def main():
log.info('Loading %s...', Game.__name__)
g = Game(8)
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()
run = wandb.init(project=args.wandb_project,config=args,reinit=True)
log.info('Starting the learning process 🎉')
c.learn()
run.finish()
def learn(q):
game = Game(8)
nnet = onnet(game)
for i in range(1, 2):
print('------ITER ' + str(i) + '------')
iterationTrainExamples = deque([], maxlen=200000)
for eps in range(1):
mcts = MCTS(game, nnet)
iterationTrainExamples += executeEpisode(game, mcts)
print('1')
q.put(iterationTrainExamples)
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()
def collect_data(q):
args = dotdict({
'numIters': 11,
'numEps': 50,
'tempThreshold': 15,
'updateThreshold': 0.5,
'maxlenOfQueue': 200000,
'numMCTSSims': 100,
'arenaCompare': 2,
'cpuct': 1,
'checkpoint': './temp/',
'load_model': True,
'load_folder_file': ('./temp/', 'best.pth.tar'),
'numItersForTrainExamplesHistory': 20,
})
game = Game(8)
nnet = nn(game)
iterationTrainExamples = deque([], maxlen=args.maxlenOfQueue)
eps_time = AverageMeter()
bar = Bar('Self Play', max=args.numEps)
end = time.time()
for eps in range(int(args.numEps / 2)):
print(eps)
mcts = MCTS(game, nnet, args) # reset search tree
iterationTrainExamples += executeEpisode(mcts, game)
# bookkeeping + plot progress
eps_time.update(time.time() - end)
end = time.time()
bar.suffix = '({eps}/{maxeps}) Eps Time: {et:.3f}s | Total: {total:} | ETA: {eta:}'.format(
eps=eps + 1,
maxeps=args.numEps,
et=eps_time.avg,
total=bar.elapsed_td,
eta=bar.eta_td)
bar.next()
q.put(iterationTrainExamples)
bar.finish()
50, # Number of games to play during arena play to determine if new net will be accepted.
'cpuct': 1,
'checkpoint': checkpoint,
'load_model': True,
'load_folder_file': (checkpoint, 'checkpoint.pth.tar'),
'numItersForTrainExamplesHistory': 20,
})
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")
next_state, next_player = game.getNextState(state, 1, action)
next_state = game.getCanonicalForm(next_state, -1)
node = str(next_state.reshape(-1) + 1)[1:-1]
cannonical_states[vertex][action]['winner'] = game.getGameEnded(next_state, 1) # * np.abs(next_state.sum())
cannonical_states[vertex][action]['next_node'] = node
if node not in seen:
seen[node] = 1
# seen.add(node)
queue.append(node)
else:
for action, data in cannonical_states[vertex].items():
node = data['next_node']
if node not in seen:
seen[node] = 1
# seen.add(node)
queue.append(node)
N = len(cannonical_states)
if N%10000 == 0:
print('\rstates: {}'.format(N), end='')
cannonical_states.sync()
print()
cannonical_states.close()
return
n = 6
game = Game(n)
board = game.getInitBoard()
bfs_cannonical(game, board, './data/6by6', first_player=1)
return trainExamplesHistory
if __name__ == '__main__':
args = dotdict({
'numIters': 11,
'numEps': 50,
'tempThreshold': 15,
'updateThreshold': 0.5,
'maxlenOfQueue': 200000,
'numMCTSSims': 100,
'arenaCompare': 2,
'cpuct': 1,
'checkpoint': './temp/',
'load_model': True,
'load_folder_file': ('./temp/', 'best.pth.tar'),
'numItersForTrainExamplesHistory': 20,
})
g = Game(8)
nnet = nn(g)
if args.load_model:
nnet.load_checkpoint(args.load_folder_file[0],
args.load_folder_file[1])
trainExamplesHistory = []
if args.load_model:
print("Load trainExamples from file")
trainExamplesHistory = loadTrainExamples(args)
learn(args, nnet, g, trainExamplesHistory)
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'),
'numItersForTrainExamplesHistory': 20,
})
if __name__=="__main__":
g = Game()
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()
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
'numIters': 1, #original value: 1000
'numEps': 2, #orginal value: 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'),
'numItersForTrainExamplesHistory': 20,
})
if __name__ == "__main__":
g = Game(6) #returns the game object (constructor)
nnet = nn(g) #NNet class returns NNetWrapper for the game object (g)
print(
'----------------------********************-----------------------*********************-----------------'
)
if args.load_model:
nnet.load_checkpoint(args.load_folder_file[0],
args.load_folder_file[1])
print('main.py==> ', 'args: ', args)
c = Coach(
g, nnet, args
) #returns the Coach object with params(game_object, NeuralNet, argument values)
if args.load_model:
print("Load trainExamples from file")
c.loadTrainExamples()
iterationTrainExamples = deque([], maxlen=200000)
for eps in range(1):
mcts = MCTS(game, nnet)
iterationTrainExamples += executeEpisode(game, mcts)
print('1')
q.put(iterationTrainExamples)
if __name__ == '__main__':
q = Manager().Queue()
p1 = Process(target=learn, args=(q, ))
p2 = Process(target=learn, args=(q, ))
p1.start()
p2.start()
p1.join()
p2.join()
res1 = q.get()
res2 = q.get()
res = []
res.append(res1)
res.append(res2)
trainExamples = []
game = Game(8)
nnet = onnet(game)
for e in res:
trainExamples.extend(e)
nnet.train(trainExamples)
#print(res1)
pass
