DQN算法实现九宫格人机对弈代码

下面是使用Deep Q-Network (DQN)算法实现九宫格人机对弈的代码示例：

import numpy as np
import random
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam

class DQNAgent:
    def __init__(self, state_size, action_size):
        self.state_size = state_size
        self.action_size = action_size
        self.memory = []
        self.gamma = 0.95
        self.epsilon = 1.0
        self.epsilon_decay = 0.995
        self.epsilon_min = 0.01
        self.learning_rate = 0.001
        self.model = self.build_model()

    def build_model(self):
        model = Sequential()
        model.add(Dense(24, input_dim=self.state_size, activation='relu'))
        model.add(Dense(24, activation='relu'))
        model.add(Dense(self.action_size, activation='linear'))
        model.compile(loss='mse', optimizer=Adam(lr=self.learning_rate))
        return model

    def remember(self, state, action, reward, next_state, done):
        self.memory.append((state, action, reward, next_state, done))

    def action(self, state):
        if np.random.rand() <= self.epsilon:
            return random.randrange(self.action_size)
        q_values = self.model.predict(state)
        return np.argmax(q_values[0])

    def train(self, batch_size):
        if len(self.memory) < batch_size:
            return
        minibatch = random.sample(self.memory, batch_size)
        for state, action, reward, next_state, done in minibatch:
            target = reward
            if not done:
                target = reward + self.gamma * np.amax(self.model.predict(next_state)[0])
            target_f = self.model.predict(state)
            target_f[0][action] = target
            self.model.fit(state, target_f, epochs=1, verbose=0)
        if self.epsilon > self.epsilon_min:
            self.epsilon *= self.epsilon_decay

def play_game():
    agent = DQNAgent(9, 9)
    state = np.zeros(9)
    done = False
    batch_size = 32
    while not done:
        action = agent.action(state.reshape(1, 9))
        next_state, reward, done = make_move(state, action)
        agent.remember(state.reshape(1, 9), action, reward, next_state.reshape(1, 9), done)
        state = next_state
        agent.train(batch_size)

def make_move(state, action):
    next_state = np.copy(state)
    if next_state[action] == 0:
        next_state[action] = 1
        if check_win(next_state, 1):
            return next_state, 1, True
        elif np.count_nonzero(next_state == 0) == 0:
            return next_state, 0, True
        else:
            return next_state, 0, False
    else:
        return next_state, -1, False

def check_win(state, player):
    rows = np.split(state, 3)
    cols = np.split(state, [3, 6])
    diagonals = [np.array([state[0], state[4], state[8]]), np.array([state[2], state[4], state[6]])]
    if np.any(np.all(rows == player, axis=1)) or np.any(np.all(cols == player, axis=1)) or np.any(np.all(diagonals == player, axis=1)):
        return True
    else:
        return False

play_game()

在上面的代码中，我们使用了Keras来定义DQN模型。模型有两个隐藏层，每个隐藏层有24个神经元，输出层有9个神经元，分别对应于九宫格中的每个位置。在训练过程中，我们使用了经验回放(memory replay)技术来训练模型。每个经验由当前状态、动作、奖励、下一个状态和是否结束组成。我们在每个时间步上执行以下操作：

1. 根据当前状态选择一个动作。
2. 执行动作并观察下一个状态和奖励。
3. 将经验添加到经验回放池中。
4. 从经验回放池中随机抽取一批经验作为训练集，使用Q-learning更新模型参数。
5. 降低探索率，使模型逐渐趋向于选择更优的动作。

在make_move函数中，我们使用了简单的规则来确定机器人的下一步。如果机器人能够获胜，则执行该步骤；否则，如果还有空格，则随机选择一个空格；否则，游戏结束。

最后，在check_win函数中，我们检查九宫格状态是否包含任意一行、一列或一条对角线全都是1或-1，如果是，则该玩家获胜。

你可以根据需要修改代码以进行更好的训练和游戏体验。