用python写一个DQN代码，提供一个基础的框架就可以

好的，下面是一个基础的DQN代码框架，可以参考一下：

import gym
import numpy as np
import tensorflow as tf

# 定义深度神经网络模型
class DQN(tf.keras.Model):
    def __init__(self, num_actions):
        super(DQN, self).__init__()
        self.dense1 = tf.keras.layers.Dense(64, activation='relu')
        self.dense2 = tf.keras.layers.Dense(64, activation='relu')
        self.dense3 = tf.keras.layers.Dense(num_actions, activation=None)
    
    def call(self, inputs):
        x = self.dense1(inputs)
        x = self.dense2(x)
        x = self.dense3(x)
        return x

# 定义经验回放缓存类
class ReplayBuffer:
    def __init__(self, capacity):
        self.capacity = capacity
        self.buffer = []
    
    def push(self, state, action, reward, next_state, done):
        if len(self.buffer) >= self.capacity:
            self.buffer.pop(0)
        self.buffer.append((state, action, reward, next_state, done))
    
    def sample(self, batch_size):
        batch = np.random.choice(len(self.buffer), batch_size, replace=False)
        states, actions, rewards, next_states, dones = [], [], [], [], []
        for i in batch:
            state, action, reward, next_state, done = self.buffer[i]
            states.append(state)
            actions.append(action)
            rewards.append(reward)
            next_states.append(next_state)
            dones.append(done)
        return np.array(states), np.array(actions), np.array(rewards), np.array(next_states), np.array(dones)

# 定义DQN类
class DQNAgent:
    def __init__(self, env):
        self.env = env
        self.replay_buffer = ReplayBuffer(capacity=10000)
        self.model = DQN(num_actions=self.env.action_space.n)
        self.target_model = DQN(num_actions=self.env.action_space.n)
        self.target_model.set_weights(self.model.get_weights())
        self.optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)
        self.gamma = 0.99
        self.epsilon = 1.0
        self.epsilon_decay = 0.995
        self.epsilon_min = 0.01
        self.batch_size = 32
        self.steps = 0
        self.update_freq = 1000
    
    def act(self, state):
        if np.random.rand() < self.epsilon:
            return self.env.action_space.sample()
        state = np.expand_dims(state, axis=0)
        q_value = self.model.predict(state)
        return np.argmax(q_value[0])
    
    def train(self):
        self.steps += 1
        states, actions, rewards, next_states, dones = self.replay_buffer.sample(self.batch_size)
        targets = self.model.predict_on_batch(states)
        q_next = self.target_model.predict_on_batch(next_states)
        targets[np.arange(self.batch_size), actions] = rewards + self.gamma * np.max(q_next, axis=1) * (1 - dones)
        loss = self.model.train_on_batch(states, targets)
        if self.steps % self.update_freq == 0:
            self.target_model.set_weights(self.model.get_weights())
        if self.epsilon > self.epsilon_min:
            self.epsilon *= self.epsilon_decay
        return loss

# 创建CartPole-v0环境实例
env = gym.make('CartPole-v0')

# 创建DQNAgent实例
agent = DQNAgent(env)

# 训练DQN网络
for episode in range(100):
    state = env.reset()
    episode_reward = 0
    for step in range(200):
        action = agent.act(state)
        next_state, reward, done, info = env.step(action)
        episode_reward += reward
        agent.replay_buffer.push(state, action, reward, next_state, done)
        loss = agent.train()
        state = next_state
        if done:
            break
    print('Episode: {}, Steps: {}, Epsilon: {:.4f}, Episode Reward: {:.4f}, Loss: {:.4f}'.format(
        episode, step+1, agent.epsilon, episode_reward, loss))

上面的代码是使用TensorFlow 2.0实现的DQN算法，使用经验回放缓存来存储与批量采样过程。CartPole-v0游戏是OpenAI Gym中的一个非常简单的游戏，首先建议你先玩一下这个游戏，了解一下游戏规则和对象。你可以将这个代码作为代码框架，然后根据自己的需要进行修改，比如替换成其他的游戏环境，或者尝试更先进的深度强化学习算法，比如：DDQN、Dueling DQN或Rainbow等算法。