double DQN代码实现

感谢您的提问。以下是 Double DQN 代码的实现：

首先，我们先定义一个 Double DQN 的类：

import tensorflow as tf
from tensorflow import keras

class DoubleDQN:
    def __init__(self, input_shape, output_shape, learning_rate=0.001, discount_factor=0.99):
        self.input_shape = input_shape
        self.output_shape = output_shape
        self.learning_rate = learning_rate
        self.discount_factor = discount_factor
        
        self.main_q_network = self.build_q_network()
        self.target_q_network = self.build_q_network()
        self.optimizer = tf.keras.optimizers.Adam(lr=self.learning_rate)
        
    def build_q_network(self):
        inputs = keras.layers.Input(shape=self.input_shape)
        x = keras.layers.Dense(64, activation='relu')(inputs)
        x = keras.layers.Dense(64, activation='relu')(x)
        q_values = keras.layers.Dense(self.output_shape, activation='linear')(x)
        model = keras.Model(inputs=inputs, outputs=q_values)
        return model

接下来，我们定义 Double DQN 的训练过程：

import numpy as np

class DoubleDQN:
    ...

    def train(self, states, actions, rewards, next_states, dones, batch_size=32):
        indexes = np.random.randint(0, len(states), size=batch_size)
        states_batch = states[indexes]
        action_batch = actions[indexes]
        reward_batch = rewards[indexes]
        next_states_batch = next_states[indexes]
        done_batch = dones[indexes]

        q_values_next = self.main_q_network.predict(next_states_batch)
        best_actions = np.argmax(q_values_next, axis=-1)
        q_values_next_target = self.target_q_network.predict(next_states_batch)
        target_values = q_values_next_target[np.arange(batch_size), best_actions]
        target_values = reward_batch + (1-done_batch) * self.discount_factor * target_values

        with tf.GradientTape() as tape:
            q_values = self.main_q_network(states_batch)
            q_values_actions = tf.reduce_sum(tf.one_hot(action_batch, self.output_shape) * q_values, axis=-1)
            loss = tf.reduce_mean(tf.square(q_values_actions - target_values))
        grads = tape.gradient(loss, self.main_q_network.trainable_variables)
        self.optimizer.apply_gradients(zip(grads, self.main_q_network.trainable_variables))
        
        return loss.numpy()

最后，我们还需要一个更新 target network 的函数：

class DoubleDQN:
    ...

    def update_target_network(self):
        self.target_q_network.set_weights(self.main_q_network.get_weights())

这样，我们就完成了 Double DQN 的实现。当然，在实际的应用中可能还需要做一些细节上的调整，例如 epsilon-greedy 策略等等。