def genmove(self, board):
result, winner = board.check_game_end_gomoku(self)
assert not result
simulate_moves = board.get_empty_point()
simulate_moves_num = len(simulate_moves)
score = [0] * simulate_moves_num
for i in range(simulate_moves_num):
move = simulate_moves[i]
score[i] = self.mc_simulate(board, move)
best_move_index = score.index(max(score))
best_move = simulate_moves[best_move_index]
assert best_move in simulate_moves
return best_move
def solve_alphabeta(board: SimpleGoBoard, color: int, board_is_evaluated=False):
result, winner = board.check_game_end_gomoku()
if result:
if winner == color:
return 10*score.FIVE, None
else:
return -10*score.FIVE, None
# print('ok')
alpha, beta = -10*score.FIVE, 10*score.FIVE
if not board_is_evaluated:
evaluate_board(board)
moves = gen_possible_moves(board, color)
print(list(map(lambda t: (t[0], gtp_connection.format_point(
gtp_connection.point_to_coord(t[0], board.size)), t[1], t[2]), moves)))
if len(moves) == 0:
return 0, None
if (gtp_connection.total_steps == 0 or gtp_connection.total_steps == 1) and board.board[36] == EMPTY:
return 1, 36
# best_move = None
global best_move
best_move = PASS
for m in moves:
move = m[0]
# move_coord = gtp_connection.point_to_coord(move, board.size)
# move_as_string = gtp_connection.format_point(move_coord)
board.board[move] = color
update_board(board, move)
result = -alphabeta_search(board, move, GoBoardUtil.opponent(color), 40, -beta, -alpha)
# print("trying move:", move_as_string, "score:", result)
board.board[move] = EMPTY
update_board(board, move)
if result > alpha:
alpha = result
best_move = move
if result >= beta:
return beta, move
return alpha, best_move
