940001 games.
2021-09-20 v23 kldgain option for training. update required. w745, 7940000 games.
2021-08-05 Drop the learning rate to 0.0001. (from 3711k games, w321).
2021-06-28 v1.1 softmax temperature > 1.0 is adjusted, even if moves <= 30. aobak ver is 20. w92,1430000 games.
2021-06-23 Windows version(v1.0) is released.
2021-06-07 Fixed adjustment ELO method.
2021-06-07 Bug fix. It fails to find 1 ply mate sometimes.
2021-06-06 Web site open. Google Colab is available. Interestingly, at present, uwate(white)'s winrate is high in 6-Piece. This is because less pieces player has more chance to get pieces if you move pieces almost randomly. AobaKomaochi uses 27-point declare rule. The removed pieces are counted towards uwate(white)'s total.
|In past hour,||number of clients are 3,||1873 games.|
|In past 24 hours,||number of clients are 21,||46540 games.|
|In past 7000 games||In past 500,000 games|
|Average of moves||Sente winrate||Draw rate||Average of moves||Sente winrate||Draw rate||Handicap ELO|
AobaKomaochi 100playout/move vs Kristallweizen(6.00) 20k/move. 400 match games.
You can see the transition of opening moves.
For randomeness, it often plays blunder for the first 30 moves. And Black strength is adjusted.
From no000000000000.csa to no000000500007.csa are generated by not using neural network, but random function. The first game that is generated by neural network is no000000500008.csa 256x20block, replay buffer is past 500,000 games.Weights
w001 ... 256x20b,minibatch 128, learning rate 0.01, wd 0.0002, momentum 0.9, 500000 games fail at w009. w001 ... 256x20b,minibatch 128, learning rate 0.001, wd 0.0002, momentum 0.9, 500000 games. restart with smaller lr. w321 ... 256x20b,minibatch 128, learning rate 0.0001, wd 0.0002, momentum 0.9, 3711485 games w524 ... 256x20b,minibatch 128, learning rate 0.00001, wd 0.0002, momentum 0.9, 5738768 games w745 ... 256x20b,minibatch 128, learning rate 0.00001, wd 0.0002, momentum 0.9, 7940000 games. kldgain w955 ... 256x20b,minibatch 128, learning rate 0.000001, wd 0.0002, momentum 0.9, 10044983 games w1031 ... 256x20b,minibatch 128, learning rate 0.00001, wd 0.0002, momentum 0.9, 10802622 games. value loss is from game result to (game result + search winrate)/2.0 w1170 ... 256x20b,minibatch 128, learning rate 0.000001, wd 0.0002, momentum 0.9, 12192627 games