解释这行代码self.sess.run( [self.q_next, self.q_eval]

这行代码是在进行神经网络训练时用到的，其中self.sess.run是运行TensorFlow的会话(Session)，[self.q_next, self.q_eval]是要同时运行的TensorFlow操作。在深度强化学习中，self.q_eval表示当前策略神经网络的输出，self.q_next表示目标神经网络的输出，这两个神经网络用于预测在某个状态下采取某个动作后得到的奖励值。在运行时，会通过输入的状态得到两个网络的输出，并根据采用的学习算法计算出误差，并更新策略神经网络的权重，以实现模型的优化。

相关问题

dqn代码 tensorflow

以下是一个DQN算法的TensorFlow代码示例：

import tensorflow as tf
import numpy as np
import gym

# 神经网络类
class DQN:
    def __init__(self, n_actions, n_features, learning_rate=0.01, reward_decay=0.9, e_greedy=0.9, replace_target_iter=300, memory_size=500, batch_size=32, e_greedy_increment=None):
        self.n_actions = n_actions
        self.n_features = n_features
        self.lr = learning_rate
        self.gamma = reward_decay
        self.epsilon_max = e_greedy
        self.replace_target_iter = replace_target_iter
        self.memory_size = memory_size
        self.batch_size = batch_size
        self.epsilon_increment = e_greedy_increment
        self.epsilon = 0 if e_greedy_increment is not None else self.epsilon_max

        self.learn_step_counter = 0
        self.memory = np.zeros((self.memory_size, n_features * 2 + 2))
        self.build_net()

        t_params = tf.get_collection('target_net_params')
        e_params = tf.get_collection('eval_net_params')
        self.replace_target_op = [tf.assign(t, e) for t, e in zip(t_params, e_params)]

        self.sess = tf.Session()
        self.sess.run(tf.global_variables_initializer())
        self.cost_his = []

    # 建立神经网络
    def build_net(self):
        self.s = tf.placeholder(tf.float32, [None, self.n_features], name='s')
        self.q_target = tf.placeholder(tf.float32, [None, self.n_actions], name='Q_target')
        with tf.variable_scope('eval_net'):
            c_names = ['eval_net_params', tf.GraphKeys.GLOBAL_VARIABLES]
            n_l1 = 10
            w_initializer = tf.random_normal_initializer(0., 0.3)
            b_initializer = tf.constant_initializer(0.1)
            with tf.variable_scope('l1'):
                w1 = tf.get_variable('w1', [self.n_features, n_l1], initializer=w_initializer, collections=c_names)
                b1 = tf.get_variable('b1', [1, n_l1], initializer=b_initializer, collections=c_names)
                l1 = tf.nn.relu(tf.matmul(self.s, w1) + b1)

            with tf.variable_scope('l2'):
                w2 = tf.get_variable('w2', [n_l1, self.n_actions], initializer=w_initializer, collections=c_names)
                b2 = tf.get_variable('b2', [1, self.n_actions], initializer=b_initializer, collections=c_names)
                self.q_eval = tf.matmul(l1, w2) + b2

        with tf.variable_scope('loss'):
            self.loss = tf.reduce_mean(tf.squared_difference(self.q_target, self.q_eval))
        with tf.variable_scope('train'):
            self._train_op = tf.train.RMSPropOptimizer(self.lr).minimize(self.loss)

        self.s_ = tf.placeholder(tf.float32, [None, self.n_features], name='s_')
        with tf.variable_scope('target_net'):
            c_names = ['target_net_params', tf.GraphKeys.GLOBAL_VARIABLES]
            n_l1 = 10
            w_initializer = tf.random_normal_initializer(0., 0.3)
            b_initializer = tf.constant_initializer(0.1)
            with tf.variable_scope('l1'):
                w1 = tf.get_variable('w1', [self.n_features, n_l1], initializer=w_initializer, collections=c_names)
                b1 = tf.get_variable('b1', [1, n_l1], initializer=b_initializer, collections=c_names)
                l1 = tf.nn.relu(tf.matmul(self.s_, w1) + b1)

            with tf.variable_scope('l2'):
                w2 = tf.get_variable('w2', [n_l1, self.n_actions], initializer=w_initializer, collections=c_names)
                b2 = tf.get_variable('b2', [1, self.n_actions], initializer=b_initializer, collections=c_names)
                self.q_next = tf.matmul(l1, w2) + b2

    # 记忆
    def store_transition(self, s, a, r, s_):
        if not hasattr(self, 'memory_counter'):
            self.memory_counter = 0
        transition = np.hstack((s, [a, r], s_))
        index = self.memory_counter % self.memory_size
        self.memory[index, :] = transition
        self.memory_counter += 1

    # 选择动作
    def choose_action(self, observation):
        observation = observation[np.newaxis, :]
        if np.random.uniform() < self.epsilon:
            actions_value = self.sess.run(self.q_eval, feed_dict={self.s: observation})
            action = np.argmax(actions_value)
        else:
            action = np.random.randint(0, self.n_actions)
        return action

    # 学习
    def learn(self):
        if self.learn_step_counter % self.replace_target_iter == 0:
            self.sess.run(self.replace_target_op)
            print('\ntarget_params_replaced\n')

        if self.memory_counter > self.memory_size:
            sample_index = np.random.choice(self.memory_size, size=self.batch_size)
        else:
            sample_index = np.random.choice(self.memory_counter, size=self.batch_size)
        batch_memory = self.memory[sample_index, :]

        q_next, q_eval4next = self.sess.run(
            [self.q_next, self.q_eval],
            feed_dict={
                self.s_: batch_memory[:, -self.n_features:],
                self.s: batch_memory[:, :self.n_features],
            })
        q_eval = self.sess.run(self.q_eval, {self.s: batch_memory[:, :self.n_features]})

        q_target = q_eval.copy()
        batch_index = np.arange(self.batch_size, dtype=np.int32)
        eval_act_index = batch_memory[:, self.n_features].astype(int)
        reward = batch_memory[:, self.n_features + 1]

        q_target[batch_index, eval_act_index] = reward + self.gamma * np.max(q_next, axis=1)
        _, self.cost = self.sess.run([self._train_op, self.loss],
                                     feed_dict={self.s: batch_memory[:, :self.n_features],
                                                self.q_target: q_target})
        self.cost_his.append(self.cost)

        self.epsilon = self.epsilon + self.epsilon_increment if self.epsilon < self.epsilon_max else self.epsilon_max
        self.learn_step_counter += 1

# 训练网络
def main():
    env = gym.make('CartPole-v0')
    env = env.unwrapped
    n_actions = env.action_space.n
    n_features = env.observation_space.shape[0]

    dqn = DQN(n_actions=n_actions, n_features=n_features, learning_rate=0.01, reward_decay=0.9, e_greedy=0.9, replace_target_iter=100, memory_size=2000, e_greedy_increment=0.001, )

    for i_episode in range(400):
        observation = env.reset()
        ep_r = 0
        while True:
            env.render()
            action = dqn.choose_action(observation)

            observation_, reward, done, info = env.step(action)

            x, x_dot, theta, theta_dot = observation_
            r1 = (env.x_threshold - abs(x)) / env.x_threshold - 0.8
            r2 = (env.theta_threshold_radians - abs(theta)) / env.theta_threshold_radians - 0.5
            reward = r1 + r2

            dqn.store_transition(observation, action, reward, observation_)

            ep_r += reward
            if dqn.memory_counter > dqn.memory_size:
                dqn.learn()
                if done:
                    print('episode: ', i_episode, 'ep_r: ', round(ep_r, 2), ' epsilon: ', round(dqn.epsilon, 2))
            if done:
                break
            observation = observation_

    print('game over')
    env.close()

if __name__ == '__main__':
    main()

这个代码演示了如何使用DQN算法来解决CartPole-v0游戏。在代码中，定义了一个DQN类，其中包含了神经网络的构建，记忆的存储，动作的选择和学习。在主函数中，创建了CartPole-v0环境，并使用DQN算法进行训练。

DQN算法对Atari训练代码

DQN算法对Atari训练的代码如下：

import gym
import numpy as np
import tensorflow as tf

env = gym.make('SpaceInvaders-v0')
state_size = env.observation_space.shape
action_size = env.action_space.n

# Hyperparameters
learning_rate = 0.001
memory_size = 1000000
batch_size = 32
gamma = 0.99
epsilon = 1.0
epsilon_min = 0.01
epsilon_decay = 0.995
target_update_frequency = 10000
num_episodes = 10000
max_steps = 5000

# Replay Memory
memory = []

# Q-Network
class QNetwork:
    def __init__(self, state_size, action_size, learning_rate):
        self.state_size = state_size
        self.action_size = action_size
        self.learning_rate = learning_rate
        
        self.inputs = tf.placeholder(tf.float32, [None, *state_size])
        self.actions = tf.placeholder(tf.float32, [None, action_size])
        self.targets = tf.placeholder(tf.float32, [None])
        
        conv1 = tf.layers.conv2d(inputs=self.inputs, filters=32, kernel_size=[8,8], strides=[4,4], padding="VALID", activation=tf.nn.relu)
        conv2 = tf.layers.conv2d(inputs=conv1, filters=64, kernel_size=[4,4], strides=[2,2], padding="VALID", activation=tf.nn.relu)
        conv3 = tf.layers.conv2d(inputs=conv2, filters=64, kernel_size=[3,3], strides=[1,1], padding="VALID", activation=tf.nn.relu)
        flatten = tf.layers.flatten(conv3)
        fc1 = tf.layers.dense(inputs=flatten, units=512, activation=tf.nn.relu)
        self.output = tf.layers.dense(inputs=fc1, units=action_size)
        
        self.loss = tf.reduce_mean(tf.square(self.targets - tf.reduce_sum(tf.multiply(self.output, self.actions), axis=1)))
        self.optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate).minimize(self.loss)

# DQN Agent
class DQNAgent:
    def __init__(self, state_size, action_size, learning_rate, memory_size, batch_size, gamma, epsilon, epsilon_min, epsilon_decay, target_update_frequency):
        self.state_size = state_size
        self.action_size = action_size
        self.learning_rate = learning_rate
        self.memory_size = memory_size
        self.batch_size = batch_size
        self.gamma = gamma
        self.epsilon = epsilon
        self.epsilon_min = epsilon_min
        self.epsilon_decay = epsilon_decay
        self.target_update_frequency = target_update_frequency
        
        self.q_network = QNetwork(state_size, action_size, learning_rate)
        self.target_network = QNetwork(state_size, action_size, learning_rate)
        self.replay_memory = []
        self.timestep = 0
        
        self.sess = tf.Session()
        self.sess.run(tf.global_variables_initializer())
    
    def act(self, state):
        if np.random.rand() <= self.epsilon:
            return np.random.choice(self.action_size)
        q_values = self.sess.run(self.q_network.output, feed_dict={self.q_network.inputs: state.reshape(1, *self.state_size)})
        return np.argmax(q_values[0])
    
    def remember(self, state, action, reward, next_state, done):
        self.replay_memory.append((state, action, reward, next_state, done))
        if len(self.replay_memory) > self.memory_size:
            self.replay_memory.pop(0)
        
    def replay(self):
        if len(self.replay_memory) < self.batch_size:
            return
        samples = np.random.choice(self.replay_memory, self.batch_size)
        states, actions, rewards, next_states, dones = map(np.array, zip(*samples))
        targets = rewards + self.gamma * (np.amax(self.target_network.output.eval(feed_dict={self.target_network.inputs: next_states}), axis=1)) * (1 - dones)
        targets_full = self.q_network.output.eval(feed_dict={self.q_network.inputs: states})
        targets_full[np.arange(self.batch_size), actions] = targets
        self.q_network.optimizer.run(feed_dict={self.q_network.inputs: states, self.q_network.actions: np.eye(self.action_size)[actions], self.q_network.targets: targets_full})
        
        if self.timestep % self.target_update_frequency == 0:
            self.target_network = self.q_network
        self.timestep += 1
        
    def train(self, num_episodes, max_steps):
        for i in range(num_episodes):
            state = env.reset()
            done = False
            total_reward = 0
            for j in range(max_steps):
                action = self.act(state)
                next_state, reward, done, _ = env.step(action)
                self.remember(state, action, reward, next_state, done)
                state = next_state
                total_reward += reward
                self.replay()
                if done:
                    break
            if self.epsilon > self.epsilon_min:
                self.epsilon *= self.epsilon_decay
            print("Episode: {}, Reward: {}".format(i, total_reward))
            
agent = DQNAgent(state_size, action_size, learning_rate, memory_size, batch_size, gamma, epsilon, epsilon_min, epsilon_decay, target_update_frequency)
agent.train(num_episodes, max_steps)

这个代码实现了DQN算法，使用TensorFlow实现了Q-Network和Target-Network。在每个episode中，循环执行以下步骤：

1. 根据当前状态选择一个动作。
2. 执行所选动作并观察下一个状态和奖励。
3. 将当前状态、所选动作、奖励、下一个状态、以及是否终止的标志存储到replay memory中。
4. 从replay memory中随机选择一批经验进行训练，更新Q-Network的参数。
5. 如果需要，更新Target-Network的参数。
6. 如果达到终止条件，则结束循环。

在训练过程中，epsilon值会逐渐减小，从而使得智能体在开始阶段更多的进行探索，然后逐渐转向利用已有知识。同时，也会定期更新Target-Network的参数，以提高算法的稳定性。