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from Coach import Coach
from quoridor.QuoridorGame import QuoridorGame as Game
from quoridor.pytorch.NNet import NNetWrapper as nn
from utils import *
args = dotdict({
'numIters': 500, #1000
'numEps': 50, #100
'tempThreshold': 0.3,
'updateThreshold': 0.55,
'maxlenOfQueue': 200000,
'numMCTSSims': 300,
'arenaCompare': 20,
'cpuct': 2.4,
'checkpoint': './temp/',
'load_model': False,
'load_folder_file': ('./temp','best.pth.tar'),
'load_folder_examples_file': ('./temp','checkpoint_34.pth.tar'),
'load_examples': False,
'numItersForTrainExamplesHistory': 100,
})
if __name__=="__main__":
g = Game(5)
print(g.getBoardSize())
nnet = nn(g)
if args.load_model:
nnet.load_checkpoint(args.load_folder_file[0], args.load_folder_file[1])
c = Coach(g, nnet, args)
if args.load_examples:
print("Load trainExamples from file")
c.loadTrainExamples()
c.learn()
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