import Game
import Players
import MCTS
import Arena
import numpy as np
import NNet
from utils import *
game = Game.Game2048()
player = Players.RandomPlayer(game)
nnet_player = NNet.NNetWrapper(game)
nnet_player.load_checkpoint('./temp', 'best.pth.tar')
args = dotdict({
'numIters': 1000,
'numEps': 100, # Number of complete self-play games to simulate during a new iteration.
'tempThreshold': 15, #
'updateThreshold': 0.6,
# During arena playoff, new neural net will be accepted if threshold or more of games are won.
'maxlenOfQueue': 200000, # Number of game examples to train the neural networks.
'numMCTSSims': 25, # Number of games moves for MCTS to simulate.
'arenaCompare': 40, # Number of games to play during arena play to determine if new net will be accepted.
'cpuct': 1,
'checkpoint': './temp/',
'load_model': False,
'load_folder_file': ('/dev/models/8x100x50', 'best.pth.tar'),
'numItersForTrainExamplesHistory': 20,
})
mcts = MCTS.MCTS(game, nnet_player, args)
