PER-DQN是什么

根据提供的引用内容，没有直接提到PER-DQN。但是可以根据引用中提到的Ensemble-DQN和Averaged-DQN来推测PER-DQN的含义。PER-DQN是Prioritized Experience Replay Deep Q-Network的缩写，是一种基于经验回放的深度强化学习算法。与传统的DQN算法不同，PER-DQN使用优先级队列来存储经验，以便更有效地学习重要的经验。在PER-DQN中，经验被赋予不同的优先级，优先级高的经验被更频繁地抽样以便更好地学习。因此，PER-DQN在学习效率和性能方面优于传统的DQN算法。

相关问题

dqn python代码

以下是DQN算法的Python实现（使用TensorFlow）：

import tensorflow as tf
import numpy as np
import gym

# 创建游戏环境
env = gym.make('CartPole-v0')

# 定义超参数
learning_rate = 0.01
discount_factor = 0.99
batch_size = 64
memory_size = 10000
epsilon = 1.0
epsilon_decay = 0.9995
min_epsilon = 0.01
n_episodes = 2000
n_steps_per_episode = 200
update_target_network_every = 100

# 创建经验回放存储器
memory = []

# 定义神经网络
class DQN(tf.keras.Model):
    def __init__(self, n_actions):
        super(DQN, self).__init__()
        self.dense1 = tf.keras.layers.Dense(32, activation='relu')
        self.dense2 = tf.keras.layers.Dense(32, activation='relu')
        self.dense3 = tf.keras.layers.Dense(n_actions)

    def call(self, inputs):
        x = self.dense1(inputs)
        x = self.dense2(x)
        x = self.dense3(x)
        return x

# 创建主网络和目标网络
n_actions = env.action_space.n
main_network = DQN(n_actions)
target_network = DQN(n_actions)
target_network.set_weights(main_network.get_weights())

# 定义损失函数和优化器
loss_fn = tf.keras.losses.MeanSquaredError()
optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)

# 定义动作选择策略
def choose_action(state, epsilon):
    if np.random.rand() < epsilon:
        return env.action_space.sample()
    else:
        Q_values = main_network(tf.constant([state], dtype=tf.float32)).numpy()[0]
        return np.argmax(Q_values)

# 从经验回放存储器中随机采样一个批次进行训练
def train():
    batch = np.array(memory)[np.random.choice(len(memory), batch_size, replace=False)]
    states = np.array([b[0] for b in batch])
    actions = np.array([b[1] for b in batch])
    rewards = np.array([b[2] for b in batch])
    next_states = np.array([b[3] for b in batch])
    dones = np.array([b[4] for b in batch])
    Q_values_next = target_network(next_states).numpy()
    Q_values_target = rewards + discount_factor * np.max(Q_values_next, axis=1) * (1 - dones)
    Q_values = main_network(states).numpy()
    Q_values[np.arange(batch_size), actions] = Q_values_target
    with tf.GradientTape() as tape:
        loss = loss_fn(Q_values, main_network(states))
    grads = tape.gradient(loss, main_network.trainable_variables)
    optimizer.apply_gradients(zip(grads, main_network.trainable_variables))

# 训练主网络
total_reward_history = []
for episode in range(n_episodes):
    state = env.reset()
    total_reward = 0
    for step in range(n_steps_per_episode):
        epsilon = max(epsilon * epsilon_decay, min_epsilon)
        action = choose_action(state, epsilon)
        next_state, reward, done, _ = env.step(action)
        memory.append((state, action, reward, next_state, done))
        total_reward += reward
        state = next_state
        if len(memory) > memory_size:
            memory.pop(0)
        if episode % update_target_network_every == 0:
            target_network.set_weights(main_network.get_weights())
        if done:
            break
    if len(memory) > batch_size:
        train()
    total_reward_history.append(total_reward)
    print(f'Episode {episode} completed with total reward of {total_reward}.')

juypter 运行Cartpole-v1游戏中 训练测试DQN强化学习算法的完整代码

在使用Jupyter Notebook运行CartPole-v1游戏中的深度Q网络（Deep Q-Network，DQN）强化学习算法，首先需要安装必要的库，如`gym`, `tensorflow`, `keras`, 和 `matplotlib`等。由于这是一个复杂的示例，以下是简化的步骤和关键代码片段：

1. 导入所需库：

import gym
import tensorflow as tf
from tensorflow.keras import layers
import numpy as np
import matplotlib.pyplot as plt

2. 创建环境并观察状态空间和动作空间：

env = gym.make('CartPole-v1')
obs_shape = env.observation_space.shape
n_actions = env.action_space.n

3. 定义DQN模型：

model = tf.keras.Sequential([
    layers.Dense(64, activation='relu', input_shape=obs_shape),
    layers.Dense(64, activation='relu'),
    layers.Dense(n_actions)
])

4. 创建优化器、损失函数和回调（如记忆回放缓冲区和目标网络更新）：

optimizer = tf.keras.optimizers.Adam()
loss_fn = tf.keras.losses.Huber(reduction='none')

# Memory buffer and target network (for experience replay and periodic updates)
memory = []
target_update_freq = 100
target_model = tf.keras.models.clone_model(model)
target_model.set_weights(model.get_weights())

5. 主训练循环（包含体验收集、学习、绘图等部分）：

def train_step(state, action, reward, next_state, done):
    # ... (将经历添加到内存缓冲区并采样经验进行学习)

def run_episode():
    # ... (获取初始状态，执行动作，存储经验和奖励，直到达到最大步数或游戏结束)

def main_loop(num_episodes, max_steps_per_episode):
    for episode in range(num_episodes):
        state = env.reset()
        for t in range(max_steps_per_episode):
            # ... (获取行动，执行并记录数据，训练模型，然后移动到下一个状态)
            if done:
                break

        # ... (绘制训练进度图表)

if __name__ == "__main__":
    main_loop(num_episodes=1000, max_steps_per_episode=500)

完整的代码会比较长，包含了记忆管理、探索策略（比如ε-greedy）、经验回放以及定期更新目标网络等功能。实际编写过程中，你需要结合这些步骤，并参考相关的深度强化学习教程或GitHub上的代码示例。