def suggest_move(self, position):
if position.caps[0] + 50 < position.caps[1]:
return gtp.RESIGN
start = time.time()
# 获取当前这一步的特征概率，进行判断搜索
move_probs = self.policy_network.run(position)
# 创建当前这一步的根结点
for i in range(9):
for j in range(9):
x = go.is_eyeish(position.board, (i, j))
#print(x)
if position.board[i][j] != go.EMPTY:
move_probs[i][j] = float(0)
elif x != None and x != position.to_play:
move_probs[i][j] = float(0)
# print(move_probs)
root = MCTSNode.root_node(position, move_probs)
# print('进入蒙特卡洛搜索树')
while time.time() - start < self.seconds_per_move:
self.tree_search(root)
# print('蒙特卡洛搜索树结束')
# 如果自己拒绝了pass这一步，这个ai会开始填充自己的眼，所以要进行判断，如果是非法的行棋要找出来。
# 返回值为一个根节点的所有子节点中的最大的那一个
if position.n < 45:
# print(str(root.children[(4, 4)].Q))
# print(max(root.children.keys(), key=lambda move, root=root: root.children[move].Q))
return max(root.children.keys(), key=lambda move, root=root: root.children[move].N)
else:
while True:
max_move = max(root.children.keys(), key=lambda move, root=root: root.children[move].N)
# print(move_probs)
# root.children.pop(max_move)
x = max_move[0]
y = max_move[1]
if go.is_eyeish(position.board, max_move) != position.to_play and move_probs[x][y] != float(0):
return max(root.children.keys(), key=lambda move, root=root: root.children[move].N)
elif move_probs[x][y] == float(0):
position.pass_move(mutate=True)
return None
else:
# root.children[max_move] = float(0)
root.children.pop(max_move)
def play_valid_move(self, position, move_probs):
for move in sorted_moves(move_probs):
if go.is_eyeish(position.board, move):
move_probs[move[0]][move[1]] = float(0)
continue
try:
# 判断当前一个可行的行棋策略，是不是合法的，不合法去寻找下一个点，合法的就返回这个点
candidate_pos = position.play_move(move, mutate=True)
except go.IllegalMove:
continue
else:
return candidate_pos
# 没有找到适合的点的话，就返回pass
return position.pass_move(mutate=True)
def is_move_reasonable(position, move):
return position.is_move_legal(move) and go.is_eyeish(position.board, move) != position.to_play