概述
参考网上的视频教学：https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/4-3-DQN3/
DQN虽然简单，但是实现起来其实也没有能够一通到底的通透，有时候回忆一下又要很长时间，所以参照网上的教学视频，把代码加了很多注释记录一下，方便今后快速回忆。
注释写的可以说相当详细了，甚至很多是冗余的。这个无所谓，不是工业生产环境，怎么回忆的方便怎么来，所以也会显得凌乱，并且老鸟看起来会觉得很啰嗦，so，适合新手。
"""
Deep Q network,
Using:
Tensorflow: 1.0
gym: 0.7.3
"""
import gym
from RL_brain import DeepQNetwork
import matplotlib.pyplot as plt
env = gym.make('CartPole-v0')
env = env.unwrapped
print("env.action_space: ", env.action_space) # Discrete(2)
print("env.action_space.n :", env.action_space.n) # 2
print("env.observation_space: ", env.observation_space) # Box(4,)
print("env.observation_space.shape:", env.observation_space.shape) # (4,)
print("env.observation_space.high: ", env.observation_space.high) # [4.8000002e+00 3.4028235e+38 4.1887903e-01 3.4028235e+38]
print("env.observation_space.low: ", env.observation_space.low) # [-4.8000002e+00 -3.4028235e+38 -4.1887903e-01 -3.4028235e+38]
RL = DeepQNetwork(n_actions=env.action_space.n, # 2
n_features=env.observation_space.shape[0], # 4
learning_rate=0.01,
e_greedy=0.9,
replace_target_iter=100,
memory_size=2000,
e_greedy_increment=0.001,)
START_POINT = 1000 # 从START_POINT次互动开始训练网络
total_steps = 0 # 记录总的互动次数（从第一个episode开始计数，跨域episode也可以）
epi_r_record=[] # 记录每个episode的总奖励值
epi_step_record = [] # 记录每个episode的总步数
for i_episode in range(300): # 100个episode
observation = env.reset() # observation是一个np.ndarray,如：[-0.02081334 -0.02731451 0.02365333 -0.04925076]
epi_r = 0 # 每个episode初始奖励为0
epi_step = 0 # episode运行的步数计数器
# 下面的每次循环：
# 1.根据初始ob选择action
# 2.action与环境互动，产生数据，并存储数据
# 3.做两个判断：①超过START_POINT步后，开始训练；②如果episode结束，print出信息，并结束该episode
while True:
# env.render() # 渲染输出画面，注释掉则不显示画面，但是可以大大加快仿真速度
action = RL.choose_action(observation)
observation_, reward, done, info = env.step(action)
# the smaller theta and closer to center the better
x, x_dot, theta, theta_dot = observation_
######################################################
#### x, / *cart position, meters * / #####
#### x_dot, / *cart velocity * / #####
#### theta, / *pole angle, radians * / #####
#### theta_dot; / *pole angular velocity * / #####
######################################################
# x_threshold = 2.4
# theta_threshold_radians = 12 * 2 * math.pi / 360 即：12°对应的弧度0.209
r1 = (env.x_threshold - abs(x))/env.x_threshold - 0.8 # 0.8是一个偏置，abs(x)=2.4时，r1=-0.8,abs(x)=0时，r1=0.2
r2 = (env.theta_threshold_radians - abs(theta))/env.theta_threshold_radians - 0.5 # 同上，# 0.5是一个偏置，r2=(-0.5,0.5)
reward = r1 + r2 # 若出现redeclared 'xxxxx' defined above without usage，可能是误报
RL.store_transition(observation, action, reward, observation_)
epi_r += reward
if total_steps > START_POINT:
RL.learn()
if done:
print('episode: ', i_episode,
'epi_r: ', round(epi_r, 2), # round(x,y)浮点数x保留y位小数
' epsilon: ', round(RL.epsilon, 2))
epi_r_record.append(epi_r) # 记录每个episode奖励值
epi_step_record.append(epi_step) # 记录每个回合能够运行的step数
epi_step = 0 # 每个episode结束后，计数器置0
break
observation = observation_
epi_step += 1
total_steps += 1
plt.figure(1)
plt.plot(epi_r_record)
plt.figure(2)
print(epi_step_record)
plt.plot(epi_step_record)
plt.show()
#RL.plot_cost()
"""
This part of code is the DQN brain, which is a brain of the agent.
All decisions are made in here.
Using Tensorflow to build the neural network.
View more on my tutorial page: https://morvanzhou.github.io/tutorials/
Using:
Tensorflow: 1.0
gym: 0.8.0
"""
import numpy as np
import pandas as pd
import tensorflow as tf
# Deep Q Network off-policy
class DeepQNetwork:
def __init__(
self,
n_actions, # 2
n_features, # 4
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)) # 2个s，1个a，1个r
# 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')
# 以下两行是第一次运行时出现的结果
# print('t_params', t_params) # t_params [<tf.Variable 'target_net/l1/w1:0' shape=(4, 10) dtype=float32_ref>, <tf.Variable 'target_net/l1/b1:0' shape=(1, 10) dtype=float32_ref>, <tf.Variable 'target_net/l2/w2:0' shape=(10, 2) dtype=float32_ref>, <tf.Variable 'target_net/l2/b2:0' shape=(1, 2) dtype=float32_ref>]
# t_params是一个长度为4的list，
# ['target_net/l1/w1:0' shape=(4, 10),
# 'target_net/l1/b1:0' shape=(1, 10),
# 'target_net/l2/w2:0' shape=(10, 2),
# 'target_net/l2/b2:0' shape=(1, 2)]
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):
'''
建立2个网络，
:return:
'''
# ------------------ 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,只是一个自定义的列表而已
# 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_eval = tf.matmul(l1, w2) + b2 # shape=[?,2]
# q_eval是评估网络的输出，数据传输关系是q_eval→l1→s，所以feed_dict的第二个键名是s
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 ------------------
self.s_ = tf.placeholder(tf.float32, [None, self.n_features], name='s_') # input
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 # 输出数据类型是Tensor
# q_next是目标网络的输出，数据传输关系是q_next→l1→s_，所以feed_dict的第一个键名是s_
def store_transition(self, s, a, r, s_): # 每个episode的每个step，都会调用该函数
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 # 取余
self.memory[index, :] = transition # self.memory = np.zeros((self.memory_size, n_features * 2 + 2)) # 2个s，1个a，1个r
self.memory_counter += 1
def choose_action(self, observation): # observation.shape=(4,)
# to have batch dimension when feed into tf placeholder
observation = observation[np.newaxis, :] # observation.shape=(1,4)
if np.random.uniform() < self.epsilon: # self.epsilon = 0 if e_greedy_increment is not None else self.epsilon_max
# forward feed the observation and get q value for every actions
actions_value = self.sess.run(self.q_eval, feed_dict={self.s: observation}) # [[2.1524513 2.2836583]]
action = np.argmax(actions_value) # 从形如[[2.1524513 2.2836583]]中选取大的那个数的索引,即[0,2)
else:
action = np.random.randint(0, self.n_actions) # 2,即从[0,2)中随机选取整数
# print(type(action))
# action的数据类型，有时候是int，有时候是numpy.int64，程序还不够严谨，需要改善
return action # 0或者1
def learn(self): # if total_steps > START_POINT:就执行该函数
# 检查是否需要更新目标网络参数
# self.learn_step_counter在该函数末尾self.learn_step_counter += 1
if self.learn_step_counter % self.replace_target_iter == 0: # 取余，按self.replace_target_iter整数倍更换target net的参数
self.sess.run(self.replace_target_op) # self.replace_target_op = [tf.assign(t, e) for t, e in zip(t_params, e_params)]
print('target_params_replaced，第', self.learn_step_counter / self.replace_target_iter, '次')
# sample batch memory from all memory
if self.memory_counter > self.memory_size: # memory_counter在store_transition中，每个step都会增加1
# 在[0, self.memory_size)数组中，抽取size为batch_size的数组作为索引
sample_index = np.random.choice(self.memory_size, size=self.batch_size) # 原文代码中是没有replace参数的，这是一种可重复抽样，个人感觉应该是不可重复抽样比较好些，为啥我也说不清，要实现不重复抽样，应该添加参数replace=False
else:
# 在[0, memory_counter)数组中，抽取size为batch_size的数组作为索引
sample_index = np.random.choice(self.memory_counter, size=self.batch_size) # 同上，
# memory.shape=(xxx,yyy)，从中按索引sample_index抽取若干行，结果batch_memory的shape的维度不变，还是2
# batch_memory.shape=(32,10)
batch_memory = self.memory[sample_index, :] # self.memory = np.zeros((self.memory_size, n_features * 2 + 2)) # 2个s，1个a，1个r
q_next, q_eval = self.sess.run( #type(q_next)=ndarray, q_next.shape (32, 2), 32是batch_size
# q_next是目标网络的输出，数据传输关系是q_next→l1→s_，所以feed_dict的第一个键名是s_
# q_eval是评估网络的输出，数据传输关系是q_eval→l1→s，所以feed_dict的第二个键名是s
[self.q_next, self.q_eval], # 分别是2个网络输出，数据类型是Tensor
feed_dict={
# 记忆库的顺序是(s, a, r, s_) store_transition(self, s, a, r, s_):
# n_features=4。[:, -self.n_features:]意思是所有行的 [倒数第4个元素, 最后一个元素]
# n_features=4。[:, :self.n_features]意思是所有行的 [第1个元素, 第4个元素]
self.s_: batch_memory[:, -self.n_features:], # fixed params。 feed_dict键名是tf.placeholder:键值是numpy ndarray
self.s: batch_memory[:, :self.n_features], # newest params
})
# change q_target w.r.t q_eval's action 这部分代码解释见下面注释
q_target = q_eval.copy() # q_eval的数据类型是ndarray，该操作可以理解为深复制，也即c语言传统意义上的复制一个值
batch_index = np.arange(self.batch_size, dtype=np.int32) # [0, 1, 2, 3,...,32]的一维数组
# [:, self.n_features]是所有行的第4个元素，也即action
# 不加astype(int)时，输出的是浮点型数据
# 以下两行代码，实际上是把batche_memory中的action和reward抽取了出来，形成一个一维数组（32，）
eval_act_index = batch_memory[:, self.n_features].astype(int) # batch_memory.shape=(32,10) # eval_act_index.shape (32,) 由0和1组成
reward = batch_memory[:, self.n_features + 1]
# 这一句是算法的关键
# q_target.shape=(32,2)
# batch_index.shape=(32,)
# eval_act_index.shape=(32,)
# q_next.shape=(32,2)
# np.max(q_next, axis=1).shape=(32,)
q_target[batch_index, eval_act_index] = reward + self.gamma * np.max(q_next, axis=1)
'''
上面几句代码的原理，要从网络结构和DQN算法来解释。
由简至繁解释一下
①最简单的情况下，网络输入为state和action，输出为Q(s,a)/Q(s_,a)
如Q(s,a)=1.1，maxQ(s_,a)=3.2，r=0.9
则根据DQN的伪代码，target是0.9+3.2=4.1
网络更新，就是要更新Q(s,a)=1.1向4.1靠近
②更改网络结构，网络输入为state，输出为Q(s,a1)，Q(s,a2)等
如q_eval输出为[1.2, 5,8]，并且记忆库告诉我们，选择的action是a2（若是e-greedy起作用，也可能选择a1)，q_next输出为[4.6, 1.9]，r=0.9
则根据DQN的伪代码，target是0.9+4.6=5.5
网络更新，就是要更新Q(s,a2)=5.8向5.5靠近，而Q(s,a1)是不！需！要！动的。所以，写成矩阵形式，target就是[1.2, 5.5]
试想一下，如果上面的描述action换成是a1，其他不变，那么target就是[5.5, 5.8]
这就是上边代码为何要如此操作的原因——仅仅改变需要改变的Q(s,a)，更换为target，其余的不变。
下面的是原始代码的解释：
'''
"""
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
# _train_op数据传输关系是_train_op→loss→(q_target和q_eval)
# q_eval是评估网络的输出，数据传输关系是q_eval→l1→s
_, 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()
最后
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