| 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() |
|
|
