def run():
n = 5
width, height = 8, 8
model_file = 'best_policy_8_8_5.model'
try:
board = Board(width=width, height=height, n_in_row=n)
game = Game(board)
################ human VS AI ###################
# MCTS player with the policy_value_net trained by AlphaZero algorithm
# policy_param = pickle.load(open(model_file, 'rb'))
# best_policy = PolicyValueNet(width, height, net_params = policy_param)
# mcts_player = MCTSPlayer(best_policy.policy_value_fn, c_puct=5, n_playout=400)
# MCTS player with the trained policy_value_net written in pure numpy
try:
policy_param = pickle.load(open(model_file, 'rb'))
except:
policy_param = pickle.load(open(model_file, 'rb'), encoding = 'bytes') # To support python3
best_policy = PolicyValueNetNumpy(width, height, policy_param)
mcts_player = MCTSPlayer(best_policy.policy_value_fn, c_puct=5, n_playout=400) # set larger n_playout for better performance
# uncomment the following line to play with pure MCTS (its much weaker even with a larger n_playout)
# mcts_player = MCTS_Pure(c_puct=5, n_playout=1000)
# human player, input your move in the format: 2,3
human = Human()
# set start_player=0 for human first
game.start_play(human, mcts_player, start_player=1, is_shown=1)
except KeyboardInterrupt:
print('\n\rquit')
def run():
n = 5
width, height = 15, 15
model_file = 'best_policy.model'
try:
board = Board(width=width, height=height, n_in_row=n)
game = Game(board)
# ############### human VS AI ###################
# load the trained policy_value_net in either Theano/Lasagne, PyTorch or TensorFlow
# best_policy = PolicyValueNet(width, height, model_file = model_file)
# mcts_player = MCTSPlayer(best_policy.policy_value_fn, c_puct=5, n_playout=400)
# load the provided model (trained in Theano/Lasagne) into a MCTS player written in pure numpy
try:
policy_param = pickle.load(open(model_file, 'rb'))
except:
policy_param = pickle.load(open(model_file, 'rb'),
encoding='bytes') # To support python3
best_policy = PolicyValueNetNumpy(width, height, policy_param)
mcts_player = MCTSPlayer(
best_policy.policy_value_fn, c_puct=5,
n_playout=400) # set larger n_playout for better performance
# uncomment the following line to play with pure MCTS (it's much weaker even with a larger n_playout)
# mcts_player = MCTS_Pure(c_puct=5, n_playout=1000)
# human player, input your move in the format: 2,3
human = Human()
# set start_player=0 for human first
game.start_play(human, mcts_player, start_player=1, is_shown=1)
except KeyboardInterrupt:
print('\n\rquit')
def receive_gameSet():
receive_data = request.get_json()
print(receive_data)
board = Board(width=9, height=9, n_in_row=5)
board.init_board(1)
hard_idx = receive_data['hard_idx']
hards = [2500, 5000, 7500, 10000, 12500, 15000, 17500, 20000]
model_file = f'./model/policy_9_{hards[hard_idx]}.model'
policy_param = pickle.load(open(model_file, 'rb'), encoding='bytes')
best_policy = PolicyValueNetNumpy(9, 9, policy_param)
mcts_player = MCTSPlayer(best_policy.policy_value_fn,
c_puct=5,
n_playout=400)
# AI가 先인 경우, 1번 먼저 돌을 둔다.
ai_move = mcts_player.get_action(board)
ai_loc = board.move_to_location(ai_move)
states_loc = [[0] * 9 for _ in range(9)]
states_loc[ai_loc[0]][ai_loc[1]] = 2
data = {
'ai_moved': list(map(int, ai_loc)),
'states_loc': states_loc,
'message': None
}
return jsonify(data)
def run():
n = 5
width, height = 9, 9
print("이 오목 인공지능은 9x9 환경에서 동작합니다.")
print("현재 가능한 난이도(정책망의 학습 횟수) 목록 : [ 2500, 5000, 7500, 10000, 12500, 15000, 17500, 20000 ]")
print("난이도를 입력하세요.")
hard = int(input())
model_file = f'./omok_AI/model/policy_9_{hard}.model' # colab
# model_file = f'./model/policy_9_{hard}.model' # local
print("자신이 선공(흑)인 경우에 0, 후공(백)인 경우에 1을 입력하세요.")
order = int(input())
if order not in [0,1] : return "강제 종료"
board = Board(width=width, height=height, n_in_row=n)
game = Game(board)
# 이미 제공된 model을 불러와서 학습된 policy_value_net을 얻는다.
policy_param = pickle.load(open(model_file, 'rb'), encoding='bytes')
best_policy = PolicyValueNetNumpy(width, height, policy_param)
mcts_player = MCTSPlayer(best_policy.policy_value_fn, c_puct=5, n_playout=400) # n_playout값 : 성능
human = Human()
# start_player = 0 → 사람 선공 / 1 → AI 선공
game.start_play(human, mcts_player, start_player=order, is_shown=1)
def __init__(self):
policy_param = pickle.load(open('best_policy_8_8_5.model', 'rb'),
encoding='bytes')
best_policy = PolicyValueNetNumpy(8, 8, policy_param)
mcts_player = MCTSPlayer(best_policy.policy_value_fn, c_puct=5, n_playout=400)
human = Human()
self.game = Game(Board(width=8, height=8, n_in_row=5), human, mcts_player)
def run(width=15, height=15):
n = 5
#width, height = 15, 15
model_file = 'current_policy.model'
try:
board = Board(width=width, height=height, n_in_row=n)
game = Game(board)
instruction = input()
if isinstance(instruction, str) and instruction == "BEGIN":
begin = 1
first_move = 0
elif isinstance(instruction, str) and instruction.split()[0] == "TURN":
first_location = [
int(n, 10) for n in instruction.split()[1].split(",")
]
first_move = board.location_to_move(first_location)
begin = 0
else:
begin = 0
first_move = 0
################ human VS AI ###################
# MCTS player with the policy_value_net trained by AlphaZero algorithm
# policy_param = pickle.load(open(model_file, 'rb'))
# best_policy = PolicyValueNet(width, height, net_params = policy_param)
# mcts_player = MCTSPlayer(best_policy.policy_value_fn, c_puct=5, n_playout=400)
# MCTS player with the trained policy_value_net written in pure numpy
#try:
# policy_param = pickle.load(open(model_file, 'rb'))
#except:
policy_param = pickle.load(open(model_file, 'rb'),
encoding='bytes') # To support python3
best_policy = PolicyValueNetNumpy(width, height, policy_param)
mcts_player = MCTSPlayer(
best_policy.policy_value_fn, c_puct=5,
n_playout=1000) # set larger n_playout for better performance
#uncomment the following line to play with pure MCTS (its much weaker even with a larger n_playout)
# mcts_player = MCTS_Pure(c_puct=5, n_playout=10)
# human player, input your move in the format: 2,3
human1 = Human()
#human2 = Human()
#print(human.__str__())
# set start_player=0 for human first
game.start_play(human1,
mcts_player,
begin,
is_shown=1,
first_move=first_move)
except KeyboardInterrupt:
print('\n\rquit')
def __init__(self, root_state):
self.root = Node(root_state, 999999999)
# policy value function from pretrained model
from gomoku_state import GRID_LEN
from policy_value_net_numpy import PolicyValueNetNumpy
if GRID_LEN == 6:
model_file = 'best_policy_6_6_4.model'
else:
model_file = 'best_policy_8_8_5.model'
import pickle
policy_params = pickle.load(open(model_file, 'rb'))
nn = PolicyValueNetNumpy(GRID_LEN, GRID_LEN, policy_params)
self.policy_value_fn = nn.policy_value_fn
def run():
n = 5
width, height = 10, 10
model_file = 'best_policy_10_10_5.model'
#model_file = 'best_policy_8_8_5.model'
try:
board = Board(width=width, height=height, n_in_row=n)
game = Game(board)
# policyvaluenet = PolicyValueNet(board_width=width, board_height=height, model_file=model_file, use_gpu=False)
# ############### human VS AI ###################
# load the trained policy_value_net in either Theano/Lasagne, PyTorch or TensorFlow
# best_policy = PolicyValueNet(width, height, model_file = model_file)
# mcts_player = MCTSPlayer(best_policy.policy_value_fn, c_puct=5, n_playout=400)
# load the provided model (trained in Theano/Lasagne) into a MCTS player written in pure numpy
try:
policy_param = pickle.load(open(model_file, 'rb'))
except:
policy_param = pickle.load(open(model_file, 'rb'),
encoding='bytes') # To support python3
# keys = ['conv1.weight', 'conv1.bias', 'conv2.weight', 'conv2.bias', 'conv3.weight', 'conv3.bias'
# , 'act_conv1.weight', 'act_conv1.bias', 'act_fc1.weight', 'act_fc1.bias'
# , 'val_conv1.weight', 'val_conv1.bias', 'val_fc1.weight', 'val_fc1.bias', 'val_fc2.weight',
# 'val_fc2.bias']
# param_pytorch = OrderedDict()
# for key, value in zip(keys, policy_param):
# if 'fc' in key and 'weight' in key:
# param_pytorch[key] = torch.FloatTensor(value.T)
# elif 'conv' in key and 'weight' in key:
# param_pytorch[key] = torch.FloatTensor(value[:, :, ::-1, ::-1].copy())
# else:
# param_pytorch[key] = torch.FloatTensor(value)
best_policy = PolicyValueNetNumpy(width, height, policy_param)
mcts_player = MCTSPlayer(
best_policy.policy_value_fn, c_puct=5,
n_playout=400) # set larger n_playout for better performance
# uncomment the following line to play with pure MCTS (it's much weaker even with a larger n_playout)
# mcts_player = MCTS_Pure(c_puct=5, n_playout=1000)
# human player, input your move in the format: 2,3
human = Human()
# set start_player=0 for human first
game.start_play(human, mcts_player, start_player=0, is_shown=1)
except KeyboardInterrupt:
print('\n\rquit')
def run():
n = 5
width, height = 8, 8
model_file = 'best_policy_8_8_5.model'
try:
board = Board(width=width, height=height, n_in_row=n)
game = Game(board)
# ############### human VS AI ###################
# load the trained policy_value_net in either Theano/Lasagne, PyTorch or TensorFlow
# best_policy = PolicyValueNet(width, height, model_file = model_file)
# mcts_player = MCTSPlayer(best_policy.policy_value_fn, c_puct=5, n_playout=400)
# load the provided model (trained in Theano/Lasagne) into a MCTS player written in pure numpy
try:
policy_param = pickle.load(open(model_file, 'rb'))
except:
policy_param = pickle.load(open(model_file, 'rb'),
encoding='bytes') # To support python3
best_policy = PolicyValueNetNumpy(width, height, policy_param)
mcts_player = MCTSPlayer(
best_policy.policy_value_fn, c_puct=5,
n_playout=400) # set larger n_playout for better performance
#pure mcts player
#make quick_play=True to enable a weaker but much faster roll-out player without mcts
pure_mcts_player = MCTS_Pure(c_puct=1, n_playout=600, quick_play=False)
roll_out_player = MCTS_Pure(quick_play=True)
#1.run with two human player
game.start_play_with_UI()
#2.run with alpha zero nerutral network AI, and my quick roll-out AI
#game.start_play_with_UI(AI=mcts_player, AI2 = roll_out_player)
#3.run with alpha zero nerutral network AI, and my pure mcts AI
#game.start_play_with_UI(AI=mcts_player, AI2 = pure_mcts_player)
except KeyboardInterrupt:
print('\n\rquit')
def get_mcts_player(player_index=1):
"""
Get an mcts player, an index of 1 corresponds to first player (typically
human) and an index of 2 corresponds to the second player (typically AI
opponent).
"""
board = Board()
board.init_board()
size = 8
model_file = '../AlphaZero_Gomoku/best_policy_8_8_5.model'
try:
policy_param = pickle.load(open(model_file, 'rb'))
except Exception:
policy_param = pickle.load(open(model_file, 'rb'), encoding='bytes')
best_policy = PolicyValueNetNumpy(size, size, policy_param)
mcts_player = MCTSPlayer(best_policy.policy_value_fn, c_puct=5,
n_playout=200)
mcts_player.set_player_ind(player_index)
return mcts_player
def parse_agent(agent_type, filename):
if agent_type == 'mcts_a0':
model_file = 'best_policy_8_8_5.model'
if filename:
model_file = filename
# load the trained policy_value_net in either Theano/Lasagne, PyTorch or TensorFlow
# best_policy = PolicyValueNet(width, height, model_file = model_file)
# mcts_player = MCTSPlayer(best_policy.policy_value_fn, c_puct=5, n_playout=400)
# load the provided model (trained in Theano/Lasagne) into a MCTS player written in pure numpy
try:
policy_param = pickle.load(open(model_file, 'rb'))
except:
policy_param = pickle.load(
open(model_file,
'rb'), encoding='bytes') # To support python3
best_policy = PolicyValueNetNumpy(width, height, policy_param)
player = MCTSPlayer(
best_policy.policy_value_fn, c_puct=5, n_playout=400
) # set larger n_playout for better performance
elif agent_type == 'mcts_pure':
player = MCTS_Pure(c_puct=5, n_playout=1000)
elif agent_type == 'minmax':
player = Minimax()
elif agent_type == 'dqn':
model_file = 'output/v_1/epoch_100/agent_2.pkl'
if filename:
model_file = filename
player = DQNPlayer(model_file)
elif agent_type == 'human':
player = Human()
else:
player = Human()
print('Illegal Agent Type. Defaulting to human player.')
return player
def player_moved():
receive_data = request.get_json()
print(receive_data)
board = Board(width=9, height=9, n_in_row=5)
board.init_board(0)
states_loc = receive_data['states_loc']
if states_loc != None:
board.states_loc = states_loc
board.states_loc_to_states()
# 플레이어가 둔 돌의 위치를 받고
player_loc = receive_data['player_moved']
player_move = board.location_to_move(player_loc)
board.do_move(player_move)
board.set_forbidden() # 금수 자리 업데이트
print(np.array(board.states_loc))
print(board.states)
# 승리 판정 (플레이어가 이겼는지)
end, winner = board.game_end()
if end:
if winner == -1: message = "tie"
else: message = winner
data = {
'ai_moved': None,
'forbidden': board.forbidden_locations,
'message': message
}
return jsonify(data)
# AI가 둘 위치를 보낸다.
# 난이도에 해당하는 player 불러옴.
hard_idx = receive_data['hard_idx']
hards = [2500, 5000, 7500, 10000, 12500, 15000, 17500, 20000]
model_file = f'./model/policy_9_{hards[hard_idx]}.model'
policy_param = pickle.load(open(model_file, 'rb'), encoding='bytes')
best_policy = PolicyValueNetNumpy(9, 9, policy_param)
mcts_player = MCTSPlayer(best_policy.policy_value_fn,
c_puct=5,
n_playout=400)
ai_move = mcts_player.get_action(board)
ai_loc = board.move_to_location(ai_move)
board.do_move(ai_move)
board.set_forbidden() # 금수 자리 업데이트
print(np.array(board.states_loc))
# 승리 판정 (AI가 이겼는지)
message = None
end, winner = board.game_end()
if end:
if winner == -1: message = "tie"
else: message = winner
data = {
'ai_moved': list(map(int, ai_loc)),
'states_loc': board.states_loc,
'forbidden': board.forbidden_locations,
'message': message
}
return jsonify(data)
def run_game(surface, omok, menu):
omok.init_game() # board = 0 ~ 7
omok.id = 0
board = Board(width=8, height=8, n_in_row=5)
game = Game(board)
model_file = 'best_policy_8_8_5.model'
while True:
# balck stone
if (omok.id == 0):
a = random.randint(3, 4)
b = random.randint(3, 4)
print('first stone', a, b)
omok.board[a][b] = 1
omok.check_board_black(a, b)
human = Human(a, b)
game.start_play(human)
elif (omok.turn == 1 and omok.id > 1):
# 4 defense, 4attack, 3 defense, 3 attack
# 4 defense code
# right under 4 defense
for x in range(0, 4):
for y in range(0, 4): ## 0~11 +4 max = 15
if (omok.board[x][y] == 2 and omok.board[x + 1][y + 1] == 2
and omok.board[x + 2][y + 2] == 2
and omok.board[x + 3][y + 3] == 2
and omok.turn == 1):
if (omok.board[x + 4][y + 4] == 0):
omok.board[x + 4][y + 4] = 1
omok.check_board_black(x + 4, y + 4)
human = Human(x + 4, y + 4)
game.start_play(human)
elif (x > 0 and y > 0
and omok.board[x - 1][y - 1] == 0):
omok.board[x - 1][y - 1] = 1
omok.check_board_black(x - 1, y - 1)
human = Human(x - 1, y - 1)
game.start_play(human)
elif (omok.board[x][y] == 2
and omok.board[x + 1][y + 1] == 0
and omok.board[x + 2][y + 2] == 2
and omok.board[x + 3][y + 3] == 2
and omok.turn == 1):
omok.board[x + 1][y + 1] = 1
omok.check_board_black(x + 1, y + 1)
human = Human(x + 1, y + 1)
game.start_play(human)
elif (omok.board[x][y] == 2
and omok.board[x + 1][y + 1] == 2
and omok.board[x + 2][y + 2] == 0
and omok.board[x + 3][y + 3] == 2
and omok.turn == 1):
omok.board[x + 2][y + 2] = 1
omok.check_board_black(x + 2, y + 2)
human = Human(x + 2, y + 2)
game.start_play(human)
# left under 4 defense
for x in range(4, 7): # 3 ~ 14
for y in range(0, 4):
if (omok.board[x][y] == 2 and omok.board[x - 1][y + 1] == 2
and omok.board[x - 2][y + 2] == 2
and omok.board[x - 3][y + 3] == 2
and omok.turn == 1):
if (y > 0 and omok.board[x + 1][y - 1] == 0):
omok.board[x + 1][y - 1] = 1
omok.check_board_black(x + 1, y - 1)
human = Human(x + 1, y - 1)
game.start_play(human)
elif (omok.board[x - 4][y + 4] == 0):
omok.board[x - 4][y + 4] = 1
omok.check_board_black(x - 4, y + 4)
human = Human(x - 4, y + 4)
game.start_play(human)
elif (omok.board[x][y] == 2
and omok.board[x - 1][y + 1] == 0
and omok.board[x - 2][y + 2] == 2
and omok.board[x - 3][y + 3] == 2
and omok.turn == 1):
omok.board[x - 1][y + 1] = 1
omok.check_board_black(x - 1, y + 1)
human = Human(x - 1, y + 1)
game.start_play(human)
elif (omok.board[x][y] == 2
and omok.board[x - 1][y + 1] == 2
and omok.board[x - 2][y + 2] == 0
and omok.board[x - 3][y + 3] == 2
and omok.turn == 1):
omok.board[x - 2][y + 2] = 1
omok.check_board_black(x - 2, y + 2)
human = Human(x - 2, y + 2)
game.start_play(human)
# -> 4 defense
for x in range(0, 4):
for y in range(0, 8):
if (omok.board[x][y] == 2 and omok.board[x + 1][y] == 2
and omok.board[x + 2][y] == 2
and omok.board[x + 3][y] == 2 and omok.turn == 1):
if (omok.board[x + 4][y] == 0):
omok.board[x + 4][y] = 1
omok.check_board_black(x + 4, y)
human = Human(x + 4, y)
game.start_play(human)
elif (x > 0 and omok.board[x - 1][y] == 0):
omok.board[x - 1][y] = 1
omok.check_board_black(x - 1, y)
human = Human(x - 1, y)
game.start_play(human)
elif (omok.board[x][y] == 2 and omok.board[x + 1][y] == 0
and omok.board[x + 2][y] == 2
and omok.board[x + 3][y] == 2 and omok.turn == 1):
omok.board[x + 1][y] = 1
omok.check_board_black(x + 1, y)
human = Human(x + 1, y)
game.start_play(human)
elif (omok.board[x][y] == 2 and omok.board[x + 1][y] == 2
and omok.board[x + 2][y] == 0
and omok.board[x + 3][y] == 2 and omok.turn == 1):
omok.board[x + 2][y] = 1
omok.check_board_black(x + 2, y)
human = Human(x + 2, y)
game.start_play(human)
# to under 4 defense
for x in range(0, 8):
for y in range(0, 4):
if (omok.board[x][y] == 2 and omok.board[x][y + 1] == 2
and omok.board[x][y + 2] == 2
and omok.board[x][y + 3] == 2 and omok.turn == 1):
if (omok.board[x][y + 4] == 0):
omok.board[x][y + 4] = 1
omok.check_board_black(x, y + 4)
human = Human(x, y + 4)
game.start_play(human)
elif (y > 0 and omok.board[x][y - 1] == 0):
omok.board[x][y - 1] = 1
omok.check_board_black(x, y - 1)
human = Human(x, y - 1)
game.start_play(human)
elif (omok.board[x][y] == 2 and omok.board[x][y + 1] == 0
and omok.board[x][y + 2] == 2
and omok.board[x][y + 3] == 2 and omok.turn == 1):
omok.board[x][y + 1] = 1
omok.check_board_black(x, y + 1)
human = Human(x, y + 1)
game.start_play(human)
elif (omok.board[x][y] == 2 and omok.board[x][y + 1] == 2
and omok.board[x][y + 2] == 0
and omok.board[x][y + 3] == 2 and omok.turn == 1):
omok.board[x][y + 2] = 1
omok.check_board_black(x, y + 2)
human = Human(x, y + 2)
game.start_play(human)
# condition1 = fair position
# place the black stone near the place where the black stone gathers
# 4 attack code
# right under 4 attack
for x in range(0, 4): # 12
for y in range(0, 4): # 12
if (omok.board[x][y] == 1 and omok.board[x + 1][y + 1] == 1
and omok.board[x + 2][y + 2] == 1
and omok.board[x + 3][y + 3] == 1
and omok.turn == 1):
if (omok.board[x + 4][y + 4] == 0):
omok.board[x + 4][y + 4] = 1
omok.check_board_black(x + 4, y + 4)
human = Human(x + 4, y + 4)
game.start_play(human)
elif (x > 0 and y > 0
and omok.board[x - 1][y - 1] == 0):
omok.board[x - 1][y - 1] = 1
omok.check_board_black(x - 1, y - 1)
human = Human(x - 1, y - 1)
game.start_play(human)
elif (omok.board[x][y] == 1
and omok.board[x + 1][y + 1] == 0
and omok.board[x + 2][y + 2] == 1
and omok.board[x + 3][y + 3] == 1
and omok.turn == 1):
omok.board[x + 1][y + 1] = 1
omok.check_board_black(x + 1, y + 1)
human = Human(x + 1, y + 1)
game.start_play(human)
elif (omok.board[x][y] == 1
and omok.board[x + 1][y + 1] == 1
and omok.board[x + 2][y + 2] == 0
and omok.board[x + 3][y + 3] == 1
and omok.turn == 1):
omok.board[x + 2][y + 2] = 1
omok.check_board_black(x + 2, y + 2)
human = Human(x + 2, y + 2)
game.start_play(human)
# left under 4 attack
for x in range(4, 7): # 3 ~ 14 # 3,15
for y in range(0, 4):
if (omok.board[x][y] == 1 and omok.board[x - 1][y + 1] == 1
and omok.board[x - 2][y + 2] == 1
and omok.board[x - 3][y + 3] == 1
and omok.turn == 1):
if (x < 7 and y > 0 and omok.board[x + 1][y - 1] == 0):
omok.board[x + 1][y - 1] = 1
omok.check_board_black(x + 1, y - 1)
human = Human(x + 1, y - 1)
game.start_play(human)
elif (omok.board[x - 4][y + 4] == 0):
omok.board[x - 4][y + 4] = 1
omok.check_board_black(x - 4, y + 4)
human = Human(x - 4, y + 4)
game.start_play(human)
elif (omok.board[x][y] == 1
and omok.board[x - 1][y + 1] == 0
and omok.board[x - 2][y + 2] == 1
and omok.board[x - 3][y + 3] == 1
and omok.turn == 1):
omok.board[x - 1][y + 1] = 1
omok.check_board_black(x - 1, y + 1)
human = Human(x - 1, y + 1)
game.start_play(human)
elif (omok.board[x][y] == 1
and omok.board[x - 1][y + 1] == 1
and omok.board[x - 2][y + 2] == 0
and omok.board[x - 3][y + 3] == 1
and omok.turn == 1):
omok.board[x - 2][y + 2] = 1
omok.check_board_black(x - 2, y + 2)
human = Human(x - 2, y + 2)
game.start_play(human)
# -> 4 attack
for x in range(0, 4): # 12
for y in range(0, 8): # 12
if (omok.board[x][y] == 1 and omok.board[x + 1][y] == 1
and omok.board[x + 2][y] == 1
and omok.board[x + 3][y] == 1 and omok.turn == 1):
if (omok.board[x + 4][y] == 0):
omok.board[x + 4][y] = 1
omok.check_board_black(x + 4, y)
human = Human(x + 4, y)
game.start_play(human)
elif (x > 0 and omok.board[x - 1][y] == 0):
omok.board[x - 1][y] = 1
omok.check_board_black(x - 1, y)
human = Human(x - 1, y)
game.start_play(human)
elif (omok.board[x][y] == 1 and omok.board[x + 1][y] == 0
and omok.board[x + 2][y] == 1
and omok.board[x + 3][y] == 1 and omok.turn == 1):
omok.board[x + 1][y] = 1
omok.check_board_black(x + 1, y)
human = Human(x + 1, y)
game.start_play(human)
elif (omok.board[x][y] == 1 and omok.board[x + 1][y] == 1
and omok.board[x + 2][y] == 0
and omok.board[x + 3][y] == 1 and omok.turn == 1):
omok.board[x + 2][y] = 1
omok.check_board_black(x + 2, y)
human = Human(x + 2, y)
game.start_play(human)
# to under 4 attack
for x in range(0, 8): # 15
for y in range(0, 4): # 12
if (omok.board[x][y] == 1 and omok.board[x][y + 1] == 1
and omok.board[x][y + 2] == 1
and omok.board[x][y + 3] == 1 and omok.turn == 1):
if (omok.board[x][y + 4] == 0):
omok.board[x][y + 4] = 1
omok.check_board_black(x, y + 4)
human = Human(x, y + 4)
game.start_play(human)
elif (y > 0 and omok.board[x][y - 1] == 0):
omok.board[x][y - 1] = 1
omok.check_board_black(x, y - 1)
human = Human(x, y - 1)
game.start_play(human)
elif (omok.board[x][y] == 1 and omok.board[x][y + 1] == 0
and omok.board[x][y + 2] == 1
and omok.board[x][y + 3] == 1 and omok.turn == 1):
omok.board[x][y + 1] = 1
omok.check_board_black(x, y + 1)
human = Human(x, y + 1)
game.start_play(human)
elif (omok.board[x][y] == 1 and omok.board[x][y + 1] == 1
and omok.board[x][y + 2] == 0
and omok.board[x][y + 3] == 1 and omok.turn == 1):
omok.board[x][y + 2] = 1
omok.check_board_black(x, y + 2)
human = Human(x, y + 2)
game.start_play(human)
# 3 defense
# right under 3 defense
for x in range(0, 5): # 0~12 +3 max =15
for y in range(0, 5):
if (omok.board[x][y] == 2 and omok.board[x + 1][y + 1] == 2
and omok.board[x + 2][y + 2] == 2
and omok.turn == 1):
if (omok.board[x + 3][y + 3] == 0):
omok.board[x + 3][y + 3] = 1
omok.check_board_black(x + 3, y + 3)
human = Human(x + 3, y + 3)
game.start_play(human)
elif (y > 0 and x > 0
and omok.board[x - 1][y - 1] == 0):
omok.board[x - 1][y - 1] = 1
omok.check_board_black(x - 1, y - 1)
human = Human(x - 1, y - 1)
game.start_play(human)
# left under 3 defense
for x in range(3, 7):
for y in range(0, 5):
if (omok.board[x][y] == 2 and omok.board[x - 1][y + 1] == 2
and omok.board[x - 2][y + 2] == 2
and omok.turn == 1):
if (y > 0 and omok.board[x + 1][y - 1] == 0):
omok.board[x + 1][y - 1] = 1
omok.check_board_black(x + 1, y - 1)
human = Human(x + 1, y - 1)
game.start_play(human)
elif (omok.board[x - 3][y + 3] == 0):
omok.board[x - 3][y + 3] = 1
omok.check_board_black(x - 3, y + 3)
human = Human(x - 3, y + 3)
game.start_play(human)
# -> defense
# > 3 defense
for x in range(0, 5):
for y in range(0, 8):
if (omok.board[x][y] == 2 and omok.board[x + 1][y] == 2
and omok.board[x + 2][y] == 2 and omok.turn == 1):
if (omok.board[x + 3][y] == 0):
omok.board[x + 3][y] = 1
omok.check_board_black(x + 3, y)
human = Human(x + 3, y)
game.start_play(human)
elif (x > 0 and omok.board[x - 1][y] == 0):
omok.board[x - 1][y] = 1
omok.check_board_black(x - 1, y)
human = Human(x - 1, y)
game.start_play(human)
# to under defense
# under 3 defense
for x in range(0, 8):
for y in range(0, 5): # 0~12 for i in range(0,14)
if (omok.board[x][y] == 2 and omok.board[x][y + 1] == 2
and omok.board[x][y + 2] == 2 and omok.turn == 1):
if (omok.board[x][y + 3] == 0):
omok.board[x][y + 3] = 1
omok.check_board_black(x, y + 3)
human = Human(x, y + 3)
game.start_play(human)
elif (y > 0 and omok.board[x][y - 1] == 0):
omok.board[x][y - 1] = 1
omok.check_board_black(x, y - 1)
human = Human(x, y - 1)
game.start_play(human)
# 3 attack
# right under 3 attack
for x in range(0, 5):
for y in range(0, 5): # 13
if (omok.board[x][y] == 1 and omok.board[x + 1][y + 1] == 1
and omok.board[x + 2][y + 2] == 1
and omok.turn == 1):
if (omok.board[x + 3][y + 3] == 0):
omok.board[x + 3][y + 3] = 1
omok.check_board_black(x + 3, y + 3)
human = Human(x + 3, y + 3)
game.start_play(human)
elif (y > 0 and x > 0
and omok.board[x - 1][y - 1] == 0):
omok.board[x - 1][y - 1] = 1
omok.check_board_black(x - 1, y - 1)
human = Human(x - 1, y - 1)
game.start_play(human)
# left under attack
for x in range(3, 7): # 2 ~ 14 # 2,15
for y in range(0, 5): # 13
if (omok.board[x][y] == 1 and omok.board[x - 1][y + 1] == 1
and omok.board[x - 2][y + 2] == 1
and omok.turn == 1):
if (x < 7 and y > 0 and omok.board[x + 1][y - 1] == 0):
omok.board[x + 1][y - 1] = 1
omok.check_board_black(x + 1, y - 1)
human = Human(x + 1, y - 1)
game.start_play(human)
elif (omok.board[x - 3][y + 3] == 0):
omok.board[x - 3][y + 3] = 1
omok.check_board_black(x - 3, y + 3)
human = Human(x - 3, y + 3)
game.start_play(human)
# -> attack
# > 3 attack
for x in range(0, 5): # 4
for y in range(0, 8): # 7
if (omok.board[x][y] == 1 and omok.board[x + 1][y] == 1
and omok.board[x + 2][y] == 1 and omok.turn == 1):
if (omok.board[x + 3][y] == 0):
omok.board[x + 3][y] = 1
omok.check_board_black(x + 3, y)
human = Human(x + 3, y)
game.start_play(human)
elif (x > 0 and omok.board[x - 1][y] == 0):
omok.board[x - 1][y] = 1
omok.check_board_black(x - 1, y)
human = Human(x - 1, y)
game.start_play(human)
# to under attack
for x in range(0, 8): # 15
for y in range(0, 5): # 13
if (omok.board[x][y] == 1 and omok.board[x][y + 1] == 1
and omok.board[x][y + 2] == 1 and omok.turn == 1):
if (omok.board[x][y + 3] == 0):
omok.board[x][y + 3] = 1
omok.check_board_black(x, y + 3)
human = Human(x, y + 3)
game.start_play(human)
elif (y > 0 and omok.board[x][y - 1] == 0):
omok.board[x][y - 1] = 1
omok.check_board_black(x, y - 1)
human = Human(x, y - 1)
game.start_play(human)
# left under attack
for x in range(3, 7): # 2 ~ 14 # 2,15
for y in range(0, 5): # 13
if (omok.board[x][y] == 1 and omok.board[x - 1][y + 1] == 1
and omok.board[x - 2][y + 2] == 1
and omok.turn == 1):
if (x < 7 and y > 0 and omok.board[x + 1][y - 1] == 0):
omok.board[x + 1][y - 1] = 1
omok.check_board_black(x + 1, y - 1)
human = Human(x + 1, y - 1)
game.start_play(human)
elif (omok.board[x - 3][y + 3] == 0):
omok.board[x - 3][y + 3] = 1
omok.check_board_black(x - 3, y + 3)
human = Human(x - 3, y + 3)
game.start_play(human)
# defense code
# empty triangle
"""for x in range(2, 4):
for y in range(3, 5):
if (omok.board[x][y] == 1 and omok.board[x + 1][y] == 1 and omok.board[x][
y - 1] == 0 and omok.turn == 1):
if (y > 0 and omok.board[x][y - 1] == 0):
omok.board[x][y - 1] = 1
omok.check_board_black(x, y - 1)
human = Human(x, y - 1)
game.start_play(human)"""
if (omok.turn != 2 and omok.id != 0):
movelist = list()
countlist = list()
for a in range(1, 7): # 1 ~ 6
for b in range(1, 7): # 1 ~ 6
count = 0
if (omok.board[a][b] != 0): # a b =!0
pass
else:
if (omok.board[a - 1][b - 1] == 1):
count = count + 1
if (omok.board[a][b - 1] == 1):
count = count + 1
if (omok.board[a + 1][b - 1] == 1):
count = count + 1
if (omok.board[a - 1][b] == 1):
count = count + 1
if (omok.board[a + 1][b] == 1):
count = count + 1
if (omok.board[a - 1][b + 1] == 1):
count = count + 1
if (omok.board[a][b + 1] == 1):
count = count + 1
if (omok.board[a + 1][b + 1] == 1):
count = count + 1
xymove = str(a) + ',' + str(
b) # (a,b) list add (1,1) , (1,2)
movelist.append(
xymove) # xy's move list add(1,1 ~ 13,13)
countlist.append(
count
) # 모든 좌표마다의 가중치 값을 카운트 리스트에 넣음 [0 , 0 , 0 , ...]
move = countlist.index(max(countlist)) # 가장 큰 가중치가 있는 좌표 반환
splitposition = movelist[move].split(
','
) # 받은 값을 x , y로 변환 = splitposition[0],spolitposition[1]
if (move == 0): # 배열에 아무것도 없다면 혹은 가중치가 다 같다면 ?
indexx = 0 # x,y를 0 초기화
indexy = 0
for c in range(2, 5):
for d in range(2, 5):
if (omok.board[c][d] == 2): # block stone point
indexx = c
indexy = d
movepm = [1, 2, -1, -2, 0]
var1 = movepm[random.randint(0, 4)]
if (var1 == 0):
var2 = movepm[random.randint(0, 3)]
else:
var2 = movepm[random.randint(
0, 4)] # var1이 0 아니라면 -1 1 0중에 하나 고
indexx = indexx + var1
indexy = indexy + var2
splitposition[0] = str(indexx)
splitposition[1] = str(indexy)
positionx = int(splitposition[0])
positiony = int(splitposition[1])
try:
if (positionx > 0 and positiony > 0
and omok.board[positionx][positiony] == 0):
omok.board[positionx][positiony] = 1
omok.check_board_black(positionx, positiony)
human = Human(positionx, positiony)
game.start_play(human)
except:
pass
elif (omok.turn == 2):
try:
policy_param = pickle.load(open(model_file, 'rb'))
except:
policy_param = pickle.load(open(model_file, 'rb'),
encoding='bytes')
best_policy = PolicyValueNetNumpy(8, 8, policy_param)
print('current model is', model_file)
mcts = MCTSPlayer(best_policy.policy_value_fn,
c_puct=7,
n_playout=2000)
move = game.start_play1(mcts)
x = move // board_size
y = move % board_size
omok.board[x][y] = 2
omok.check_board_white(x, y)
if omok.is_gameover: ##return true
return
pygame.display.update()
fps_clock.tick(fps)
result.init_board(start_player=0)
if input1 != ' ':
for i in range(len(input1)):
result.states[input1[i]] = 1
result.availables.remove(input1[i])
if input2 != ' ':
for j in range(len(input2)):
result.states[input2[j]] = 2
result.availables.remove(input2[j])
result.current_player = 1
return result
parsed_input1, parsed_input2, ai = parse(input_from_app)
width = height = length_list[ai]
board = makemap(parsed_input1, parsed_input2)
model_file = model_file_list[ai]
try:
policy_param = pickle.load(open(model_file, 'rb'))
except:
policy_param = pickle.load(open(model_file, 'rb'),
encoding='bytes') # To support python3
best_policy = PolicyValueNetNumpy(width, height, policy_param)
mcts_player = MCTSPlayer(
best_policy.policy_value_fn, c_puct=5,
n_playout=400) # set larger n_playout for better performance
print(mcts_player.get_action(board))
sys.stdout.flush()