def get_input(self, go: Game, piece_type):
self.load_dict()
# print(board.n_move)
# go.visualize_board()
if go.count_player_stones(piece_type) <= 0:
self.side = piece_type
self.opponent = 1 if self.side == 2 else 2
self.cache = {}
open("cache.txt", "w").close()
if go.is_position_valid(2, 2, self.side, True):
copy_board = copy.deepcopy(go)
copy_board.next_board(2, 2, self.side, True)
# print("Minimax: piece_type = {}".format(self.side), \
# "current board value = {}".format(self.total_score(copy_board, self.side)))
return 2, 2
if go.is_game_finished():
return
else:
# score, action = self._max(board)
depth = DEPTH
if go.n_move > 18:
depth = 24 - go.n_move
elif go.n_move < 8:
# return aggressive_action(go, piece_type)
depth = 1
action = self.alpha_beta_cutoff_search(go, depth)
copy_board = copy.deepcopy(go)
if action != "PASS":
# print(action)
copy_board.next_board(action[0], action[1], self.side, True)
# print("Minimax: piece_type = {}".format(self.side), \
# "current board value = {}".format(self.total_score(copy_board, self.side)))
self.save_dict()
return action # board.move(action[0], action[1], self.side)
def get_input(self, board: Game, piece_type):
if board.count_player_stones(piece_type) <= 0:
self.side = piece_type
self.opponent = 1 if self.side == 2 else 2
if board.is_position_valid(2, 2, self.side, True):
copy_board = copy.deepcopy(board)
copy_board.next_board(2, 2, self.side, True)
# print("Minimax_old: piece_type = {}".format(self.side), \
# "current board value = {}".format(self.total_score(copy_board, self.side)))
return 2, 2
if board.is_game_finished():
return
else:
# score, action = self._max(board)
action = self.alpha_beta_cutoff_search(board, DEPTH)
if action != "PASS":
copy_board = copy.deepcopy(board)
copy_board.next_board(action[0], action[1], self.side, True)
# print("Minimax_old: piece_type = {}".format(self.side), \
# "current board value = {}".format(self.total_score(copy_board, self.side)))
return action # board.move(action[0], action[1], self.side)
def alpha_beta_cutoff_search(self, go: Game, depth=4):
def max_value(board, alpha, beta, depth):
state = board.state_string()
if depth == 0 or board.is_game_finished():
if state in self.cache:
return self.cache[state]
return self.total_score(board, self.side)
v_max = -numpy.inf
candidates = []
for i in range(board.size):
for j in range(board.size):
if board.is_position_valid(i,
j,
self.side,
test_check=True):
candidates.append((i, j))
random.shuffle(candidates)
if not candidates:
action = "PASS"
v_max = max(v_max, min_value(board, alpha, beta, depth - 1))
if v_max <= alpha:
return v_max
alpha = max(alpha, v_max)
else:
for i, j in candidates:
copyBoard = copy.deepcopy(board)
copyBoard.next_board(i, j, self.side, False)
copyBoard.n_move += 1
v_max = max(v_max,
min_value(copyBoard, alpha, beta, depth - 1))
if v_max is not None:
self.cache[state] = v_max
if v_max >= beta:
return v_max
alpha = max(alpha, v_max)
return v_max
def min_value(board, alpha, beta, depth):
state = board.state_string()
if depth == 0 or board.is_game_finished():
if state in self.cache:
return self.cache[state]
return self.total_score(board, self.side)
v_min = numpy.inf
candidates = []
for i in range(board.size):
for j in range(board.size):
if board.is_position_valid(i,
j,
self.opponent,
test_check=True):
candidates.append((i, j))
random.shuffle(candidates)
if not candidates:
action = "PASS"
v_min = min(v_min, max_value(board, alpha, beta, depth - 1))
if v_min <= alpha:
return v_min
beta = min(beta, v_min)
else:
for i, j in candidates:
copyBoard = copy.deepcopy(board)
valid = copyBoard.next_board(i, j, self.opponent, True)
copyBoard.n_move += 1
if not valid:
raise ValueError("in min invalid move")
v_min = min(v_min,
max_value(copyBoard, alpha, beta, depth - 1))
if v_min is not None:
self.cache[state] = v_min
if v_min <= alpha:
return v_min
beta = min(beta, v_min)
return v_min
best_score = -numpy.inf
beta = numpy.inf
best_action = None
candidates = []
for i in range(go.size):
for j in range(go.size):
if go.is_position_valid(i, j, self.side, test_check=True):
candidates.append((i, j))
random.shuffle(candidates)
if not candidates:
best_action = "PASS"
else:
for i, j in candidates:
copyBoard = copy.deepcopy(go)
copyBoard.next_board(i, j, self.side, True)
copyBoard.n_move += 1
if go.n_move < 8:
value = max_value(copyBoard, best_score, beta, depth)
else:
value = min_value(copyBoard, best_score, beta, depth)
if value > best_score:
best_score = value
best_action = (i, j)
return best_action