我要DQN算法的python代码

下面是一个简单的DQN算法实现的Python代码：

import tensorflow as tf
import numpy as np


class DQN:
    def __init__(self, state_dim, action_dim, gamma, learning_rate):
        self.state_dim = state_dim
        self.action_dim = action_dim
        self.gamma = gamma
        self.learning_rate = learning_rate

        self.memory = []

        self.model = tf.keras.Sequential([
            tf.keras.layers.Dense(32, input_shape=(self.state_dim,), activation='relu'),
            tf.keras.layers.Dense(32, activation='relu'),
            tf.keras.layers.Dense(self.action_dim, activation=None)
        ])

        self.optimizer = tf.keras.optimizers.Adam(lr=self.learning_rate)

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

    def act(self, state, epsilon):
        if np.random.rand() < epsilon:
            return np.random.randint(self.action_dim)
        else:
            q_values = self.model.predict(state)
            return np.argmax(q_values[0])

    def replay(self, batch_size):
        if len(self.memory) < batch_size:
            return

        samples = np.array(random.sample(self.memory, batch_size))

        states = np.vstack(samples[:, 0])
        actions = samples[:, 1]
        rewards = samples[:, 2]
        next_states = np.vstack(samples[:, 3])
        dones = samples[:, 4]

        targets = rewards + self.gamma * np.max(self.model.predict(next_states), axis=1) * (1 - dones)

        q_values = self.model.predict(states)

        for i in range(batch_size):
            q_values[i][actions[i]] = targets[i]

        self.model.fit(states, q_values, verbose=0)

这是一个简单的DQN类，包含remember、act、replay等方法，可以根据需要进行调整。其中，act方法用来根据当前状态选择动作，replay方法用来对记忆库中的样本进行训练。