def play_games(n=5,
p1='quoridor_n5_v3_nnet_v2_torch_best.pth.tar',
p2='quoridor_n5_v3_nnet_v2_torch_best.pth.tar',
folder='/run/media/leleco/4EB5CC9A2FD2A5F9/dev/models/n5_v3/',
num_games=4,
numMCTSSims=100):
args = dotdict({
'numIters':
1000,
'numEps':
200, # Number of complete self-play games to simulate during a new iteration.
'tempThreshold':
15, #
'updateThreshold':
0.60,
# 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':
numMCTSSims, # Number of games moves for MCTS to simulate.
'arenaCompare':
num_games,
# Number of games to play during arena play to determine if new net will be accepted.
'cpuct':
2.5,
'cpuct_base':
19652,
'cpuct_mult':
2,
'checkpoint':
'./temp/',
'load_model':
True,
'load_folder_file':
('./dev/models/v0_n5', 'quoridor_n5_v2_nnet_v2_torch_best.pth.tar'),
'numItersForTrainExamplesHistory':
20,
})
log.info('Loading %s...', Game.__name__)
g = Game(n)
log.info('Loading %s...', nn.__name__)
nnet = nn(g)
pnet = nn(g)
nnet.load_checkpoint(folder=folder, filename=p1)
pnet.load_checkpoint(folder=folder, filename=p2)
pmcts = MCTS(g, pnet, args)
nmcts = MCTS(g, nnet, args)
log.info('PITTING AGAINST PREVIOUS VERSION')
arena = Arena(lambda x: np.argmax(pmcts.getActionProb(x, temp=1)),
lambda x: np.argmax(nmcts.getActionProb(x, temp=1)), g,
g.display)
pwins, nwins, draws = arena.playGames(args.arenaCompare, verbose=True)
log.info('NEW/PREV WINS : %d / %d ; DRAWS : %d' % (nwins, pwins, draws))
def train(n=9):
args = dotdict({
'numIters': 1000,
'numEps': 100, # Number of complete self-play games to simulate during a new iteration.
'tempThreshold': 15, #
'updateThreshold': 0.60,
# 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': 100, # 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': 2.5,
'cpuct_base': 19652,
'cpuct_mult': 2,
'checkpoint': '/run/media/leleco/4EB5CC9A2FD2A5F9/dev/models/n9_v3/',
'load_model': True,
'load_examples': True,
'load_folder_file': ('/run/media/leleco/4EB5CC9A2FD2A5F9/dev/models/n9_v3/',
'quoridor_n9_v3_nnet_v2_torch_checkpoint.pth.tar'),
'numItersForTrainExamplesHistory': 20,
})
nn_args = dotdict({
'lr': 0.001,
'dropout': 0.3,
'epochs': 10,
'batch_size': 128,
'cuda': torch.cuda.is_available(),
'num_channels': 256,
})
log.info('Loading %s...', Game.__name__)
g = Game(n)
log.info('Loading %s...', nn.__name__)
nnet = nn(g, nn_args)
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_examples:
log.info("Loading 'trainExamples' from file...")
c.loadTrainExamples()
log.info('Starting the learning process 🎉')
c.learn()
def main():
log.info('Loading %s...', Game.__name__)
g = Game(5)
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_examples:
log.info("Loading 'trainExamples' from file...")
c.loadTrainExamples()
log.info('Starting the learning process 🎉')
c.learn()
from utils import *
args = dotdict({
'numIters': 10, #1000
'numEps': 50, #100
'tempThreshold': 15,
'updateThreshold': 0.55,
'maxlenOfQueue': 200000,
'numMCTSSims': 50,
'arenaCompare': 40,
'cpuct': 1,
'checkpoint': './temp/',
'load_model': True,
'load_folder_file': ('./temp', '5x5best.pth.tar'),
'numItersForTrainExamplesHistory': 20,
})
if __name__ == "__main__":
g = Game(5)
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()