diff --git a/VSharp.ML.AIAgent/ml/models/RGCNEdgeTypeTAG3SageDouble/64ch/100e/GNN_state_pred_het_dict b/VSharp.ML.AIAgent/ml/models/RGCNEdgeTypeTAG3SageDouble/64ch/100e/GNN_state_pred_het_dict new file mode 100644 index 000000000..106094615 Binary files /dev/null and b/VSharp.ML.AIAgent/ml/models/RGCNEdgeTypeTAG3SageDouble/64ch/100e/GNN_state_pred_het_dict differ diff --git a/VSharp.ML.AIAgent/ml/models/RGCNEdgeTypeTAG3SageDouble/64ch/100e/GNN_state_pred_het_full b/VSharp.ML.AIAgent/ml/models/RGCNEdgeTypeTAG3SageDouble/64ch/100e/GNN_state_pred_het_full new file mode 100644 index 000000000..8d962d82a Binary files /dev/null and b/VSharp.ML.AIAgent/ml/models/RGCNEdgeTypeTAG3SageDouble/64ch/100e/GNN_state_pred_het_full differ diff --git a/VSharp.ML.AIAgent/ml/models/RGCNEdgeTypeTAG3SageDouble/64ch/100e/train_res b/VSharp.ML.AIAgent/ml/models/RGCNEdgeTypeTAG3SageDouble/64ch/100e/train_res new file mode 100644 index 000000000..14af802df --- /dev/null +++ b/VSharp.ML.AIAgent/ml/models/RGCNEdgeTypeTAG3SageDouble/64ch/100e/train_res @@ -0,0 +1,100 @@ +Epoch: 000, Train Loss: 0.048970, Test Loss: 0.048667 +Epoch: 001, Train Loss: 0.030800, Test Loss: 0.030511 +Epoch: 002, Train Loss: 0.025334, Test Loss: 0.025270 +Epoch: 003, Train Loss: 0.023651, Test Loss: 0.023538 +Epoch: 004, Train Loss: 0.019162, Test Loss: 0.019080 +Epoch: 005, Train Loss: 0.019853, Test Loss: 0.019819 +Epoch: 006, Train Loss: 0.014548, Test Loss: 0.014642 +Epoch: 007, Train Loss: 0.015340, Test Loss: 0.015407 +Epoch: 008, Train Loss: 0.013076, Test Loss: 0.013189 +Epoch: 009, Train Loss: 0.010759, Test Loss: 0.010827 +Epoch: 010, Train Loss: 0.009676, Test Loss: 0.009796 +Epoch: 011, Train Loss: 0.009729, Test Loss: 0.009877 +Epoch: 012, Train Loss: 0.009505, Test Loss: 0.009588 +Epoch: 013, Train Loss: 0.007938, Test Loss: 0.008046 +Epoch: 014, Train Loss: 0.007638, Test Loss: 0.007730 +Epoch: 015, Train Loss: 0.007149, Test Loss: 0.007246 +Epoch: 016, Train Loss: 0.007712, Test Loss: 0.007853 +Epoch: 017, Train Loss: 0.007507, Test Loss: 0.007556 +Epoch: 018, Train Loss: 0.006890, Test Loss: 0.006997 +Epoch: 019, Train Loss: 0.006902, Test Loss: 0.007011 +Epoch: 020, Train Loss: 0.006120, Test Loss: 0.006238 +Epoch: 021, Train Loss: 0.006021, Test Loss: 0.006144 +Epoch: 022, Train Loss: 0.005587, Test Loss: 0.005711 +Epoch: 023, Train Loss: 0.005837, Test Loss: 0.005951 +Epoch: 024, Train Loss: 0.005759, Test Loss: 0.005873 +Epoch: 025, Train Loss: 0.005572, Test Loss: 0.005677 +Epoch: 026, Train Loss: 0.005298, Test Loss: 0.005421 +Epoch: 027, Train Loss: 0.005178, Test Loss: 0.005298 +Epoch: 028, Train Loss: 0.005297, Test Loss: 0.005416 +Epoch: 029, Train Loss: 0.005530, Test Loss: 0.005629 +Epoch: 030, Train Loss: 0.005050, Test Loss: 0.005169 +Epoch: 031, Train Loss: 0.005010, Test Loss: 0.005124 +Epoch: 032, Train Loss: 0.005088, Test Loss: 0.005215 +Epoch: 033, Train Loss: 0.004981, Test Loss: 0.005100 +Epoch: 034, Train Loss: 0.004784, Test Loss: 0.004912 +Epoch: 035, Train Loss: 0.004354, Test Loss: 0.004489 +Epoch: 036, Train Loss: 0.004310, Test Loss: 0.004452 +Epoch: 037, Train Loss: 0.004201, Test Loss: 0.004338 +Epoch: 038, Train Loss: 0.004280, Test Loss: 0.004434 +Epoch: 039, Train Loss: 0.004249, Test Loss: 0.004401 +Epoch: 040, Train Loss: 0.004414, Test Loss: 0.004560 +Epoch: 041, Train Loss: 0.004089, Test Loss: 0.004244 +Epoch: 042, Train Loss: 0.004175, Test Loss: 0.004347 +Epoch: 043, Train Loss: 0.004278, Test Loss: 0.004436 +Epoch: 044, Train Loss: 0.004163, Test Loss: 0.004314 +Epoch: 045, Train Loss: 0.004047, Test Loss: 0.004236 +Epoch: 046, Train Loss: 0.004052, Test Loss: 0.004219 +Epoch: 047, Train Loss: 0.003960, Test Loss: 0.004133 +Epoch: 048, Train Loss: 0.003730, Test Loss: 0.003902 +Epoch: 049, Train Loss: 0.003869, Test Loss: 0.004039 +Epoch: 050, Train Loss: 0.003696, Test Loss: 0.003866 +Epoch: 051, Train Loss: 0.003892, Test Loss: 0.004041 +Epoch: 052, Train Loss: 0.004020, Test Loss: 0.004185 +Epoch: 053, Train Loss: 0.003810, Test Loss: 0.003983 +Epoch: 054, Train Loss: 0.003761, Test Loss: 0.003915 +Epoch: 055, Train Loss: 0.003905, Test Loss: 0.004070 +Epoch: 056, Train Loss: 0.003691, Test Loss: 0.003864 +Epoch: 057, Train Loss: 0.003878, Test Loss: 0.004045 +Epoch: 058, Train Loss: 0.003448, Test Loss: 0.003622 +Epoch: 059, Train Loss: 0.003818, Test Loss: 0.003982 +Epoch: 060, Train Loss: 0.003715, Test Loss: 0.003885 +Epoch: 061, Train Loss: 0.003829, Test Loss: 0.003994 +Epoch: 062, Train Loss: 0.004003, Test Loss: 0.004183 +Epoch: 063, Train Loss: 0.003527, Test Loss: 0.003699 +Epoch: 064, Train Loss: 0.003164, Test Loss: 0.003315 +Epoch: 065, Train Loss: 0.003364, Test Loss: 0.003543 +Epoch: 066, Train Loss: 0.003863, Test Loss: 0.004034 +Epoch: 067, Train Loss: 0.003042, Test Loss: 0.003210 +Epoch: 068, Train Loss: 0.003170, Test Loss: 0.003324 +Epoch: 069, Train Loss: 0.003216, Test Loss: 0.003375 +Epoch: 070, Train Loss: 0.003173, Test Loss: 0.003336 +Epoch: 071, Train Loss: 0.002911, Test Loss: 0.003069 +Epoch: 072, Train Loss: 0.003125, Test Loss: 0.003285 +Epoch: 073, Train Loss: 0.003070, Test Loss: 0.003210 +Epoch: 074, Train Loss: 0.002740, Test Loss: 0.002884 +Epoch: 075, Train Loss: 0.003088, Test Loss: 0.003235 +Epoch: 076, Train Loss: 0.003172, Test Loss: 0.003334 +Epoch: 077, Train Loss: 0.002919, Test Loss: 0.003071 +Epoch: 078, Train Loss: 0.002999, Test Loss: 0.003141 +Epoch: 079, Train Loss: 0.003031, Test Loss: 0.003189 +Epoch: 080, Train Loss: 0.002906, Test Loss: 0.003084 +Epoch: 081, Train Loss: 0.002974, Test Loss: 0.003123 +Epoch: 082, Train Loss: 0.002798, Test Loss: 0.002937 +Epoch: 083, Train Loss: 0.003092, Test Loss: 0.003233 +Epoch: 084, Train Loss: 0.003443, Test Loss: 0.003602 +Epoch: 085, Train Loss: 0.002840, Test Loss: 0.002990 +Epoch: 086, Train Loss: 0.002805, Test Loss: 0.002959 +Epoch: 087, Train Loss: 0.002878, Test Loss: 0.003014 +Epoch: 088, Train Loss: 0.002929, Test Loss: 0.003086 +Epoch: 089, Train Loss: 0.002926, Test Loss: 0.003084 +Epoch: 090, Train Loss: 0.002785, Test Loss: 0.002938 +Epoch: 091, Train Loss: 0.002726, Test Loss: 0.002869 +Epoch: 092, Train Loss: 0.003428, Test Loss: 0.003573 +Epoch: 093, Train Loss: 0.003024, Test Loss: 0.003174 +Epoch: 094, Train Loss: 0.002989, Test Loss: 0.003127 +Epoch: 095, Train Loss: 0.002801, Test Loss: 0.002942 +Epoch: 096, Train Loss: 0.003118, Test Loss: 0.003258 +Epoch: 097, Train Loss: 0.003105, Test Loss: 0.003258 +Epoch: 098, Train Loss: 0.002740, Test Loss: 0.002885 +Epoch: 099, Train Loss: 0.002515, Test Loss: 0.002646 diff --git a/VSharp.ML.AIAgent/ml/models/RGCNEdgeTypeTAG3SageDouble/64ch/20e/GNN_state_pred_het_dict b/VSharp.ML.AIAgent/ml/models/RGCNEdgeTypeTAG3SageDouble/64ch/20e/GNN_state_pred_het_dict new file mode 100644 index 000000000..c2843a79b Binary files /dev/null and b/VSharp.ML.AIAgent/ml/models/RGCNEdgeTypeTAG3SageDouble/64ch/20e/GNN_state_pred_het_dict differ diff --git a/VSharp.ML.AIAgent/ml/models/RGCNEdgeTypeTAG3SageDouble/64ch/20e/GNN_state_pred_het_full b/VSharp.ML.AIAgent/ml/models/RGCNEdgeTypeTAG3SageDouble/64ch/20e/GNN_state_pred_het_full new file mode 100644 index 000000000..6adbe6bb5 Binary files /dev/null and b/VSharp.ML.AIAgent/ml/models/RGCNEdgeTypeTAG3SageDouble/64ch/20e/GNN_state_pred_het_full differ diff --git a/VSharp.ML.AIAgent/ml/models/RGCNEdgeTypeTAG3SageDouble/64ch/20e/train_res b/VSharp.ML.AIAgent/ml/models/RGCNEdgeTypeTAG3SageDouble/64ch/20e/train_res new file mode 100644 index 000000000..c4d889494 --- /dev/null +++ b/VSharp.ML.AIAgent/ml/models/RGCNEdgeTypeTAG3SageDouble/64ch/20e/train_res @@ -0,0 +1,20 @@ +Epoch: 000, Train Loss: 0.050863, Test Loss: 0.052546 +Epoch: 001, Train Loss: 0.029373, Test Loss: 0.030833 +Epoch: 002, Train Loss: 0.020787, Test Loss: 0.021997 +Epoch: 003, Train Loss: 0.016364, Test Loss: 0.017456 +Epoch: 004, Train Loss: 0.014149, Test Loss: 0.015224 +Epoch: 005, Train Loss: 0.012573, Test Loss: 0.013564 +Epoch: 006, Train Loss: 0.010724, Test Loss: 0.011628 +Epoch: 007, Train Loss: 0.009498, Test Loss: 0.010333 +Epoch: 008, Train Loss: 0.008883, Test Loss: 0.009697 +Epoch: 009, Train Loss: 0.008178, Test Loss: 0.008891 +Epoch: 010, Train Loss: 0.007793, Test Loss: 0.008484 +Epoch: 011, Train Loss: 0.007520, Test Loss: 0.008133 +Epoch: 012, Train Loss: 0.007100, Test Loss: 0.007761 +Epoch: 013, Train Loss: 0.006461, Test Loss: 0.007043 +Epoch: 014, Train Loss: 0.006458, Test Loss: 0.007039 +Epoch: 015, Train Loss: 0.006238, Test Loss: 0.006798 +Epoch: 016, Train Loss: 0.006034, Test Loss: 0.006582 +Epoch: 017, Train Loss: 0.005676, Test Loss: 0.006194 +Epoch: 018, Train Loss: 0.005672, Test Loss: 0.006189 +Epoch: 019, Train Loss: 0.005481, Test Loss: 0.005976 diff --git a/VSharp.ML.AIAgent/ml/models/RGCNEdgeTypeTAG3SageDouble/64ch/50e/GNN_state_pred_het_dict b/VSharp.ML.AIAgent/ml/models/RGCNEdgeTypeTAG3SageDouble/64ch/50e/GNN_state_pred_het_dict new file mode 100644 index 000000000..15067a261 Binary files /dev/null and b/VSharp.ML.AIAgent/ml/models/RGCNEdgeTypeTAG3SageDouble/64ch/50e/GNN_state_pred_het_dict differ diff --git a/VSharp.ML.AIAgent/ml/models/RGCNEdgeTypeTAG3SageDouble/64ch/50e/GNN_state_pred_het_full b/VSharp.ML.AIAgent/ml/models/RGCNEdgeTypeTAG3SageDouble/64ch/50e/GNN_state_pred_het_full new file mode 100644 index 000000000..142373748 Binary files /dev/null and b/VSharp.ML.AIAgent/ml/models/RGCNEdgeTypeTAG3SageDouble/64ch/50e/GNN_state_pred_het_full differ diff --git a/VSharp.ML.AIAgent/ml/models/RGCNEdgeTypeTAG3SageDouble/64ch/50e/train_res b/VSharp.ML.AIAgent/ml/models/RGCNEdgeTypeTAG3SageDouble/64ch/50e/train_res new file mode 100644 index 000000000..e58564d23 --- /dev/null +++ b/VSharp.ML.AIAgent/ml/models/RGCNEdgeTypeTAG3SageDouble/64ch/50e/train_res @@ -0,0 +1,50 @@ +Epoch: 000, Train Loss: 0.057171, Test Loss: 0.058010 +Epoch: 001, Train Loss: 0.030787, Test Loss: 0.031484 +Epoch: 002, Train Loss: 0.023136, Test Loss: 0.023673 +Epoch: 003, Train Loss: 0.019109, Test Loss: 0.019526 +Epoch: 004, Train Loss: 0.017071, Test Loss: 0.017409 +Epoch: 005, Train Loss: 0.014807, Test Loss: 0.015129 +Epoch: 006, Train Loss: 0.014352, Test Loss: 0.014680 +Epoch: 007, Train Loss: 0.012505, Test Loss: 0.012756 +Epoch: 008, Train Loss: 0.011339, Test Loss: 0.011526 +Epoch: 009, Train Loss: 0.011503, Test Loss: 0.011724 +Epoch: 010, Train Loss: 0.009757, Test Loss: 0.009975 +Epoch: 011, Train Loss: 0.009136, Test Loss: 0.009331 +Epoch: 012, Train Loss: 0.009876, Test Loss: 0.010134 +Epoch: 013, Train Loss: 0.009290, Test Loss: 0.009502 +Epoch: 014, Train Loss: 0.007732, Test Loss: 0.007969 +Epoch: 015, Train Loss: 0.008310, Test Loss: 0.008550 +Epoch: 016, Train Loss: 0.007947, Test Loss: 0.008159 +Epoch: 017, Train Loss: 0.007175, Test Loss: 0.007429 +Epoch: 018, Train Loss: 0.006615, Test Loss: 0.006852 +Epoch: 019, Train Loss: 0.006258, Test Loss: 0.006468 +Epoch: 020, Train Loss: 0.006021, Test Loss: 0.006213 +Epoch: 021, Train Loss: 0.005957, Test Loss: 0.006177 +Epoch: 022, Train Loss: 0.005714, Test Loss: 0.005900 +Epoch: 023, Train Loss: 0.005592, Test Loss: 0.005798 +Epoch: 024, Train Loss: 0.005693, Test Loss: 0.005882 +Epoch: 025, Train Loss: 0.005862, Test Loss: 0.006065 +Epoch: 026, Train Loss: 0.005933, Test Loss: 0.006124 +Epoch: 027, Train Loss: 0.004832, Test Loss: 0.004976 +Epoch: 028, Train Loss: 0.005156, Test Loss: 0.005320 +Epoch: 029, Train Loss: 0.004828, Test Loss: 0.004969 +Epoch: 030, Train Loss: 0.004528, Test Loss: 0.004665 +Epoch: 031, Train Loss: 0.004441, Test Loss: 0.004558 +Epoch: 032, Train Loss: 0.004391, Test Loss: 0.004523 +Epoch: 033, Train Loss: 0.004387, Test Loss: 0.004529 +Epoch: 034, Train Loss: 0.004330, Test Loss: 0.004454 +Epoch: 035, Train Loss: 0.004358, Test Loss: 0.004487 +Epoch: 036, Train Loss: 0.004368, Test Loss: 0.004527 +Epoch: 037, Train Loss: 0.004413, Test Loss: 0.004563 +Epoch: 038, Train Loss: 0.004288, Test Loss: 0.004420 +Epoch: 039, Train Loss: 0.004150, Test Loss: 0.004276 +Epoch: 040, Train Loss: 0.004238, Test Loss: 0.004380 +Epoch: 041, Train Loss: 0.004212, Test Loss: 0.004351 +Epoch: 042, Train Loss: 0.004132, Test Loss: 0.004276 +Epoch: 043, Train Loss: 0.004148, Test Loss: 0.004289 +Epoch: 044, Train Loss: 0.003967, Test Loss: 0.004098 +Epoch: 045, Train Loss: 0.003948, Test Loss: 0.004084 +Epoch: 046, Train Loss: 0.003984, Test Loss: 0.004108 +Epoch: 047, Train Loss: 0.003791, Test Loss: 0.003920 +Epoch: 048, Train Loss: 0.004121, Test Loss: 0.004241 +Epoch: 049, Train Loss: 0.003816, Test Loss: 0.003945 diff --git a/VSharp.ML.AIAgent/ml/models/RGCNEdgeTypeTAG3SageDouble/model.py b/VSharp.ML.AIAgent/ml/models/RGCNEdgeTypeTAG3SageDouble/model.py new file mode 100644 index 000000000..a6060aabd --- /dev/null +++ b/VSharp.ML.AIAgent/ml/models/RGCNEdgeTypeTAG3SageDouble/model.py @@ -0,0 +1,62 @@ +import torch +from torch.nn import Linear +from torch_geometric.nn import TAGConv, SAGEConv, GraphConv, RGCNConv + + +class StateModelEncoder(torch.nn.Module): + def __init__(self, hidden_channels, out_channels): + super().__init__() + self.conv1 = RGCNConv(5, hidden_channels, 3) + self.conv2 = TAGConv(hidden_channels, hidden_channels, 3) # TAGConv + self.conv3 = GraphConv((-1, -1), hidden_channels) # SAGEConv + self.conv32 = SAGEConv((-1, -1), hidden_channels) # SAGEConv + self.conv4 = SAGEConv((-1, -1), hidden_channels) + self.conv42 = SAGEConv((-1, -1), hidden_channels) + self.conv5 = SAGEConv(-1, hidden_channels) + self.lin = Linear(hidden_channels, out_channels) + + def forward( + self, + game_x, + state_x, + edge_index_v_v, + edge_type_v_v, + edge_index_history_v_s, + edge_attr_history_v_s, + edge_index_in_v_s, + edge_index_s_s, + ): + game_x = self.conv1(game_x, edge_index_v_v, edge_type_v_v).relu() + + state_x = self.conv3( + (game_x, state_x), + edge_index_history_v_s, + edge_attr_history_v_s, + ).relu() + + state_x = self.conv32( + (game_x, state_x), + edge_index_history_v_s, + ).relu() + + state_x = self.conv4( + (game_x, state_x), + edge_index_in_v_s, + ).relu() + + state_x = self.conv42( + (game_x, state_x), + edge_index_in_v_s, + ).relu() + + state_x = self.conv2( + state_x, + edge_index_s_s, + ).relu() + + state_x = self.conv5( + state_x, + edge_index_s_s, + ).relu() + + return self.lin(state_x) diff --git a/VSharp.ML.AIAgent/ml/models/TAGSageDouble/32ch/20e/GNN_state_pred_het_dict b/VSharp.ML.AIAgent/ml/models/TAGSageDouble/32ch/20e/GNN_state_pred_het_dict new file mode 100644 index 000000000..e972c068e Binary files /dev/null and b/VSharp.ML.AIAgent/ml/models/TAGSageDouble/32ch/20e/GNN_state_pred_het_dict differ diff --git a/VSharp.ML.AIAgent/ml/models/TAGSageDouble/32ch/20e/GNN_state_pred_het_full b/VSharp.ML.AIAgent/ml/models/TAGSageDouble/32ch/20e/GNN_state_pred_het_full new file mode 100644 index 000000000..c577f77bb Binary files /dev/null and b/VSharp.ML.AIAgent/ml/models/TAGSageDouble/32ch/20e/GNN_state_pred_het_full differ diff --git a/VSharp.ML.AIAgent/ml/models/TAGSageDouble/32ch/20e/train_res b/VSharp.ML.AIAgent/ml/models/TAGSageDouble/32ch/20e/train_res new file mode 100644 index 000000000..0c7fa36e4 --- /dev/null +++ b/VSharp.ML.AIAgent/ml/models/TAGSageDouble/32ch/20e/train_res @@ -0,0 +1,20 @@ +Epoch: 000, Train Loss: 0.072378, Test Loss: 0.073496 +Epoch: 001, Train Loss: 0.043047, Test Loss: 0.043577 +Epoch: 002, Train Loss: 0.034224, Test Loss: 0.034591 +Epoch: 003, Train Loss: 0.028296, Test Loss: 0.028690 +Epoch: 004, Train Loss: 0.024289, Test Loss: 0.024659 +Epoch: 005, Train Loss: 0.021720, Test Loss: 0.022099 +Epoch: 006, Train Loss: 0.020279, Test Loss: 0.020652 +Epoch: 007, Train Loss: 0.018290, Test Loss: 0.018627 +Epoch: 008, Train Loss: 0.016991, Test Loss: 0.017269 +Epoch: 009, Train Loss: 0.016168, Test Loss: 0.016476 +Epoch: 010, Train Loss: 0.014912, Test Loss: 0.015189 +Epoch: 011, Train Loss: 0.014065, Test Loss: 0.014356 +Epoch: 012, Train Loss: 0.014232, Test Loss: 0.014502 +Epoch: 013, Train Loss: 0.013865, Test Loss: 0.014091 +Epoch: 014, Train Loss: 0.013500, Test Loss: 0.013771 +Epoch: 015, Train Loss: 0.012756, Test Loss: 0.012998 +Epoch: 016, Train Loss: 0.012388, Test Loss: 0.012617 +Epoch: 017, Train Loss: 0.012210, Test Loss: 0.012405 +Epoch: 018, Train Loss: 0.012160, Test Loss: 0.012321 +Epoch: 019, Train Loss: 0.011690, Test Loss: 0.011885 diff --git a/VSharp.ML.AIAgent/ml/models/TAGSageDouble/64ch/100e/GNN_state_pred_het_dict b/VSharp.ML.AIAgent/ml/models/TAGSageDouble/64ch/100e/GNN_state_pred_het_dict new file mode 100644 index 000000000..0517348c5 Binary files /dev/null and b/VSharp.ML.AIAgent/ml/models/TAGSageDouble/64ch/100e/GNN_state_pred_het_dict differ diff --git a/VSharp.ML.AIAgent/ml/models/TAGSageDouble/64ch/100e/GNN_state_pred_het_full b/VSharp.ML.AIAgent/ml/models/TAGSageDouble/64ch/100e/GNN_state_pred_het_full new file mode 100644 index 000000000..a949058c6 Binary files /dev/null and b/VSharp.ML.AIAgent/ml/models/TAGSageDouble/64ch/100e/GNN_state_pred_het_full differ diff --git a/VSharp.ML.AIAgent/ml/models/TAGSageDouble/64ch/100e/train_res b/VSharp.ML.AIAgent/ml/models/TAGSageDouble/64ch/100e/train_res new file mode 100644 index 000000000..f15f73606 --- /dev/null +++ b/VSharp.ML.AIAgent/ml/models/TAGSageDouble/64ch/100e/train_res @@ -0,0 +1,100 @@ +Epoch: 000, Train Loss: 0.047441, Test Loss: 0.047307 +Epoch: 001, Train Loss: 0.027983, Test Loss: 0.028091 +Epoch: 002, Train Loss: 0.019875, Test Loss: 0.019800 +Epoch: 003, Train Loss: 0.015671, Test Loss: 0.015709 +Epoch: 004, Train Loss: 0.013007, Test Loss: 0.013053 +Epoch: 005, Train Loss: 0.010809, Test Loss: 0.010988 +Epoch: 006, Train Loss: 0.009938, Test Loss: 0.010142 +Epoch: 007, Train Loss: 0.009297, Test Loss: 0.009558 +Epoch: 008, Train Loss: 0.008306, Test Loss: 0.008476 +Epoch: 009, Train Loss: 0.007844, Test Loss: 0.008031 +Epoch: 010, Train Loss: 0.007373, Test Loss: 0.007566 +Epoch: 011, Train Loss: 0.007039, Test Loss: 0.007258 +Epoch: 012, Train Loss: 0.006610, Test Loss: 0.006775 +Epoch: 013, Train Loss: 0.006401, Test Loss: 0.006583 +Epoch: 014, Train Loss: 0.006156, Test Loss: 0.006349 +Epoch: 015, Train Loss: 0.006003, Test Loss: 0.006174 +Epoch: 016, Train Loss: 0.005762, Test Loss: 0.005955 +Epoch: 017, Train Loss: 0.005660, Test Loss: 0.005828 +Epoch: 018, Train Loss: 0.005280, Test Loss: 0.005431 +Epoch: 019, Train Loss: 0.005207, Test Loss: 0.005368 +Epoch: 020, Train Loss: 0.005102, Test Loss: 0.005245 +Epoch: 021, Train Loss: 0.004956, Test Loss: 0.005110 +Epoch: 022, Train Loss: 0.004837, Test Loss: 0.004973 +Epoch: 023, Train Loss: 0.004780, Test Loss: 0.004912 +Epoch: 024, Train Loss: 0.004628, Test Loss: 0.004775 +Epoch: 025, Train Loss: 0.004730, Test Loss: 0.004861 +Epoch: 026, Train Loss: 0.004443, Test Loss: 0.004571 +Epoch: 027, Train Loss: 0.004354, Test Loss: 0.004493 +Epoch: 028, Train Loss: 0.004315, Test Loss: 0.004458 +Epoch: 029, Train Loss: 0.004350, Test Loss: 0.004485 +Epoch: 030, Train Loss: 0.004211, Test Loss: 0.004352 +Epoch: 031, Train Loss: 0.004113, Test Loss: 0.004253 +Epoch: 032, Train Loss: 0.004068, Test Loss: 0.004211 +Epoch: 033, Train Loss: 0.004030, Test Loss: 0.004165 +Epoch: 034, Train Loss: 0.003899, Test Loss: 0.004036 +Epoch: 035, Train Loss: 0.003832, Test Loss: 0.003964 +Epoch: 036, Train Loss: 0.003761, Test Loss: 0.003910 +Epoch: 037, Train Loss: 0.003703, Test Loss: 0.003839 +Epoch: 038, Train Loss: 0.003704, Test Loss: 0.003837 +Epoch: 039, Train Loss: 0.003600, Test Loss: 0.003739 +Epoch: 040, Train Loss: 0.003544, Test Loss: 0.003683 +Epoch: 041, Train Loss: 0.003528, Test Loss: 0.003679 +Epoch: 042, Train Loss: 0.003515, Test Loss: 0.003656 +Epoch: 043, Train Loss: 0.003423, Test Loss: 0.003566 +Epoch: 044, Train Loss: 0.003475, Test Loss: 0.003613 +Epoch: 045, Train Loss: 0.003346, Test Loss: 0.003486 +Epoch: 046, Train Loss: 0.003336, Test Loss: 0.003475 +Epoch: 047, Train Loss: 0.003270, Test Loss: 0.003401 +Epoch: 048, Train Loss: 0.003265, Test Loss: 0.003391 +Epoch: 049, Train Loss: 0.003190, Test Loss: 0.003301 +Epoch: 050, Train Loss: 0.003140, Test Loss: 0.003256 +Epoch: 051, Train Loss: 0.003111, Test Loss: 0.003236 +Epoch: 052, Train Loss: 0.003114, Test Loss: 0.003231 +Epoch: 053, Train Loss: 0.003045, Test Loss: 0.003151 +Epoch: 054, Train Loss: 0.002983, Test Loss: 0.003090 +Epoch: 055, Train Loss: 0.002978, Test Loss: 0.003089 +Epoch: 056, Train Loss: 0.002931, Test Loss: 0.003043 +Epoch: 057, Train Loss: 0.002931, Test Loss: 0.003039 +Epoch: 058, Train Loss: 0.002922, Test Loss: 0.003025 +Epoch: 059, Train Loss: 0.002918, Test Loss: 0.003017 +Epoch: 060, Train Loss: 0.002894, Test Loss: 0.002988 +Epoch: 061, Train Loss: 0.002802, Test Loss: 0.002901 +Epoch: 062, Train Loss: 0.002828, Test Loss: 0.002917 +Epoch: 063, Train Loss: 0.002812, Test Loss: 0.002898 +Epoch: 064, Train Loss: 0.002941, Test Loss: 0.003026 +Epoch: 065, Train Loss: 0.002762, Test Loss: 0.002856 +Epoch: 066, Train Loss: 0.002782, Test Loss: 0.002879 +Epoch: 067, Train Loss: 0.002745, Test Loss: 0.002826 +Epoch: 068, Train Loss: 0.002705, Test Loss: 0.002787 +Epoch: 069, Train Loss: 0.002805, Test Loss: 0.002893 +Epoch: 070, Train Loss: 0.002758, Test Loss: 0.002843 +Epoch: 071, Train Loss: 0.002624, Test Loss: 0.002710 +Epoch: 072, Train Loss: 0.002774, Test Loss: 0.002854 +Epoch: 073, Train Loss: 0.002759, Test Loss: 0.002842 +Epoch: 074, Train Loss: 0.002614, Test Loss: 0.002701 +Epoch: 075, Train Loss: 0.002605, Test Loss: 0.002688 +Epoch: 076, Train Loss: 0.002582, Test Loss: 0.002659 +Epoch: 077, Train Loss: 0.002550, Test Loss: 0.002635 +Epoch: 078, Train Loss: 0.002603, Test Loss: 0.002696 +Epoch: 079, Train Loss: 0.002573, Test Loss: 0.002650 +Epoch: 080, Train Loss: 0.002495, Test Loss: 0.002565 +Epoch: 081, Train Loss: 0.002490, Test Loss: 0.002565 +Epoch: 082, Train Loss: 0.002456, Test Loss: 0.002534 +Epoch: 083, Train Loss: 0.002446, Test Loss: 0.002514 +Epoch: 084, Train Loss: 0.002489, Test Loss: 0.002568 +Epoch: 085, Train Loss: 0.002667, Test Loss: 0.002737 +Epoch: 086, Train Loss: 0.002421, Test Loss: 0.002483 +Epoch: 087, Train Loss: 0.002547, Test Loss: 0.002629 +Epoch: 088, Train Loss: 0.002356, Test Loss: 0.002422 +Epoch: 089, Train Loss: 0.002392, Test Loss: 0.002453 +Epoch: 090, Train Loss: 0.002351, Test Loss: 0.002427 +Epoch: 091, Train Loss: 0.002332, Test Loss: 0.002407 +Epoch: 092, Train Loss: 0.004105, Test Loss: 0.004236 +Epoch: 093, Train Loss: 0.002387, Test Loss: 0.002469 +Epoch: 094, Train Loss: 0.002316, Test Loss: 0.002390 +Epoch: 095, Train Loss: 0.002291, Test Loss: 0.002376 +Epoch: 096, Train Loss: 0.002354, Test Loss: 0.002421 +Epoch: 097, Train Loss: 0.002294, Test Loss: 0.002367 +Epoch: 098, Train Loss: 0.002244, Test Loss: 0.002338 +Epoch: 099, Train Loss: 0.002263, Test Loss: 0.002370 diff --git a/VSharp.ML.AIAgent/ml/models/TAGSageDouble/64ch/20e/GNN_state_pred_het_dict b/VSharp.ML.AIAgent/ml/models/TAGSageDouble/64ch/20e/GNN_state_pred_het_dict new file mode 100644 index 000000000..0c4201d5a Binary files /dev/null and b/VSharp.ML.AIAgent/ml/models/TAGSageDouble/64ch/20e/GNN_state_pred_het_dict differ diff --git a/VSharp.ML.AIAgent/ml/models/TAGSageDouble/64ch/20e/GNN_state_pred_het_full b/VSharp.ML.AIAgent/ml/models/TAGSageDouble/64ch/20e/GNN_state_pred_het_full new file mode 100644 index 000000000..80819e1d8 Binary files /dev/null and b/VSharp.ML.AIAgent/ml/models/TAGSageDouble/64ch/20e/GNN_state_pred_het_full differ diff --git a/VSharp.ML.AIAgent/ml/models/TAGSageDouble/64ch/20e/train_res b/VSharp.ML.AIAgent/ml/models/TAGSageDouble/64ch/20e/train_res new file mode 100644 index 000000000..f134a6c1d --- /dev/null +++ b/VSharp.ML.AIAgent/ml/models/TAGSageDouble/64ch/20e/train_res @@ -0,0 +1,20 @@ +Epoch: 000, Train Loss: 0.058653, Test Loss: 0.061931 +Epoch: 001, Train Loss: 0.033599, Test Loss: 0.035252 +Epoch: 002, Train Loss: 0.024017, Test Loss: 0.025401 +Epoch: 003, Train Loss: 0.018566, Test Loss: 0.020084 +Epoch: 004, Train Loss: 0.016096, Test Loss: 0.017555 +Epoch: 005, Train Loss: 0.014193, Test Loss: 0.015381 +Epoch: 006, Train Loss: 0.012142, Test Loss: 0.013107 +Epoch: 007, Train Loss: 0.011092, Test Loss: 0.011963 +Epoch: 008, Train Loss: 0.010418, Test Loss: 0.011354 +Epoch: 009, Train Loss: 0.008393, Test Loss: 0.008903 +Epoch: 010, Train Loss: 0.008293, Test Loss: 0.008749 +Epoch: 011, Train Loss: 0.007354, Test Loss: 0.007718 +Epoch: 012, Train Loss: 0.006936, Test Loss: 0.007338 +Epoch: 013, Train Loss: 0.006520, Test Loss: 0.006797 +Epoch: 014, Train Loss: 0.006349, Test Loss: 0.006665 +Epoch: 015, Train Loss: 0.005991, Test Loss: 0.006300 +Epoch: 016, Train Loss: 0.005723, Test Loss: 0.006024 +Epoch: 017, Train Loss: 0.005562, Test Loss: 0.005868 +Epoch: 018, Train Loss: 0.005451, Test Loss: 0.005739 +Epoch: 019, Train Loss: 0.005401, Test Loss: 0.005666 diff --git a/VSharp.ML.AIAgent/ml/models/TAGSageDouble/64ch/50e/GNN_state_pred_het_dict b/VSharp.ML.AIAgent/ml/models/TAGSageDouble/64ch/50e/GNN_state_pred_het_dict new file mode 100644 index 000000000..e7b24ee14 Binary files /dev/null and b/VSharp.ML.AIAgent/ml/models/TAGSageDouble/64ch/50e/GNN_state_pred_het_dict differ diff --git a/VSharp.ML.AIAgent/ml/models/TAGSageDouble/64ch/50e/GNN_state_pred_het_full b/VSharp.ML.AIAgent/ml/models/TAGSageDouble/64ch/50e/GNN_state_pred_het_full new file mode 100644 index 000000000..24f76d8fa Binary files /dev/null and b/VSharp.ML.AIAgent/ml/models/TAGSageDouble/64ch/50e/GNN_state_pred_het_full differ diff --git a/VSharp.ML.AIAgent/ml/models/TAGSageDouble/64ch/50e/train_res b/VSharp.ML.AIAgent/ml/models/TAGSageDouble/64ch/50e/train_res new file mode 100644 index 000000000..53b74e0f5 --- /dev/null +++ b/VSharp.ML.AIAgent/ml/models/TAGSageDouble/64ch/50e/train_res @@ -0,0 +1,50 @@ +Epoch: 000, Train Loss: 0.055234, Test Loss: 0.054694 +Epoch: 001, Train Loss: 0.025779, Test Loss: 0.025564 +Epoch: 002, Train Loss: 0.018780, Test Loss: 0.018762 +Epoch: 003, Train Loss: 0.014738, Test Loss: 0.014721 +Epoch: 004, Train Loss: 0.014752, Test Loss: 0.014738 +Epoch: 005, Train Loss: 0.011082, Test Loss: 0.011120 +Epoch: 006, Train Loss: 0.010220, Test Loss: 0.010273 +Epoch: 007, Train Loss: 0.009401, Test Loss: 0.009455 +Epoch: 008, Train Loss: 0.009154, Test Loss: 0.009261 +Epoch: 009, Train Loss: 0.008321, Test Loss: 0.008456 +Epoch: 010, Train Loss: 0.007499, Test Loss: 0.007654 +Epoch: 011, Train Loss: 0.007446, Test Loss: 0.007599 +Epoch: 012, Train Loss: 0.006967, Test Loss: 0.007105 +Epoch: 013, Train Loss: 0.006487, Test Loss: 0.006650 +Epoch: 014, Train Loss: 0.005996, Test Loss: 0.006176 +Epoch: 015, Train Loss: 0.005860, Test Loss: 0.006047 +Epoch: 016, Train Loss: 0.005585, Test Loss: 0.005773 +Epoch: 017, Train Loss: 0.005514, Test Loss: 0.005701 +Epoch: 018, Train Loss: 0.005469, Test Loss: 0.005659 +Epoch: 019, Train Loss: 0.005294, Test Loss: 0.005491 +Epoch: 020, Train Loss: 0.005149, Test Loss: 0.005359 +Epoch: 021, Train Loss: 0.005043, Test Loss: 0.005244 +Epoch: 022, Train Loss: 0.004933, Test Loss: 0.005152 +Epoch: 023, Train Loss: 0.004932, Test Loss: 0.005132 +Epoch: 024, Train Loss: 0.004678, Test Loss: 0.004873 +Epoch: 025, Train Loss: 0.004764, Test Loss: 0.004978 +Epoch: 026, Train Loss: 0.004552, Test Loss: 0.004733 +Epoch: 027, Train Loss: 0.004565, Test Loss: 0.004771 +Epoch: 028, Train Loss: 0.004463, Test Loss: 0.004661 +Epoch: 029, Train Loss: 0.004375, Test Loss: 0.004548 +Epoch: 030, Train Loss: 0.004280, Test Loss: 0.004473 +Epoch: 031, Train Loss: 0.004256, Test Loss: 0.004417 +Epoch: 032, Train Loss: 0.004228, Test Loss: 0.004398 +Epoch: 033, Train Loss: 0.004142, Test Loss: 0.004309 +Epoch: 034, Train Loss: 0.004160, Test Loss: 0.004335 +Epoch: 035, Train Loss: 0.004107, Test Loss: 0.004293 +Epoch: 036, Train Loss: 0.003954, Test Loss: 0.004124 +Epoch: 037, Train Loss: 0.003918, Test Loss: 0.004093 +Epoch: 038, Train Loss: 0.003874, Test Loss: 0.004027 +Epoch: 039, Train Loss: 0.003726, Test Loss: 0.003898 +Epoch: 040, Train Loss: 0.003805, Test Loss: 0.003967 +Epoch: 041, Train Loss: 0.003774, Test Loss: 0.003953 +Epoch: 042, Train Loss: 0.003694, Test Loss: 0.003852 +Epoch: 043, Train Loss: 0.003677, Test Loss: 0.003846 +Epoch: 044, Train Loss: 0.003673, Test Loss: 0.003846 +Epoch: 045, Train Loss: 0.003600, Test Loss: 0.003753 +Epoch: 046, Train Loss: 0.003545, Test Loss: 0.003703 +Epoch: 047, Train Loss: 0.003458, Test Loss: 0.003623 +Epoch: 048, Train Loss: 0.003457, Test Loss: 0.003616 +Epoch: 049, Train Loss: 0.003424, Test Loss: 0.003573 diff --git a/VSharp.ML.AIAgent/ml/models/TAGSageDouble/model.py b/VSharp.ML.AIAgent/ml/models/TAGSageDouble/model.py new file mode 100644 index 000000000..ddfb02525 --- /dev/null +++ b/VSharp.ML.AIAgent/ml/models/TAGSageDouble/model.py @@ -0,0 +1,65 @@ +import torch +from torch.nn import Linear +from torch_geometric.nn import TAGConv, SAGEConv, GraphConv + + +class StateModelEncoder(torch.nn.Module): + def __init__(self, hidden_channels, out_channels): + super().__init__() + self.conv1 = TAGConv(5, hidden_channels, 2) + self.conv2 = TAGConv(hidden_channels, hidden_channels, 3) # TAGConv + self.conv3 = GraphConv((-1, -1), hidden_channels) # SAGEConv + self.conv32 = SAGEConv((-1, -1), hidden_channels) # SAGEConv + self.conv4 = SAGEConv((-1, -1), hidden_channels) + self.conv42 = SAGEConv((-1, -1), hidden_channels) + self.conv5 = SAGEConv(-1, hidden_channels) + self.lin = Linear(hidden_channels, out_channels) + + def forward( + self, + game_x, + state_x, + edge_index_v_v, + edge_type_v_v, + edge_index_history_v_s, + edge_attr_history_v_s, + edge_index_in_v_s, + edge_index_s_s, + ): + game_x = self.conv1( + game_x, + edge_index_v_v, + ).relu() + + state_x = self.conv3( + (game_x, state_x), + edge_index_history_v_s, + edge_attr_history_v_s, + ).relu() + + state_x = self.conv32( + (game_x, state_x), + edge_index_history_v_s, + ).relu() + + state_x = self.conv4( + (game_x, state_x), + edge_index_in_v_s, + ).relu() + + state_x = self.conv42( + (game_x, state_x), + edge_index_in_v_s, + ).relu() + + state_x = self.conv2( + state_x, + edge_index_s_s, + ).relu() + + state_x = self.conv5( + state_x, + edge_index_s_s, + ).relu() + + return self.lin(state_x) diff --git a/VSharp.ML.AIAgent/ml/models/TAGSageSimple/32ch/1e/GNN_state_pred_het_dict b/VSharp.ML.AIAgent/ml/models/TAGSageSimple/32ch/1e/GNN_state_pred_het_dict new file mode 100644 index 000000000..0317e35e8 Binary files /dev/null and b/VSharp.ML.AIAgent/ml/models/TAGSageSimple/32ch/1e/GNN_state_pred_het_dict differ diff --git a/VSharp.ML.AIAgent/ml/models/TAGSageSimple/32ch/1e/GNN_state_pred_het_full b/VSharp.ML.AIAgent/ml/models/TAGSageSimple/32ch/1e/GNN_state_pred_het_full new file mode 100644 index 000000000..9ab9f6e00 Binary files /dev/null and b/VSharp.ML.AIAgent/ml/models/TAGSageSimple/32ch/1e/GNN_state_pred_het_full differ diff --git a/VSharp.ML.AIAgent/ml/models/TAGSageSimple/32ch/1e/train_res b/VSharp.ML.AIAgent/ml/models/TAGSageSimple/32ch/1e/train_res new file mode 100644 index 000000000..7d8b61772 --- /dev/null +++ b/VSharp.ML.AIAgent/ml/models/TAGSageSimple/32ch/1e/train_res @@ -0,0 +1 @@ +Epoch: 000, Train Loss: 0.141583, Test Loss: 0.145491 diff --git a/VSharp.ML.AIAgent/ml/models/TAGSageSimple/64ch/100e/GNN_state_pred_het_dict b/VSharp.ML.AIAgent/ml/models/TAGSageSimple/64ch/100e/GNN_state_pred_het_dict new file mode 100644 index 000000000..d185a743f Binary files /dev/null and b/VSharp.ML.AIAgent/ml/models/TAGSageSimple/64ch/100e/GNN_state_pred_het_dict differ diff --git a/VSharp.ML.AIAgent/ml/models/TAGSageSimple/64ch/100e/GNN_state_pred_het_full b/VSharp.ML.AIAgent/ml/models/TAGSageSimple/64ch/100e/GNN_state_pred_het_full new file mode 100644 index 000000000..56418e6a4 Binary files /dev/null and b/VSharp.ML.AIAgent/ml/models/TAGSageSimple/64ch/100e/GNN_state_pred_het_full differ diff --git a/VSharp.ML.AIAgent/ml/models/TAGSageSimple/64ch/100e/train_res b/VSharp.ML.AIAgent/ml/models/TAGSageSimple/64ch/100e/train_res new file mode 100644 index 000000000..70053e17f --- /dev/null +++ b/VSharp.ML.AIAgent/ml/models/TAGSageSimple/64ch/100e/train_res @@ -0,0 +1,100 @@ +Epoch: 000, Train Loss: 0.157131, Test Loss: 0.154543 +Epoch: 001, Train Loss: 0.098552, Test Loss: 0.098144 +Epoch: 002, Train Loss: 0.058471, Test Loss: 0.058878 +Epoch: 003, Train Loss: 0.035232, Test Loss: 0.036190 +Epoch: 004, Train Loss: 0.030188, Test Loss: 0.031279 +Epoch: 005, Train Loss: 0.025495, Test Loss: 0.026449 +Epoch: 006, Train Loss: 0.021157, Test Loss: 0.021989 +Epoch: 007, Train Loss: 0.018034, Test Loss: 0.018797 +Epoch: 008, Train Loss: 0.016052, Test Loss: 0.016735 +Epoch: 009, Train Loss: 0.013862, Test Loss: 0.014500 +Epoch: 010, Train Loss: 0.013893, Test Loss: 0.014568 +Epoch: 011, Train Loss: 0.012088, Test Loss: 0.012668 +Epoch: 012, Train Loss: 0.011636, Test Loss: 0.012203 +Epoch: 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