AobaZero is a user-participated Shogi AI project that will test the AlphaZero Shogi experiment.

If you are interested, please join us. Anyone can contribute using Google Colab.

GitHub Source, executable files. GitHub(Japanese top page)

2019-07-09 v1.4 Update required. Random seed for visit count sampling was constant.
2019-07-08 v1.3 Tree reuse. PV and score is available on Shogidokoro.
2019-05-29 v1.2 Update required. MCTS initial value is not draw(0), but loss(-1).

2019-10-14 11:56 JST(update every 30 minutes)
In past hour,number of clients are 16, 576 games.
In past 24 hours, number of clients are 30, 14679 games.
Total 4026963 games. Latest weight= w754. Thank you for your contribution!
In past 1000 games, Average of moves 123.9, Sente winrate 0.550
In past 500,000 games, Average of moves 126.6, Sente winrate 0.538

Elo progress. It is based on a self-match with the previous weight. Left vertical axis is Elo. Right is Floodgate and vs Kristallweizen 1k,10k,50k,100k. Horizontal axis is the weight for every 10,000 games.
As of 2019-10-13.

AobaZero 800playouts/move vs Kristallweizen 100k/move. 800 match games.

You can see the process of acquiring Shogi knowledge from the game records.
Self-play games for every 10,000 games added. The top of the page is the latest game. It will be updated every other day.

These are self-play games for training. It often plays blunder for the first 30 moves.
And sometimes it choose a move that is not a best by adding noise on root node.

Game records
From arch000000000000.csa.xz to arch000003640000.csa.xz.
These will be updated each two weeks.
no000000000000.csa to
 are generated by not using neural network, but random function.
The first game that is generated by neural network is
no000001017999.csa. Up to here, 64x15block, window is past 100,000 games.
no000001018000.csa. From here, 256x20block, window is past 500,000 games.
From w000000000001.txt.xz to w000000000717.txt.xz.
Network size is 64 x 15 block up to w448, 256 x 20 block from w449.
w001  ...  64x15b, 64 minibatch, learning rate 0.01,  wd 0.00005, momentum 0.9,   120000 games
w156  ...  64x15b, 64 minibatch, learning rate 0.001, wd 0.00005, momentum 0.9,   430000 games
w449  ... 256x20b, 64 minibatch, learning rate 0.01,  wd 0.0002,  momentum 0.9,  1018000 games
w465  ... 256x20b, 64 minibatch, learning rate 0.001, wd 0.0002,  momentum 0.9,  1180000 games
Weights are updated each  2000 games ( 4000 iterations) up to w448.
Weights are updated each 10000 games (20000 iterations) from  w449.