Я практикую свои алгоритмы и хотел реализовать искусственный интеллект на основе минимаксного алгоритма в Python (код ниже):
import itertools
import more_itertools
import numpy as np
class Position:
win_positions = np.array(([1, 2, 3], [4, 5, 6], [7, 8, 9], [1, 5, 9],
[1, 4, 7], [2, 5, 8], [3, 6, 9], [3, 5, 7]))
def __init__(self, position=None):
"""If a position is given ensure 0 is in the first index,
for a dictionary lookup later for
an empty board otherwise use the position
"""
self.position = np.array([0]) if position is None else \
np.array(position) if position[0] == 0 else np.append([0], np.array(
position))
self.turn = itertools.cycle(
['x', 'o']) if self.position.size % 2 else itertools.cycle(
['o', 'x'])
self.p = more_itertools.peekable(self.turn)
def add(self, value):
next(self.turn)
self.position = np.append(self.position, value)
return self.win()
def win(self):
x = np.array(self.position[1::2])
o = np.array(self.position[2::2])
"""
takes the difference of a players moves and the winning move sets,
an array resulting from this with a size of 0 indicates the player
contains
a winning arrangement in their moves
"""
if any([False if np.setdiff1d(position, x).size else True for position
in Position.win_positions]):
return 10
elif any([False if np.setdiff1d(position, o).size else True for position
in Position.win_positions]):
return -10
elif self.valid_moves().size == 0:
return 0
else:
return None
def whose_turn(self, player=None):
return self.p.peek() if player is None else self.p.peek() == player
def valid_moves(self):
return np.setdiff1d(np.arange(1, 10), self.position)
def clone(self, next_pos=None):
"""returns a new position object with one of two options, either a copy,
of the current position or a copy with a move added"""
return Position(
np.append(self.position, next_pos)) if next_pos else Position(
np.copy(self.position))
def mini_max(self, depth=0):
best_move = -1
"""return the win value of the board position
draws are 0, 'o' wins are -10 and 'x' wins are
10. A board with valid moves available returns none for
this call """
if self.win() is not None:
return self.win(), None
minimax = float('-inf') if self.whose_turn('x') else float('inf')
for move in self.valid_moves():
val = self.clone(move).mini_max()[0]
if minimax < val and self.whose_turn(
'x') or minimax > val and self.whose_turn('o'):
minimax = val
best_move = move
return minimax + (-1 if self.whose_turn('x') else 1), best_move
def print_board(self):
blank = np.chararray(shape=(9,), unicode=True)
for i in range(1, self.position.size):
blank[self.position[i] - 1] = 'X' if i % 2 else 'O'
string = ("{:^3s}█{:^3s}█{:^3s}\n"
"▄▄▄█▄▄▄█▄▄▄\n"
"{:^3s}█{:^3s}█{:^3s}\n"
"▀▀▀█▀▀▀█▀▀▀\n"
"{:^3s}█{:^3s}█{:^3s}\n".format(*blank))
print(string)
def __str__(self):
return "".join(map(str, self.position))
if __name__ == '__main__':
# test winning conditions
assert Position([1, 4, 2, 5, 3]).win() == 10
assert Position([1, 4, 2, 5, 7, 6]).win() == -10
assert Position([1, 4, 2, 5, 7, 8, 6, 5, 9, 3]).win() == 0
# testing turns
assert Position([1, 2, 3]).whose_turn() == 'o'
assert Position([1, 5, 6, 4]).whose_turn() == 'x'
assert Position([5, 6, 3, 2]).whose_turn('x')
assert Position([5, 6, 3, 2, 1]).whose_turn('o')
# valid moves
assert all(
Position([1, 2, 3, 4, 5, 6]).valid_moves() == np.array([7, 8, 9]))
assert all(
Position([9, 5, 3, 8]).valid_moves() == np.array([1, 2, 4, 6, 7]))
# test minimax correct moves
assert Position([1, 4, 2, 5]).mini_max()[1] == 3
assert Position([1, 3, 4, 5]).mini_max()[1] == 7
assert Position([1, 2, 5]).mini_max()[1] == 9 # error here
Я пытался переписать свой код несколько раз, номои тесты продолжают проваливаться на оптимальных ходах.О игрок, кажется, игнорирует выигрышную позицию х игрока.Я в растерянности.Я проверил это по псевдокоду в википедии, но, похоже, что-то явно не так