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afab5da | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | import os
import torch
from Games.TicTacToe.TicTacToe import TicTacToe
from Games.TicTacToe.TicTacToeNN import ResNet
from Alpha_Zero_Parallel import Alpha_Zero
GAME = "TicTacToe"
args = {
"MODEL_PATH" : os.path.join(os.getcwd(), "Games", GAME, "models_n_optimizers"),
"SAVE_GAME_PATH" : os.path.join(os.getcwd(), "Games", GAME, "games"),
"EXPLORATION_CONSTANT" : 2,
"TEMPERATURE" : 2,
"DIRICHLET_EPSILON" : 0.25,
"DIRICHLET_ALPHA" : 0.3,
"ROOT_RANDOMNESS": True,
"ADVERSARIAL" : True,
"NO_OF_SEARCHES" : 800,
"NO_ITERATIONS" : 1,
"SELF_PLAY_ITERATIONS" : 3000,
"PARALLEL_PROCESS" : 500,
"EPOCHS" : 1,
"BATCH_SIZE" : 128,
"MODEL_CHECK_GAMES" : 200,
"WIN_RATIO_FOR_SAVING": 0.5,
}
game = TicTacToe()
torch.backends.cudnn.enabled = False
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device, "in use")
model = ResNet(game, 9, 128, device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.001, weight_decay = 0.0001)
state = game.initialise_state()
alpha_zero = Alpha_Zero(game, args, model, optimizer)
alpha_zero.learn()
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