Python 围棋 AI 代码示例：随机落子策略

下面是一个简单的围棋 AI 代码示例：\n\npython\nimport numpy as np\nimport random\n\n# 初始化棋盘\nboard_size = 9\nboard = np.zeros((board_size, board_size))\n\n# 定义棋手\nclass Player:\n def __init__(self, stone):\n self.stone = stone\n\n def get_move(self, board):\n # 随机选择一个合法的落子位置\n legal_moves = self.get_legal_moves(board)\n move = random.choice(legal_moves)\n return move\n\n def get_legal_moves(self, board):\n legal_moves = []\n for i in range(board_size):\n for j in range(board_size):\n if board[i][j] == 0:\n legal_moves.append((i, j))\n return legal_moves\n\n def make_move(self, board, move):\n board[move[0]][move[1]] = self.stone\n\n# 创建两个棋手\nplayer1 = Player(1)\nplayer2 = Player(2)\n\n# 开始对弈\ncurrent_player = player1\nwhile True:\n move = current_player.get_move(board)\n current_player.make_move(board, move)\n print("Player", current_player.stone, "makes move:", move)\n print(board)\n\n # 判断游戏是否结束\n if len(player1.get_legal_moves(board)) == 0 and len(player2.get_legal_moves(board)) == 0:\n break\n\n # 切换棋手\n if current_player == player1:\n current_player = player2\n else:\n current_player = player1\n\n\n这个示例中的围棋 AI 采用的是随机选择一个合法的落子位置的策略进行下棋。你可以根据需要改进 AI 的下棋策略，例如使用 Minimax 算法、蒙特卡洛树搜索等。