def set_agents(self, model_path_a, model_path_b, model_path_m):
# 플레이어 중 human이 있으면 pygame창에서 게임 실행, 아니면 텍스트만 출력
if model_path_a == 'human' or model_path_b == 'human':
game_mode = 'pygame'
else:
game_mode = 'text'
# env파일의 gamemode 설정
self.env = game.GameState(game_mode)
# 플레이어의 모델 설정 (human)
if model_path_a == 'random':
print('load player model:', model_path_a)
self.player = agents.RandomAgent(BOARD_SIZE)
elif model_path_a == 'puct':
print('load player model:', model_path_a)
self.player = agents.PUCTAgent(BOARD_SIZE, N_MCTS_PLAYER)
elif model_path_a == 'uct':
print('load player model:', model_path_a)
self.player = agents.UCTAgent(BOARD_SIZE, N_MCTS_PLAYER)
elif model_path_a == 'human':
print('load player model:', model_path_a)
self.player = agents.HumanAgent(BOARD_SIZE, self.env)
elif model_path_a == 'web':
print('load player model:', model_path_a)
self.player = agents.WebAgent(BOARD_SIZE)
else:
print('load player model:', model_path_a)
self.player = agents.ZeroAgent(BOARD_SIZE,
N_MCTS_PLAYER,
IN_PLANES_PLAYER,
noise=False)
self.player.model = model.PVNet(N_BLOCKS_PLAYER, IN_PLANES_PLAYER,
OUT_PLANES_PLAYER,
BOARD_SIZE).to(device)
state_a = self.player.model.state_dict()
my_state_a = torch.load(
model_path_a, map_location='cuda:0' if use_cuda else 'cpu')
for k, v in my_state_a.items():
if k in state_a:
state_a[k] = v
self.player.model.load_state_dict(state_a)
# 적 플레이어의 모델 설정 (
if model_path_b == 'random':
print('load enemy model:', model_path_b)
self.enemy = agents.RandomAgent(BOARD_SIZE)
elif model_path_b == 'puct':
print('load enemy model:', model_path_b)
self.enemy = agents.PUCTAgent(BOARD_SIZE, N_MCTS_ENEMY)
elif model_path_b == 'uct':
print('load enemy model:', model_path_b)
self.enemy = agents.UCTAgent(BOARD_SIZE, N_MCTS_ENEMY)
elif model_path_b == 'human':
print('load enemy model:', model_path_b)
self.enemy = agents.HumanAgent(BOARD_SIZE, self.env)
elif model_path_b == 'web':
print('load enemy model:', model_path_b)
self.enemy = agents.WebAgent(BOARD_SIZE)
else: # 이미 만들어진 데이터를 사용할땐 이 부분이 실행됨
print('load enemy model:', model_path_b)
# 적 에이전트 설정
self.enemy = agents.ZeroAgent(BOARD_SIZE,
N_MCTS_ENEMY,
IN_PLANES_ENEMY,
noise=False)
# 적 신경망 모델 설정 및 device(GPU)로 불러와 agents.ZeroAgent().model에 저장
self.enemy.model = model.PVNet(N_BLOCKS_ENEMY, IN_PLANES_ENEMY,
OUT_PLANES_ENEMY,
BOARD_SIZE).to(device)
state_b = self.enemy.model.state_dict() # dict형식의 신경망 파라미터의 텐서
my_state_b = torch.load(model_path_b,
map_location='cuda:0'
if use_cuda else 'cpu') # 저장한 파라미터 파일을 불러옴
# state_b에는 키 값으로 여러 레이어의 weight, bias 등과 그에 해당하는 value들이 저장됨
for k, v in my_state_b.items():
if k in state_b:
state_b[k] = v
self.enemy.model.load_state_dict(state_b) # 딥러닝 모델에 파라미터 설정
# monitor agent 위와 동일
self.monitor = agents.ZeroAgent(BOARD_SIZE,
N_MCTS_MONITOR,
IN_PLANES_ENEMY,
noise=False)
self.monitor.model = model.PVNet(N_BLOCKS_ENEMY, IN_PLANES_ENEMY,
OUT_PLANES_ENEMY,
BOARD_SIZE).to(device)
state_b = self.monitor.model.state_dict()
my_state_b = torch.load(model_path_m,
map_location='cuda:0' if use_cuda else 'cpu')
for k, v in my_state_b.items():
if k in state_b:
state_b[k] = v
self.monitor.model.load_state_dict(state_b)
def set_agents(self, model_path_a, model_path_b, model_path_m):
if model_path_a == 'human' or model_path_b == 'human':
game_mode = 'pygame'
else:
game_mode = 'text'
self.env = game.GameState(game_mode)
if model_path_a == 'random':
print('load player model:', model_path_a)
self.player = agents.RandomAgent(BOARD_SIZE)
elif model_path_a == 'puct':
print('load player model:', model_path_a)
self.player = agents.PUCTAgent(BOARD_SIZE, N_MCTS_PLAYER)
elif model_path_a == 'uct':
print('load player model:', model_path_a)
self.player = agents.UCTAgent(BOARD_SIZE, N_MCTS_PLAYER)
elif model_path_a == 'human':
print('load player model:', model_path_a)
self.player = agents.HumanAgent(BOARD_SIZE, self.env)
elif model_path_a == 'web':
print('load player model:', model_path_a)
self.player = agents.WebAgent(BOARD_SIZE)
else:
print('load player model:', model_path_a)
self.player = agents.ZeroAgent(BOARD_SIZE,
N_MCTS_PLAYER,
IN_PLANES_PLAYER,
noise=False)
self.player.model = model.PVNet(N_BLOCKS_PLAYER,
IN_PLANES_PLAYER,
OUT_PLANES_PLAYER,
BOARD_SIZE).to(device)
state_a = self.player.model.state_dict()
my_state_a = torch.load(
model_path_a, map_location='cuda:0' if use_cuda else 'cpu')
for k, v in my_state_a.items():
if k in state_a:
state_a[k] = v
self.player.model.load_state_dict(state_a)
if model_path_b == 'random':
print('load enemy model:', model_path_b)
self.enemy = agents.RandomAgent(BOARD_SIZE)
elif model_path_b == 'puct':
print('load enemy model:', model_path_b)
self.enemy = agents.PUCTAgent(BOARD_SIZE, N_MCTS_ENEMY)
elif model_path_b == 'uct':
print('load enemy model:', model_path_b)
self.enemy = agents.UCTAgent(BOARD_SIZE, N_MCTS_ENEMY)
elif model_path_b == 'human':
print('load enemy model:', model_path_b)
self.enemy = agents.HumanAgent(BOARD_SIZE, self.env)
elif model_path_b == 'web':
print('load enemy model:', model_path_b)
self.enemy = agents.WebAgent(BOARD_SIZE)
else:
print('load enemy model:', model_path_b)
self.enemy = agents.ZeroAgent(BOARD_SIZE,
N_MCTS_ENEMY,
IN_PLANES_ENEMY,
noise=False)
self.enemy.model = model.PVNet(N_BLOCKS_ENEMY,
IN_PLANES_ENEMY,
OUT_PLANES_ENEMY,
BOARD_SIZE).to(device)
state_b = self.enemy.model.state_dict()
my_state_b = torch.load(
model_path_b, map_location='cuda:0' if use_cuda else 'cpu')
for k, v in my_state_b.items():
if k in state_b:
state_b[k] = v
self.enemy.model.load_state_dict(state_b)
# monitor agent
self.monitor = agents.ZeroAgent(BOARD_SIZE,
N_MCTS_MONITOR,
IN_PLANES_ENEMY,
noise=False)
self.monitor.model = model.PVNet(N_BLOCKS_ENEMY,
IN_PLANES_ENEMY,
OUT_PLANES_ENEMY,
BOARD_SIZE).to(device)
state_b = self.monitor.model.state_dict()
my_state_b = torch.load(
model_path_m, map_location='cuda:0' if use_cuda else 'cpu')
for k, v in my_state_b.items():
if k in state_b:
state_b[k] = v
self.monitor.model.load_state_dict(state_b)
def main():
# Initialize agent & model
agent = agents.ZeroAgent(BOARD_SIZE, N_MCTS, IN_PLANES, noise=True)
agent.model = model.PVNet(N_BLOCKS, IN_PLANES, OUT_PLANES,
BOARD_SIZE).to(device)
agent.model.share_memory()
no_decay = ['bn', 'bias']
model_parameters = [{
'params': [
p for n, p in agent.model.named_parameters()
if not any(nd in n for nd in no_decay)
],
'weight_decay':
L2
}, {
'params': [
p for n, p in agent.model.named_parameters()
if any(nd in n for nd in no_decay)
],
'weight_decay':
0.0
}]
optimizer = optim.SGD(model_parameters, momentum=0.9, lr=LR)
scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, TOTAL_ITER)
logging.info(f'\nCUDA: {use_cuda}'
f'\nAGENT: {type(agent).__name__}'
f'\nMODEL: {agent.model}'
f'\nBOARD_SIZE: {BOARD_SIZE}'
f'\nN_MCTS: {N_MCTS}'
f'\nTAU_THRES: {TAU_THRES}'
f'\nN_BLOCKS: {N_BLOCKS}'
f'\nIN_PLANES: {IN_PLANES}'
f'\nOUT_PLANES: {OUT_PLANES}'
f'\nTOTAL_ITER: {TOTAL_ITER}'
f'\nMEMORY_SIZE: {MEMORY_SIZE}'
f'\nBATCH_SIZE: {BATCH_SIZE}'
f'\nLR: {LR}'
f'\nL2: {L2}')
# ====================== self-play & training ====================== #
model_path = './data/201215_11_12099_step_model.pickle'
if model_path is not None:
load_model(agent, optimizer, scheduler, model_path)
for n_iter in range(start_iter, TOTAL_ITER):
print('=' * 58)
print(' ' * 20 + ' {:2} Iteration '.format(n_iter) + ' ' * 20)
print('=' * 58)
logging.info(datetime.now().isoformat())
logging.info('=' * 58)
logging.info(' ' * 20 + " {:2} Iteration ".format(n_iter) + ' ' * 20)
logging.info('=' * 58)
datetime_now = datetime.now().strftime('%y%m%d')
train_memory = []
cur_memory = deque()
with futures.ProcessPoolExecutor(max_workers=N_PROCESS) as executor:
fs = [
executor.submit(self_play, agent, cur_memory, i)
for i in range(N_PROCESS)
]
for f in futures.as_completed(fs):
train_memory.extend(f.result())
train(agent, train_memory, optimizer, scheduler)
save_model(agent, optimizer, scheduler, datetime_now, n_iter, step)
save_dataset(train_memory, datetime_now, n_iter, step)
reset_iter(result)
# Global variables
rep_memory = deque(maxlen=MEMORY_SIZE)
cur_memory = deque()
step = 0
start_iter = 0
total_epoch = 0
result = {'Black': 0, 'White': 0, 'Draw': 0}
if USE_TENSORBOARD:
from tensorboardX import SummaryWriter
Writer = SummaryWriter()
# Initialize agent & model
Agent = agents.ZeroAgent(BOARD_SIZE,
game.WIN_STONES,
N_MCTS,
IN_PLANES,
noise=True)
Agent.model = model.PVNet(N_BLOCKS, IN_PLANES, OUT_PLANES,
BOARD_SIZE).to(device)
logging.warning('\nCUDA: {}'
'\nAGENT: {}'
'\nMODEL: {}'
'\nSEED: {}'
'\nBOARD_SIZE: {}'
'\nN_MCTS: {}'
'\nTAU_THRES: {}'
'\nN_BLOCKS: {}'
'\nIN_PLANES: {}'
'\nOUT_PLANES: {}'
if use_cuda:
torch.cuda.manual_seed_all(SEED)
# Global variables
rep_memory = deque(maxlen=MEMORY_SIZE)
cur_memory = deque()
step = 0
start_iter = 0
total_epoch = 0
result = {'Black': 0, 'White': 0, 'Draw': 0}
if USE_TENSORBOARD:
from tensorboardX import SummaryWriter
Writer = SummaryWriter()
# Initialize agent & model
Agent = agents.ZeroAgent(BOARD_SIZE, N_MCTS, IN_PLANES, noise=True)
Agent.model = model.PVNet(N_BLOCKS, IN_PLANES, OUT_PLANES,
BOARD_SIZE).to(device)
optimizer = optim.Adam(Agent.model.parameters(),
lr=LR,
weight_decay=L2,
eps=1e-6)
logging.warning('\nCUDA: {}'
'\nAGENT: {}'
'\nMODEL: {}'
'\nSEED: {}'
'\nBOARD_SIZE: {}'
'\nN_MCTS: {}'
'\nTAU_THRES: {}'
'\nN_BLOCKS: {}'