GitHub Source, executable files. GitHub(Japanese top page)
2022-04-18 v32 update required. First play urgency. dfpn tsume-solver for all nodes. w3954, 54540k games.
2022-04-07 v30 update required. Root Policy temperarure is from 1.0 to 1.8. +50 ELO stronger. Randomness up to 30 moves was reverted to the original AlphaZero format. The win rate was not well adjusted to stay within a certain range, so all moves in the initial phase had the same win rate. kldgain=0.000006. 789 playouts per move on average. w3933, 53930k games.
2022-02-25 v28 update required. Network structure is changed. NN's input has Piece movable area. Policy output is from 11259(139x9x9) to 2187(27x9x9). Swish activation. Tsume solver up to 3 plies. Randomness up to 30 plies is based on one ply searched value. w3881, 52390k games.
2021-08-31 Another distributed Deep reinforcement learning for Shogi handicap games, AobaKomaochi is running.
2021-04-27 AobaZero has finished, and moved to 40 block. 39825k games, w3459 is last 20 block, w3460 is 40 block. Thank you!
update history
| In past hour, | number of clients are 4, | 657 games. |
| In past 24 hours, | number of clients are 20, | 19464 games. |
| In past 1000 games, | Average of moves 86.8, | Sente winrate 0.598, | Draw rate 0.056 |
| In past 1,000,000 games, | Average of moves 92.2, | Sente winrate 0.571, | Draw rate 0.045 |
AobaZero 800playouts/move vs Kristallweizen 500k/move. 800 match games.
You can see the transition of opening moves.
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.
From no000000000000.csa to no000000121031.csa are generated by not using neural network, but random function. The first game that is generated by neural network is no000000121032.csa no000001017999.csa. Up to here, 64x15block, window is past 100,000 games. no000001018000.csa. From here, 256x20block, window is past 500,000 games.Weights
w001 ... 64x15b,minibatch 64, learning rate 0.01, wd 0.00005, momentum 0.9, 120000 games w156 ... 64x15b,minibatch 64, learning rate 0.001, wd 0.00005, momentum 0.9, 430000 games w449 ... 256x20b,minibatch 64, learning rate 0.01, wd 0.0002, momentum 0.9, 1018000 games w465 ... 256x20b,minibatch 64, learning rate 0.001, wd 0.0002, momentum 0.9, 1180000 games w775 ... 256x20b,minibatch 4096, learning rate 0.02, wd 0.0002, momentum 0.9, 4220000 games w787 ... 256x20b,minibatch 128, learning rate 0.0002, wd 0.0002, momentum 0.9, 4340000 games w1450 ... 256x20b,minibatch 128, learning rate 0.00002, wd 0.0002, momentum 0.9, 10980000 games w2047 ... 256x20b,minibatch 128, learning rate 0.000002 ,wd 0.0002, momentum 0.9, 16948000 games w2750 ... 256x20b,minibatch 128, learning rate 0.000002 ,wd 0.00004, momentum 0.9, 23982000 games w3022 ... 256x20b,minibatch 128, learning rate 0.000002 ,wd 0.00004, momentum 0.9, 26706447 games, Weights updated 34285 games w3077 ... 256x20b,minibatch 128, learning rate 0.000002 ,wd 0.00004, momentum 0.9, 28352543 games, Replay buffer 1000000 games w3148 ... 256x20b,minibatch 128, learning rate 0.0000002,wd 0.00004, momentum 0.9, 30474874 games w3299 ... 256x20b,minibatch 128, learning rate 0.0000002,wd 0.0002, momentum 0.9, 34987582 games w3460 ... 256x40b,minibatch 64, learning rate 0.000001, wd 0.0002, momentum 0.9, 39825686 games(AlphaZero test ends) w3616 ... 256x40b,minibatch 64, learning rate 0.0000005,wd 0.0002, momentum 0.9, 44461876 games w3703 ... 256x20b,minibatch 64, learning rate 0.000002, wd 0.0002, momentum 0.9, 47075522 games, Temperature 1.0 -> 1.3 w3770 ... 256x20b,minibatch 128, learning rate 0.000002 ,wd 0.0002, momentum 0.9, 49046722 games, average of searched winrate and game result w3806 ... 256x20b,minibatch 128, learning rate 0.000002 ,wd 0.0002, momentum 0.9, 50130343 games, kldgain 0.0000013 w3881 ... 256x20b,minibatch 128, learning rate 0.000002 ,wd 0.0002, momentum 0.9, 52390111 games, NN strucure is dlshogi like. Randomness upto 30 plies based on one ply searched value. w3933 ... 256x20b,minibatch 128, learning rate 0.000002 ,wd 0.0002, momentum 0.9, 53934959 games, Root policy temperature is 1.8. Randomeness upto 30 moves was AlphaZero style. kldgain 0.000006 w3954 ... 256x20b,minibatch 128, learning rate 0.000002 ,wd 0.0002, momentum 0.9, 54545373 games, initial node winrate is based on parent winrate(fpu). dfpn tsume-solver for all nodes. w4177 ... 256x20b,minibatch 128, learning rate 0.000002 ,wd 0.0002, momentum 0.9, 61229235 games, Reduce learning positions upto 30moves and resign within 40 moves. Replaced weight is +64 ELO stronger. Weights are updated each 2000 games ( 4000 iterations) up to w448. Weights are updated each 10000 games (20000 iterations) from w449. Weights are updated each 10000 games (10000 iterations) from w787. Weights are updated each 34285 games (32000 iterations) from w3022. Replay buffer is past 1000000 games(from past 500000 games) from w3077.