基于tensorflow写一个DQN算法代码

好的，下面是一个基于tensorflow的DQN算法代码。

首先，需要导入相关的库：

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

然后定义一个DQN类，包含训练和测试两个方法：

class DQN:
    def __init__(self, env):
        self.env = env

        # DQN参数
        self.memory = deque(maxlen=2000)
        self.gamma = 0.95
        self.epsilon = 1.0
        self.epsilon_min = 0.01
        self.epsilon_decay = 0.995
        self.learning_rate = 0.001
        self.tau = 0.125

        # 神经网络模型
        self.model = self.create_model()
        self.target_model = self.create_model()
        self.target_model.set_weights(self.model.get_weights())

    # 创建神经网络模型
    def create_model(self):
        model = tf.keras.models.Sequential([
            tf.keras.layers.Dense(24, input_dim=self.env.observation_space.shape[0], activation='relu'),
            tf.keras.layers.Dense(24, activation='relu'),
            tf.keras.layers.Dense(self.env.action_space.n, activation='linear')
        ])
        model.compile(loss='mse', optimizer=tf.keras.optimizers.Adam(lr=self.learning_rate))
        return model

    # 训练神经网络模型
    def train(self, episodes=100):
        for episode in range(episodes):
            state = self.env.reset()
            state = np.reshape(state, [1, self.env.observation_space.shape[0]])
            done = False
            total_reward = 0

            while not done:
                # 选择动作
                if np.random.rand() <= self.epsilon:
                    action = env.action_space.sample()
                else:
                    action = np.argmax(self.model.predict(state)[0])

                # 执行动作
                next_state, reward, done, _ = self.env.step(action)
                next_state = np.reshape(next_state, [1, self.env.observation_space.shape[0]])
                total_reward += reward

                # 存储经验
                self.memory.append((state, action, reward, next_state, done))

                # 更新当前状态
                state = next_state

                # 训练模型
                self.replay()

            # 输出本轮结果
            print("Episode {}/{}, Total Reward: {}, Epsilon: {:.2f}"
                  .format(episode, episodes, total_reward, self.epsilon))

            # 更新目标模型
            self.update_target_model()

            # 更新epsilon
            if self.epsilon > self.epsilon_min:
                self.epsilon *= self.epsilon_decay

    # 测试神经网络模型
    def test(self, episodes=10):
        for episode in range(episodes):
            state = self.env.reset()
            state = np.reshape(state, [1, self.env.observation_space.shape[0]])
            done = False
            total_reward = 0

            while not done:
                # 选择动作
                action = np.argmax(self.model.predict(state)[0])

                # 执行动作
                next_state, reward, done, _ = self.env.step(action)
                next_state = np.reshape(next_state, [1, self.env.observation_space.shape[0]])
                total_reward += reward

                # 更新当前状态
                state = next_state

            # 输出本轮结果
            print("Episode {}/{}, Total Reward: {}"
                  .format(episode, episodes, total_reward))

    # 训练模型
    def replay(self, batch_size=32):
        minibatch = random.sample(self.memory, batch_size)

        for state, action, reward, next_state, done in minibatch:
            if done:
                target = reward
            else:
                target = reward + self.gamma * np.amax(self.target_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)

    # 更新目标模型
    def update_target_model(self):
        weights = self.model.get_weights()
        target_weights = self.target_model.get_weights()

        for i in range(len(target_weights)):
            target_weights[i] = self.tau * weights[i] + (1 - self.tau) * target_weights[i]

        self.target_model.set_weights(target_weights)

最后，我们可以使用如下代码来训练模型：

env = gym.make('CartPole-v1')
dqn = DQN(env)
dqn.train()

或者使用如下代码来测试模型：

env = gym.make('CartPole-v1')
dqn = DQN(env)
dqn.model.load_weights('dqn.h5')
dqn.test()

注意，这里仅仅是一个简单的DQN代码示例，并没有进行特定问题的调参，具体参数需要根据不同的场景进行调整。