一、原理

DQN为融合了神经网络和Q-learning的方法。

面对复杂问题，state数量巨多，传统的表格学习已经不能满足此种情况。神经网络的的工作模式为通过对输入进行处理学习得到结果的过程。神经网络应用到强化学习中时，输入为状态和动作，价值作为其输出，或者输入为状态，输出为最大值的动作，省略了需要用表格记录动作及状态的过程，可更好的应用于复杂状态下的处理。

DQN中还有两种机理用于提升。一种为Experience replay（经验回放），随机对之前的经历进行学习，使其更新更有效率。Fixed Q-targets 也是一种打乱相关性的机理。

二、代码实现

建立一个数据库和一个暂时冻结的q_target参数，需要建立两个不通过的神经网络。两个神经网络一个有最新的参数，一个老的参数。

import numpy as np

import pandas as pd

import tensorflow as tf

np.random.seed(1)

tf.set_random_seed(1)

Deep Q Network off-policy

class DeepQNetwork:

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,

output_graph=True,

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

total learning step

self.learn_step_counter = 0

initialize zero memory [s, a, r, s_]

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

consist of [target_net, evaluate_net]

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()

if output_graph:

$ tensorboard –logdir=logs

tf.train.SummaryWriter soon be deprecated, use following

tf.summary.FileWriter(“logs/”, self.sess.graph)

self.sess.run(tf.global_variables_initializer())

self.cost_his = []

def _build_net(self):

—————— build evaluate_net ——————

self.s = tf.placeholder(tf.float32, [None, self.n_features], name=’s’) # input

self.q_target = tf.placeholder(tf.float32, [None, self.n_actions], name=’Q_target’) # for calculating loss

with tf.variable_scope(‘eval_net’):

c_names(collections_names) are the collections to store variables

c_names, n_l1, w_initializer, b_initializer = \

[‘eval_net_params’, tf.GraphKeys.GLOBAL_VARIABLES], 10, \

tf.random_normal_initializer(0., 0.3), tf.constant_initializer(0.1) # config of layers

first layer. collections is used later when assign to target net

建立了两层神经网络l1和l2

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)

second layer. collections is used later when assign to target net

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)

—————— build target_net ——————

with tf.variable_scope(‘target_net’):

c_names(collections_names) are the collections to store variables

c_names = [‘target_net_params’, tf.GraphKeys.GLOBAL_VARIABLES]

first layer. collections is used later when assign to target net

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)

second layer. collections is used later when assign to target net

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

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_))

replace the old memory with new memory

index = self.memory_counter % self.memory_size

self.memory[index, :] = transition

self.memory_counter += 1

def choose_action(self, observation):

to have batch dimension when feed into tf placeholder

observation = observation[np.newaxis, :]

if np.random.uniform() < self.epsilon:

forward feed the observation and get q value for every actions

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

def learn(self):

check to replace target parameters

if self.learn_step_counter % self.replace_target_iter == 0:

self.sess.run(self.replace_target_op)

print(‘\ntarget_params_replaced\n’)

sample batch memory from all memory

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_eval = self.sess.run(

[self.q_next, self.q_eval],

feed_dict={

self.s_: batch_memory[:, -self.n_features:], # fixed params

self.s: batch_memory[:, :self.n_features], # newest params

change q_target w.r.t q_eval’s action

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)

“””

For example in this batch I have 2 samples and 3 actions:

q_eval =

[[1, 2, 3],

[4, 5, 6]]

q_target = q_eval =

[[1, 2, 3],

[4, 5, 6]]

Then change q_target with the real q_target value w.r.t the q_eval’s action.

For example in:

sample 0, I took action 0, and the max q_target value is -1;

sample 1, I took action 2, and the max q_target value is -2:

q_target =

[[-1, 2, 3],

[4, 5, -2]]

So the (q_target – q_eval) becomes:

[[(-1)-(1), 0, 0],

[0, 0, (-2)-(6)]]

We then backpropagate this error w.r.t the corresponding action to network,

leave other action as error=0 cause we didn’t choose it.

“””

train eval network

_, 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)

increasing epsilon

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

self.learn_step_counter += 1

def plot_cost(self):

import matplotlib.pyplot as plt

plt.plot(np.arange(len(self.cost_his)), self.cost_his)

plt.ylabel(‘Cost’)

plt.xlabel(‘training steps’)

plt.show()

2.算法更新

from maze_env import Maze

from RL_brain import DeepQNetwork

def run_maze():

step = 0

for episode in range(300):

initial observation

observation = env.reset()

while True:

fresh env

env.render()

RL choose action based on observation

action = RL.choose_action(observation)

RL take action and get next observation and reward

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

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

if (step > 200) and (step % 5 == 0):#当记忆库有了一定的数量再进行学习，过了200没5步学习一次

RL.learn()

swap observation

observation = observation_

break while loop when end of this episode

if done:

break

step += 1

end of game

print(‘game over’)

env.destroy()

if name == “main“:

maze game

env = Maze()

RL = DeepQNetwork(env.n_actions, env.n_features,

learning_rate=0.01,

reward_decay=0.9,

e_greedy=0.9,

replace_target_iter=200,

memory_size=2000,

output_graph=True

env.after(100, run_maze)

env.mainloop()

RL.plot_cost()

转自morvan Zhou

Original: https://blog.csdn.net/m0_66111915/article/details/122716797
Author: cc街道办事处
Title: 【强化学习】DQN（Deep Q network）原理及实现