DQN(Deep Q-Learning)

DQN

q-learning存在一个问题，真实情况的state可能无穷多，这样q-table就会无限大，解决这个问题的办法是通过神经网络实现q-table。输入state，输出不同action的q-value。

1. q-leaning的执行过程:输出state,根据Q-Table输出不同action的q-value,根据探索利用策略,获取当前状态下采取的动作a.
   
2. DQN的执行过程:输出state,根据Network输出不同action的q-value,根据探索利用策略,获取当前状态下采取的动作a.
   

DQN的架构

1. 通过记忆库,降低观测序列之间的相关性(强化学习由于state之间的相关性存在稳定性的问题，会极大的改变策略进而改变数据分布,解决的办法是在训练的时候存储当前训练的状态到记忆体M，更新参数的时候随机从M中抽样mini-batch进行更新.M 中存储的数据类型为 ，M有最大长度的限制，以保证更新采用的数据都是最近的数据。)
2. 通过Q-target的周期性固定,降低Q-target之间的相关性
   基本架构:
   

随机抽取记忆库中的数据进行学习，打乱了经历之间的相关性，使得神经网络更新更有效率，Fixed Q-targets 使得target_net能够延迟更新参数从而也打乱了相关性。

• DQN中有两个神经网络(NN)一个参数相对固定的网络，我们叫做target-net，用来获取Q-目标(Q-target)的数值, 另外一个叫做eval_net用来获取Q-评估(Q-eval)的数值。
• 我们在训练神经网络参数时用到的损失函数(Loss function)，实际上就是q_target 减 q_eval的结果 (loss = q_target- q_eval )。
• 反向传播真正训练的网络是只有一个，就是eval_net。target_net 只做正向传播得到q_target (q_target = r +γ*max Q(s,a)). 其中 Q(s,a)是若干个经过target-net正向传播的结果。
• 训练的数据是从记忆库中随机提取的，记忆库记录着每一个状态下的行动，奖励，和下一个状态的结果(s, a, r, s’)。记忆库的大小有限，当记录满了数据之后，下一个数据会覆盖记忆库中的第一个数据，记忆库就是这样覆盖更新的。
• q_target的网络target_net也会定期更新一下参数，由于target_net和eval_net的结构是一样的。更新q_target网络的参数就是直接将q_eval 的参数复制过来就行了。

DQN的算法

# import lib
import gym
import tensorflow as tf
import numpy as np

# Create the Cart-Pole game environment
env = gym.make('CartPole-v0')

# Q-network
class QNetwork:
    def __init__(self, learning_rate=0.01, state_size=4, 
                 action_size=2, hidden_size=10, 
                 name='QNetwork'):
        # state inputs to the Q-network
        with tf.variable_scope(name):
            self.inputs_ = tf.placeholder(tf.float32, [None, state_size], name='inputs')

            # One hot encode the actions to later choose the Q-value for the action
            self.actions_ = tf.placeholder(tf.int32, [None], name='actions')
            one_hot_actions = tf.one_hot(self.actions_, action_size)

            # Target Q values for training
            self.targetQs_ = tf.placeholder(tf.float32, [None], name='target')

            # ReLU hidden layers
            self.fc1 = tf.contrib.layers.fully_connected(self.inputs_, hidden_size)
            self.fc2 = tf.contrib.layers.fully_connected(self.fc1, hidden_size)

            # Linear output layer
            self.output = tf.contrib.layers.fully_connected(self.fc2, action_size, 
                                                            activation_fn=None)

            ### Train with loss (targetQ - Q)^2
            # output has length 2, for two actions. This next line chooses
            # one value from output (per row) according to the one-hot encoded actions.
            self.Q = tf.reduce_sum(tf.multiply(self.output, one_hot_actions), axis=1)

            self.loss = tf.reduce_mean(tf.square(self.targetQs_ - self.Q))
            self.opt = tf.train.AdamOptimizer(learning_rate).minimize(self.loss)

# Experience replay
from collections import deque
class Memory():
    def __init__(self, max_size = 1000):
        self.buffer = deque(maxlen=max_size)

    def add(self, experience):
        self.buffer.append(experience)

    def sample(self, batch_size):
        idx = np.random.choice(np.arange(len(self.buffer)), 
                               size=batch_size, 
                               replace=False)
        return [self.buffer[ii] for ii in idx]

# hyperparameters
train_episodes = 1000          # max number of episodes to learn from
max_steps = 200                # max steps in an episode
gamma = 0.99                   # future reward discount

# Exploration parameters
explore_start = 1.0            # exploration probability at start
explore_stop = 0.01            # minimum exploration probability 
decay_rate = 0.0001            # exponential decay rate for exploration prob

# Network parameters
hidden_size = 64               # number of units in each Q-network hidden layer
learning_rate = 0.0001         # Q-network learning rate

# Memory parameters
memory_size = 10000            # memory capacity
batch_size = 20                # experience mini-batch size
pretrain_length = batch_size   # number experiences to pretrain the memory

tf.reset_default_graph()
mainQN = QNetwork(name='main', hidden_size=hidden_size, learning_rate=learning_rate)

# Populate the experience memory
# Initialize the simulation
env.reset()
# Take one random step to get the pole and cart moving
state, reward, done, _ = env.step(env.action_space.sample())

memory = Memory(max_size=memory_size)

# Make a bunch of random actions and store the experiences
for ii in range(pretrain_length):
    # Uncomment the line below to watch the simulation
    # env.render()

    # Make a random action
    action = env.action_space.sample()
    next_state, reward, done, _ = env.step(action)

    if done:
        # The simulation fails so no next state
        next_state = np.zeros(state.shape)
        # Add experience to memory
        memory.add((state, action, reward, next_state))

        # Start new episode
        env.reset()
        # Take one random step to get the pole and cart moving
        state, reward, done, _ = env.step(env.action_space.sample())
    else:
        # Add experience to memory
        memory.add((state, action, reward, next_state))
        state = next_state

# Training
# Now train with experiences
saver = tf.train.Saver()
rewards_list = []
with tf.Session() as sess:
    # Initialize variables
    sess.run(tf.global_variables_initializer())

    step = 0
    for ep in range(1, train_episodes):
        total_reward = 0
        t = 0
        while t < max_steps:
            step += 1
            # Uncomment this next line to watch the training
            env.render() 

            # Explore or Exploit
            explore_p = explore_stop + (explore_start - explore_stop)*np.exp(-decay_rate*step) 
            if explore_p > np.random.rand():
                # Make a random action
                action = env.action_space.sample()
            else:
                # Get action from Q-network
                feed = {mainQN.inputs_: state.reshape((1, *state.shape))}
                Qs = sess.run(mainQN.output, feed_dict=feed)
                action = np.argmax(Qs)

            # Take action, get new state and reward
            next_state, reward, done, _ = env.step(action)

            total_reward += reward

            if done:
                # the episode ends so no next state
                next_state = np.zeros(state.shape)
                t = max_steps

                print('Episode: {}'.format(ep),
                      'Total reward: {}'.format(total_reward),
                      'Training loss: {:.4f}'.format(loss),
                      'Explore P: {:.4f}'.format(explore_p))
                rewards_list.append((ep, total_reward))

                # Add experience to memory
                memory.add((state, action, reward, next_state))

                # Start new episode
                env.reset()
                # Take one random step to get the pole and cart moving
                state, reward, done, _ = env.step(env.action_space.sample())

            else:
                # Add experience to memory
                memory.add((state, action, reward, next_state))
                state = next_state
                t += 1

            # Sample mini-batch from memory
            batch = memory.sample(batch_size)
            states = np.array([each[0] for each in batch])
            actions = np.array([each[1] for each in batch])
            rewards = np.array([each[2] for each in batch])
            next_states = np.array([each[3] for each in batch])

            # Train network
            target_Qs = sess.run(mainQN.output, feed_dict={mainQN.inputs_: next_states})

            # Set target_Qs to 0 for states where episode ends
            episode_ends = (next_states == np.zeros(states[0].shape)).all(axis=1)
            target_Qs[episode_ends] = (0, 0)

            targets = rewards + gamma * np.max(target_Qs, axis=1)

            loss, _ = sess.run([mainQN.loss, mainQN.opt],
                                feed_dict={mainQN.inputs_: states,
                                           mainQN.targetQs_: targets,
                                           mainQN.actions_: actions})

    saver.save(sess, "checkpoints/cartpole.ckpt")

# Testing
test_episodes = 10
test_max_steps = 400
env.reset()
with tf.Session() as sess:
    saver.restore(sess, tf.train.latest_checkpoint('checkpoints'))

    for ep in range(1, test_episodes):
        t = 0
        while t < test_max_steps:
            env.render() 

            # Get action from Q-network
            feed = {mainQN.inputs_: state.reshape((1, *state.shape))}
            Qs = sess.run(mainQN.output, feed_dict=feed)
            action = np.argmax(Qs)

            # Take action, get new state and reward
            next_state, reward, done, _ = env.step(action)

            if done:
                t = test_max_steps
                env.reset()
                # Take one random step to get the pole and cart moving
                state, reward, done, _ = env.step(env.action_space.sample())

            else:
                state = next_state
                t += 1

env.close()