麻将AI代码

下面是一个使用 TensorFlow 和 Keras 实现的麻将AI代码示例，主要使用了深度强化学习算法：

import numpy as np
import tensorflow as tf
from tensorflow import keras

class MahjongAI:
    def __init__(self):
        self.model = self.build_model()
        self.target_model = self.build_model()
        self.target_model.set_weights(self.model.get_weights())
        self.replay_buffer = []
        self.batch_size = 32
        self.gamma = 0.99
        self.epsilon = 1.0
        self.epsilon_min = 0.01
        self.epsilon_decay = 0.995
        self.update_freq = 1000
        self.steps_since_update = 0

    def build_model(self):
        model = keras.Sequential()
        model.add(keras.layers.Dense(128, input_dim=34, activation='relu'))
        model.add(keras.layers.Dense(128, activation='relu'))
        model.add(keras.layers.Dense(34, activation='linear'))
        model.compile(loss='mse', optimizer=keras.optimizers.Adam())
        return model

    def select_action(self, state):
        if np.random.rand() < self.epsilon:
            return np.random.randint(0, 34)
        q_values = self.model.predict(state.reshape(1, -1))[0]
        return np.argmax(q_values)

    def update_replay_buffer(self, state, action, reward, next_state, done):
        self.replay_buffer.append((state, action, reward, next_state, done))
        if len(self.replay_buffer) > 100000:
            self.replay_buffer.pop(0)

    def update_model(self):
        if len(self.replay_buffer) < self.batch_size:
            return
        batch = np.random.choice(self.replay_buffer, self.batch_size)
        states = np.array([transition[0] for transition in batch])
        actions = np.array([transition[1] for transition in batch])
        rewards = np.array([transition[2] for transition in batch])
        next_states = np.array([transition[3] for transition in batch])
        dones = np.array([transition[4] for transition in batch])
        q_values = self.model.predict(states)
        next_q_values = self.target_model.predict(next_states)
        for i in range(self.batch_size):
            if dones[i]:
                q_values[i][actions[i]] = rewards[i]
            else:
                q_values[i][actions[i]] = rewards[i] + self.gamma * np.max(next_q_values[i])
        self.model.fit(states, q_values, verbose=0)
        self.steps_since_update += 1
        if self.steps_since_update % self.update_freq == 0:
            self.target_model.set_weights(self.model.get_weights())

    def train(self, env, episodes=100):
        for episode in range(episodes):
            state = env.reset()
            done = False
            total_reward = 0
            while not done:
                action = self.select_action(state)
                next_state, reward, done, _ = env.step(action)
                self.update_replay_buffer(state, action, reward, next_state, done)
                self.update_model()
                state = next_state
                total_reward += reward
            print('Episode', episode+1, 'Total Reward:', total_reward)
            if self.epsilon > self.epsilon_min:
                self.epsilon *= self.epsilon_decay

    def play(self, env):
        state = env.reset()
        done = False
        while not done:
            action = self.select_action(state)
            print('Action:', action)
            state, _, done, _ = env.step(action)

if __name__ == '__main__':
    ai = MahjongAI()
    # TODO: 实现麻将游戏环境，并传入 train 或 play 方法中进行训练或游戏

这个示例代码使用了深度强化学习算法，建立了一个神经网络模型来预测每个操作的质量分数，使用经验回放和目标网络来提高训练效率。在实际使用时，需要根据实际情况对代码进行修改和调整。