from alphazero.agents.random import RandomPlayAgent
from alphazero.alphazero.nn_modules.nets import dual_resnet, resnet
from alphazero.alphazero.state_encoders.ttt_state_encoder import TicTacToeStateEncoder
from alphazero.games.tictactoe import TicTacToeGame
from alphazero.util.logging_config import setup_logger
from alphazero.util.pit_agents import pit
MODELS = [
'dualres_comp',
'dualres_nocomp',
'res_comp',
'res_nocomp',
]
NUM_GAMES = 100
setup_logger('experiment_logs', 'ttt_comp_random.log')
logger = logging.getLogger(__name__)
device = 'cuda' if torch.cuda.is_available() else 'cpu'
logger.info('Training on %s', device)
game = TicTacToeGame()
state_encoder = TicTacToeStateEncoder(device)
config = {
'num_simulations': 50,
'c_puct': 1.,
'game_size': 3,
'num_history': 1,
'num_resnet_filters': 256,
'value_head_hidden_dim': 256,
'device': device
}
import yaml
from torchsummary import summary
import alphazero.alphazero.nn_modules.nets as nets
from alphazero.alphazero.mcts import MonteCarloTreeSearch
from alphazero.alphazero.state_encoders.gomoku_state_encoder import GomokuStateEncoder
from alphazero.alphazero.trainer import AlphaZeroTrainer
from alphazero.games.gomoku import GomokuGame
from alphazero.util.logging_config import setup_logger
with open('gomoku.yaml', 'r') as f:
config = yaml.safe_load(f)
config['device'] = 'cuda' if torch.cuda.is_available() else 'cpu'
setup_logger(config['log_dir'], 'train.log')
logger = logging.getLogger(__name__)
logger.info('** Training on %s **', config['device'])
logger.info(config)
if __name__ == '__main__':
game = GomokuGame(config['game_size'])
state_encoder = GomokuStateEncoder(config['device'], config['num_history'])
net = getattr(nets, config['nn_arch'])(game, config)
summary(net,
input_size=(config['num_history'], config['game_size'],
config['game_size']),
batch_size=config['batch_size'])