dict1.setdefault(Global_Time,[]).append(id_v[i])

这行代码的作用是将 `id_v[i]` 添加到 `dict1` 字典中 `Global_Time` 对应的列表中。如果 `dict1` 中已经存在 `Global_Time` 这个键，则直接将 `id_v[i]` 添加到对应的列表中；如果 `dict1` 中不存在 `Global_Time` 这个键，则先将 `Global_Time` 作为键添加到 `dict1` 中，并将其对应的值设置为空列表 `[]`，然后再将 `id_v[i]` 添加到这个列表中。

具体来说，`setdefault()` 方法会先查找字典中是否存在指定的键，如果存在，则直接返回对应的值；如果不存在，则将指定的键添加到字典中，并将其对应的值设置为指定的默认值（如果提供了默认值的话）。然后再将新的值添加到对应的列表中。

这行代码的实际作用可以理解为：

if Global_Time in dict1:
    dict1[Global_Time].append(id_v[i])
else:
    dict1[Global_Time] = [id_v[i]]

但是，使用 `setdefault()` 方法可以更简洁地实现这个逻辑。

相关问题

dict1.setdefault(Global_Time,[]).append(id_v[i]) AttributeError: 'NoneType' object has no attribute 'append'

这个错误通常是因为你在使用 `setdefault` 方法时，没有给它一个默认值或者默认值为 `None`。在这种情况下，`setdefault` 方法会返回 `None`，而不是一个列表，因此在尝试将值追加到它上面时会出现 `'NoneType' object has no attribute 'append'` 错误。

你可以在使用 `setdefault` 方法时提供一个空列表作为默认值，例如：

my_dict = {}
my_dict.setdefault('my_key', [])
my_dict['my_key'].append('my_value')

这样，如果字典中没有 `my_key` 键，`setdefault` 方法就会将一个空列表作为默认值添加到字典中。然后你就可以通过 `my_dict['my_key']` 获取这个列表，并将值追加到它上面。

写一个python程序使用DQN的方法解决CartPole-V0问题，使用英文注释，不使用keras

Sure, I can help you with that! Here is a simple Python program using the DQN method to solve the CartPole-V0 problem. This program does not use Keras and has English comments to help you understand what's happening.

First, we'll import the necessary libraries:

import random
import numpy as np
import tensorflow as tf
import gym

Next, we'll set up our parameters:

# Define some hyperparameters
batch_size = 32 # How many memory samples to train on
replay_memory_size = 10000 # How many recent frames to remember
gamma = 0.99 # Discount rate for future rewards
n_episodes = 5000 # How many episodes to train for
n_steps = 200 # Maximum number of steps in each episode
start_epsilon = 1.0 # Initial value of epsilon for epsilon-greedy exploration
end_epsilon = 0.05 # Final value of epsilon for epsilon-greedy exploration
epsilon_decay_steps = 10000 # Number of steps to decay epsilon from start to end value
learning_rate = 0.001 # Learning rate for the neural network optimizer

Now we can define our neural network:

# Define the neural network
n_inputs = 4 # Number of input features (four observations from the environment)
n_hidden = 32 # Number of hidden neurons in the network
n_outputs = 2 # Number of output neurons (actions: move left or right)
initializer = tf.random_normal_initializer()

# Define the input and output placeholders
tf.reset_default_graph()
X = tf.placeholder(tf.float32, shape=[None, n_inputs])
y = tf.placeholder(tf.float32, shape=[None, n_outputs])

# Define the network architecture
hidden = tf.layers.dense(X, n_hidden, activation=tf.nn.relu, kernel_initializer=initializer)
logits = tf.layers.dense(hidden, n_outputs, kernel_initializer=initializer)

# Define the loss function and optimizer
cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(labels=y, logits=logits)
optimizer = tf.train.AdamOptimizer(learning_rate)
training_op = optimizer.minimize(cross_entropy)

# Define the prediction and exploration functions
predict_op = tf.argmax(logits, axis=1)
exploration_op = tf.random_uniform(tf.shape(logits))

Next, we'll define our memory and exploration strategies:

# Define the memory and exploration strategies
replay_memory = []
def sample_memories(batch_size):
    indices = np.random.permutation(len(replay_memory))[:batch_size]
    cols = [[], [], [], [], []] # state, action, reward, next_state, done
    for index in indices:
        memory = replay_memory[index]
        for col, value in zip(cols, memory):
            col.append(value)
    cols = [np.array(col) for col in cols]
    return (cols[0], cols[1], cols[2].reshape(-1, 1), cols[3], cols[4].reshape(-1, 1))

epsilon = start_epsilon
def explore(state, step):
    if step < epsilon_decay_steps:
        epsilon = start_epsilon - step / epsilon_decay_steps * (start_epsilon - end_epsilon)
    else:
        epsilon = end_epsilon
    if np.random.rand() < epsilon:
        return np.random.randint(n_outputs)
    else:
        return predict_op.eval(feed_dict={X: state.reshape(1, n_inputs)})

Now we're ready to start training:

# Start training
env = gym.make("CartPole-v0")
with tf.Session() as sess:
    tf.global_variables_initializer().run()
    for episode in range(n_episodes):
        # Reset the environment
        obs = env.reset()
        for step in range(n_steps):
            # Explore the environment
            action = explore(obs, step)
            next_obs, reward, done, _ = env.step(action)
            # Store the experience in replay memory
            replay_memory.append((obs, action, reward, next_obs, done))
            obs = next_obs
            # Sample a random batch of experiences from replay memory
            X_state_val, X_action_val, rewards, X_next_state_val, dones = (sample_memories(batch_size))
            # Compute the TD target
            next_q_values = logits.eval(feed_dict={X: X_next_state_val})
            max_next_q_values = np.max(next_q_values, axis=1, keepdims=True)
            # Compute the training targets
            y_val = rewards + (1 - dones) * gamma * max_next_q_values
            # Train the network on a batch of experiences
            training_op.run(feed_dict={X: X_state_val, y: y_val})
            # Every 50 episodes, print the episode number and average reward
            if episode % 50 == 0:
                total_reward = 0
                obs = env.reset()
                for i in range(n_steps):
                    action = predict_op.eval(feed_dict={X: obs.reshape(1, n_inputs)})
                    obs, reward, done, _ = env.step(action)
                    total_reward += reward
                    if done:
                        break
                print("Episode:", episode, "  Reward:", total_reward)

And that's it! This program should train a neural network using the DQN method to solve the CartPole-V0 problem, and print the episode number and average reward every 50 episodes. Let me know if you have any questions or if there's anything else I can help you with!