tensorflow2写DQN代码

以下是使用TensorFlow 2实现DQN（Deep Q Network）的示例代码：

首先，我们需要导入所需的库：

import random
import numpy as np
import tensorflow as tf
from collections import deque

然后，我们定义DQN模型。这个模型由一个卷积神经网络（CNN）和一个全连接神经网络（Dense）组成。CNN用于提取图像特征，而Dense用于将这些特征映射到Q值。

class DQNAgent:
    def __init__(self, state_shape, action_size, learning_rate, gamma, epsilon, epsilon_decay):
        self.state_shape = state_shape # 输入状态的形状
        self.action_size = action_size # 动作空间大小
        self.learning_rate = learning_rate # 学习率
        self.gamma = gamma # 折扣因子
        self.epsilon = epsilon # 探索率
        self.epsilon_decay = epsilon_decay # 探索率衰减率
        self.memory = deque(maxlen=2000) # 经验回放缓存
        self.model = self.build_model() # 创建模型

    def build_model(self):
        model = tf.keras.Sequential()
        model.add(tf.keras.layers.Conv2D(32, (8, 8), strides=(4, 4), activation='relu', input_shape=self.state_shape))
        model.add(tf.keras.layers.Conv2D(64, (4, 4), strides=(2, 2), activation='relu'))
        model.add(tf.keras.layers.Conv2D(64, (3, 3), activation='relu'))
        model.add(tf.keras.layers.Flatten())
        model.add(tf.keras.layers.Dense(512, activation='relu'))
        model.add(tf.keras.layers.Dense(self.action_size))
        model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=self.learning_rate), loss='mse')
        return model

接下来，我们定义经验回放函数，用于将经验元组保存到缓存中。

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

然后，我们定义动作选择函数。在这个函数中，我们使用探索率ε-greedy策略选择动作。当随机数小于探索率时，我们随机选择一个动作；否则，我们使用模型预测的Q值选择最优动作。

    def act(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])

接下来，我们定义训练函数。在这个函数中，我们首先从经验回放缓存中随机选择一批经验元组。然后，我们使用模型预测Q值，并计算目标Q值。最后，我们使用梯度下降法更新模型。

    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])
            q_values = self.model.predict(state)
            q_values[0][action] = target
            self.model.fit(state, q_values, epochs=1, verbose=0)
        if self.epsilon > 0.01:
            self.epsilon *= self.epsilon_decay

最后，我们定义保存和加载模型的函数。

    def save(self, name):
        self.model.save(name)

    def load(self, name):
        self.model = tf.keras.models.load_model(name)

现在，我们可以创建一个DQNAgent对象并开始训练。

env = gym.make('CartPole-v0')
state_shape = env.observation_space.shape
action_size = env.action_space.n
agent = DQNAgent(state_shape, action_size, learning_rate=0.001, gamma=0.95, epsilon=1.0, epsilon_decay=0.995)

for episode in range(1000):
    state = env.reset()
    state = np.reshape(state, [1, state_shape[0], state_shape[1], state_shape[2]])
    for time in range(500):
        action = agent.act(state)
        next_state, reward, done, _ = env.step(action)
        next_state = np.reshape(next_state, [1, state_shape[0], state_shape[1], state_shape[2]])
        agent.remember(state, action, reward, next_state, done)
        state = next_state
        if done:
            print("episode: {}/{}, score: {}, epsilon: {:.2}".format(episode, 1000, time, agent.epsilon))
            break
        agent.train(batch_size=32)
    if episode % 10 == 0:
        agent.save("cartpole-dqn.h5")

在训练结束后，我们可以使用以下代码测试模型的性能：

agent.load("cartpole-dqn.h5")
state = env.reset()
state = np.reshape(state, [1, state_shape[0], state_shape[1], state_shape[2]])
for time in range(500):
    env.render()
    action = agent.act(state)
    next_state, reward, done, _ = env.step(action)
    next_state = np.reshape(next_state, [1, state_shape[0], state_shape[1], state_shape[2]])
    state = next_state
    if done:
        break
env.close()