def selfPlayOnly(args):
g = nimGame(config)
nnet = nn(g)
coach_0 = Coach(g, nnet, args)
for i in range(args.numIters):
print("Self-play iteration: " + str(i))
nnet.load_checkpoint(args.load_folder_file[0],
args.load_folder_file[1])
coach_0.selfPlay()
def main():
log.info('Loading %s...', Game.__name__)
g = Game(6)
log.info('Loading %s...', nn.__name__)
nnet = nn(g)
if args.load_model:
log.info('Loading checkpoint "%s/%s"...', args.load_folder_file)
nnet.load_checkpoint(args.load_folder_file[0], args.load_folder_file[1])
else:
log.warning('Not loading a checkpoint!')
log.info('Loading the Coach...')
c = Coach(g, nnet, args)
if args.load_model:
log.info("Loading 'trainExamples' from file...")
c.loadTrainExamples()
log.info('Starting the learning process 🎉')
c.learn()
nimConfig = {'maxPileSize': 10, 'maxNumPile': 3, 'initialState': None}
if __name__ == "__main__":
print("Serial Flag: " + str(serialFlag))
if serialFlag:
if gameChoice == 0:
g = Game(6)
elif gameChoice == 1:
g = TicTacToeGame()
elif gameChoice == 2:
g = nimGame(nimConfig)
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_model:
print("Load trainExamples from file")
c.loadTrainExamples()
c.learn()
else:
def selfPlayOnly(args):
from Coach import Coach
from othello.OthelloGame import OthelloGame as Game
from othello.pytorch.NNet import NNetWrapper as nn
from utils import *
args = dotdict({
'numIters': 1000,
'numEps': 100,
'tempThreshold': 15,
'updateThreshold': 0.6,
'maxlenOfQueue': 200000,
'numMCTSSims': 25,
'arenaCompare': 40,
'cpuct': 1,
'checkpoint': './temp/',
'load_model': False,
'load_folder_file': ('/dev/models/8x100x50','best.pth.tar'),
})
if __name__=="__main__":
g = Game(6) #game env
nnet = nn(g) #network
if args.load_model:
nnet.load_checkpoint(args.load_folder_file[0], args.load_folder_file[1])
c = Coach(g, nnet, args)#set train para
c.learn()#train
