def play_later():
result = self.move_to(stone, to_)
if opp_q is not None:
opp_valid = rule.valid_action(self.board(), -player)
self.show_qtext(opp_q, opp_valid, hide=False)
if result == rule.ACCQUIRE:
# 对手走棋
self.switch_player_and_play()
elif result == rule.WIN:
logger.info('GAME OVER, WINNER IS %s', stone.player.name)
self.game_over(stone.player)
def probabilities(self, board, player):
x = self.feature_1st(board, player)
valid = rule.valid_action(board, player)
x = np.array([x])
p = self.model.predict(x)[0]
p = p.reshape(5, 5, 4)
vp = p * valid # 所有可能走法的概率
if vp.max() == 0:
vp = valid / valid.sum()
else:
vp = vp / vp.sum()
return vp
def feature_1st(board, player):
"""
第一视角的棋局特征
:param board: 棋盘
:param player: 当前的棋手
:return: 当前局面的特征(5x5xN)
"""
space = (board == 0).astype(np.int8).reshape((5, 5, 1))
self = (board == player).astype(np.int8).reshape((5, 5, 1))
opponent = (board == -player).astype(np.int8).reshape((5, 5, 1))
v_locations = rule.valid_location(board, player).reshape((5, 5, 1))
v_actions = rule.valid_action(board, player)
bias = np.ones((5, 5, 1))
return np.concatenate(
(space, self, opponent, v_locations, v_actions, bias), axis=2)
def feature(board, player):
"""
棋局的特征
:param board: 棋盘
:param player: 当前的棋手
:return: 当前局面的特征(5x5x10)
"""
space = (board == 0).astype(np.int8).reshape((5, 5, 1))
black = (board == 1).astype(np.int8).reshape((5, 5, 1))
white = (board == -1).astype(np.int8).reshape((5, 5, 1))
who = np.ones((5, 5, 1)) if player == 1 else np.zeros((5, 5, 1))
v_locations = rule.valid_location(board, player).reshape((5, 5, 1))
v_actions = rule.valid_action(board, player)
bias = np.ones((5, 5, 1))
return np.concatenate(
(space, black, white, who, v_locations, v_actions, bias), axis=2)
def simulate(nw0, nw1, init='fixed'):
board = rule.init_board() if init == 'fixed' else rule.random_init_board()
player = 1
records = Record()
while True:
nw = nw0 if player == 1 else nw1
try:
bd = board.copy()
from_, action, vp, p = nw.policy(board, player)
# print('>', from_, action)
assert board[from_] == player
to_ = tuple(np.add(from_, rule.actions_move[action]))
command, eat = rule.move(board, from_, to_)
reward = len(eat)
records.add(bd, from_, action, reward, vp, win=command == rule.WIN)
except NoActionException:
return Record(), 0
except Exception as e:
logging.info('board is:')
logging.info(board)
logging.info('player is: %s', player)
valid = rule.valid_action(board, player)
logging.info('predict is:')
print(nw.p)
logging.info('sum is: %s', nw.p.sum())
logging.info('valid action is:')
logging.info(nw.valid)
logging.info('p * valid is:')
logging.info(nw.vp)
logging.info('from:%s, action:%s', from_, action)
logging.info('prob is: %s', valid[from_][action])
records.save('records/train/1st_')
raise e
# if eat:
# print(player, from_, to_, eat, N)
if command == rule.WIN:
logging.info('%s WIN, step use: %s', str(player), records.length())
return records, player
if records.length() > 10000:
logging.info('走子数过多: %s', records.length())
return Record(), 0
player = -player
board = rule.flip_board(board)
def _play(self, player, from_, to_, p, opp_q=None):
logger.info('from:%s, to_:%s', from_, to_)
logger.debug('p:\n%s', p)
board = self.board()
valid_action = rule.valid_action(board, player)
logger.debug('valid_action:\n%s', valid_action)
self.show_qtext(p, valid_action)
self.show_select(from_, to_)
stone = self.stone(from_)
def play_later():
result = self.move_to(stone, to_)
if opp_q is not None:
opp_valid = rule.valid_action(self.board(), -player)
self.show_qtext(opp_q, opp_valid, hide=False)
if result == rule.ACCQUIRE:
# 对手走棋
self.switch_player_and_play()
elif result == rule.WIN:
logger.info('GAME OVER, WINNER IS %s', stone.player.name)
self.game_over(stone.player)
self.play_timer = self.window.after(int(self.period * 1000),
play_later)
def policy_1st(self, board, player):
x = self.feature_1st(board, player)
valid = rule.valid_action(board, player)
self.set_dropout(0)
return self._policy(x, board, valid)
def policy(self, board, player):
x = self.feature_1st(board, player).flatten()
valid = rule.valid_action(board, player)
return self._policy(x, board, player, valid)
