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DQN_tensorflow 源码解读_dqn开源代码
作者：小丑西瓜9 | 2024-03-23 15:06:01
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dqn开源代码
最近根据课题需要在研究《Playing Atari with Deep Reinforcement Learning》这篇论文，也就是DeepMind的最原始的算法，该论文对应的开源代码很多，这里以github上的一个开源代码[https://github.com/gliese581gg/DQN_tensorflow]为例，理解深度强化学习的具体训练学习过程，代码是基于tensorflow,opencv的，本人对代码进行了详细的注释，希望对大家有所帮助
- 1
Main函数，里面定义了deep_atari类，提供接口进行训练测试，其中params字典为网络进行相应的 具体看代码配置
from database import *
from emulator import *
import tensorflow as tf
import numpy as np
import time
from ale_python_interface import ALEInterface
import cv2
from scipy import misc
import gc #garbage colloector
import thread
gc.enable()
#给网络定义参数
params = {
'visualize' : True,
'network_type':'nips',
'ckpt_file':None,
'steps_per_epoch': 50000,
'num_epochs': 100,
'eval_freq':50000,
'steps_per_eval':10000,
'copy_freq' : 10000,
'disp_freq':10000,
'save_interval':10000,
'db_size': 1000000,
'batch': 32,
'num_act': 0,
'input_dims' : [210, 160, 3],
'input_dims_proc' : [84, 84, 4],
'learning_interval': 1,
'eps': 1.0,
'eps_step':1000000,
'eps_min' : 0.1,
'eps_eval' : 0.05,
'discount': 0.95,
'lr': 0.0002,
'rms_decay':0.99,
'rms_eps':1e-6,
'train_start':100,
'img_scale':255.0,
'clip_delta' : 0, #nature : 1
'gpu_fraction' : 0.25,
'batch_accumulator':'mean',
'record_eval' : True,
'only_eval' : 'n'
}
class deep_atari:
def __init__(self,params):
print 'Initializing Module...'
self.params = params
self.gpu_config = tf.ConfigProto(gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=self.params['gpu_fraction']))
self.sess = tf.Session(config=self.gpu_config)
self.DB = database(self.params)#初始化replay memory
self.engine = emulator(rom_name='breakout.bin', vis=self.params['visualize'],windowname=self.params['network_type']+'_preview')
self.params['num_act'] = len(self.engine.legal_actions)#确认该游戏，action的数量
self.build_net()#创建网络 qnet网络 和target网络
self.training = True
def build_net(self):
print 'Building QNet and targetnet...'
'''qnet表示训练网络，target表示测试的网络，整个模型可以理解为一个游戏的初学者，
在玩游戏的过程中，通过已有的经验的学会怎么玩游戏，在这里已有的经验为DB(replay memory)'''
self.qnet = DQN(self.params,'qnet')#定义qnet
self.targetnet = DQN(self.params,'targetnet')#定义targetnet
self.sess.run(tf.initialize_all_variables())
saver_dict = {'qw1':self.qnet.w1,'qb1':self.qnet.b1,
'qw2':self.qnet.w2,'qb2':self.qnet.b2,
'qw3':self.qnet.w3,'qb3':self.qnet.b3,
'qw4':self.qnet.w4,'qb4':self.qnet.b4,
'qw5':self.qnet.w5,'qb5':self.qnet.b5,
'tw1':self.targetnet.w1,'tb1':self.targetnet.b1,
'tw2':self.targetnet.w2,'tb2':self.targetnet.b2,
'tw3':self.targetnet.w3,'tb3':self.targetnet.b3,
'tw4':self.targetnet.w4,'tb4':self.targetnet.b4,
'tw5':self.targetnet.w5,'tb5':self.targetnet.b5,
'step':self.qnet.global_step}#需要保存的save_dict的权值和偏置值
self.saver = tf.train.Saver(saver_dict)
#self.saver = tf.train.Saver()
#复制qnet网络的权值和偏置值
self.cp_ops = [
self.targetnet.w1.assign(self.qnet.w1),self.targetnet.b1.assign(self.qnet.b1),
self.targetnet.w2.assign(self.qnet.w2),self.targetnet.b2.assign(self.qnet.b2),
self.targetnet.w3.assign(self.qnet.w3),self.targetnet.b3.assign(self.qnet.b3),
self.targetnet.w4.assign(self.qnet.w4),self.targetnet.b4.assign(self.qnet.b4),
self.targetnet.w5.assign(self.qnet.w5),self.targetnet.b5.assign(self.qnet.b5)]
self.sess.run(self.cp_ops)
if self.params['ckpt_file'] is not None:#恢复上一次训练的状态
print 'loading checkpoint : ' + self.params['ckpt_file']
self.saver.restore(self.sess,self.params['ckpt_file'])
temp_train_cnt = self.sess.run(self.qnet.global_step)
temp_step = temp_train_cnt * self.params['learning_interval']
print 'Continue from'
print ' -> Steps : ' + str(temp_step)
print ' -> Minibatch update : ' + str(temp_train_cnt)
def start(self):#网络开始学习和训练
self.reset_game()#开始一个新的游戏
self.step = 0#当前迭代次数
self.reset_statistics('all')#重置网络所有参数
self.train_cnt = self.sess.run(self.qnet.global_step)
#如果是恢复上一层训练的状态则读取相应的文件
if self.train_cnt > 0 :
self.step = self.train_cnt * self.params['learning_interval']
try:
self.log_train = open('log_training_'+self.params['network_type']+'.csv','a')
except:
self.log_train = open('log_training_'+self.params['network_type']+'.csv','w')
self.log_train.write('step,epoch,train_cnt,avg_reward,avg_q,epsilon,time\n')
try:
self.log_eval = open('log_eval_'+self.params['network_type']+'.csv','a')
except:
self.log_eval = open('log_eval_'+self.params['network_type']+'.csv','w')
self.log_eval.write('step,epoch,train_cnt,avg_reward,avg_q,epsilon,time\n')
else:
self.log_train = open('log_training_'+self.params['network_type']+'.csv','w')
self.log_train.write('step,epoch,train_cnt,avg_reward,avg_q,epsilon,time\n')
self.log_eval = open('log_eval_'+self.params['network_type']+'.csv','w')
self.log_eval.write('step,epoch,train_cnt,avg_reward,avg_q,epsilon,time\n')
self.s = time.time()
#输出网络状态
print self.params
print 'Start training!'
print 'Collecting replay memory for ' + str(self.params['train_start']) + ' steps'
#开始进行迭代，训练，其中，params['train_start']表示用随机的权值跑游戏，获取最初始的replay memory
while self.step < (self.params['steps_per_epoch'] * self.params['num_epochs'] * self.params['learning_interval'] + self.params['train_start']):
if self.training :
if self.DB.get_size() >= self.params['train_start'] : self.step += 1 ; self.steps_train += 1
else : self.step_eval += 1
#将上一次的状态，归一化之后的奖励值，以及所采取的action的索引,布尔类型的terminal 保存到DB里面（replay memory）
if self.state_gray_old is not None and self.training:
self.DB.insert(self.state_gray_old[26:110,:],self.reward_scaled,self.action_idx,self.terminal)
#每隔params['copy_freq']的迭代次数，将训练的qnet网络超参数，复制到target网络
if self.training and self.params['copy_freq'] > 0 and self.step % self.params['copy_freq'] == 0 and self.DB.get_size() > self.params['train_start']:
print '&&& Copying Qnet to targetnet\n'
self.sess.run(self.cp_ops)#???
#每隔params['learning_interval']的迭代次数权值更新一次，注意的是params['learning_interval']=1表明每次action之后都得训练一次
if self.training and self.step % self.params['learning_interval'] == 0 and self.DB.get_size() > self.params['train_start'] :
'''从DB(replay memory中)随机选取batch个状态序列，供网络进行学习训练，
具体的数据有状态s,动作a对应的索引,采取动作a之后的下一个状态，以及奖励值'''
bat_s,bat_a,bat_t,bat_n,bat_r = self.DB.get_batches()
bat_a = self.get_onehot(bat_a)#将action的索引值转换成一个稀疏矩阵，矩阵的行的大小表示batch_size,列的大小表示num_action,每行中对应的bat_a为1，其余为0
#将游戏的当前状态，通过targetnet，将输出的q_t当作当前状态下的最大未来奖励
if self.params['copy_freq'] > 0 :
feed_dict={self.targetnet.x: bat_n}
q_t = self.sess.run(self.targetnet.y,feed_dict=feed_dict)
else:
feed_dict={self.qnet.x: bat_n}
q_t = self.sess.run(self.qnet.y,feed_dict=feed_dict)
q_t = np.amax(q_t,axis=1)
#这里将随机取出来的状态序列（可以理解成为经验），喂入qnet网络
feed_dict={self.qnet.x: bat_s, self.qnet.q_t: q_t, self.qnet.actions: bat_a, self.qnet.terminals:bat_t, self.qnet.rewards: bat_r}
#通过之前定义的qnet计算损失函数
_,self.train_cnt,self.cost = self.sess.run([self.qnet.rmsprop,self.qnet.global_step,self.qnet.cost],feed_dict=feed_dict)
#累计损失函数的计算
self.total_cost_train += np.sqrt(self.cost)
self.train_cnt_for_disp += 1
if self.training :
self.params['eps'] = max(self.params['eps_min'],1.0 - float(self.train_cnt * self.params['learning_interval'])/float(self.params['eps_step']))
else:
self.params['eps'] = 0.05
#每隔self.params['save_interval']迭代，进行权值保存 相当于caffe里面的snapshot ,一下到228行都是一些辅助代码，显示输出啊，测试啊，等等，
if self.DB.get_size() > self.params['train_start'] and self.step % self.params['save_interval'] == 0 and self.training:
save_idx = self.train_cnt
self.saver.save(self.sess,'ckpt/model_'+self.params['network_type']+'_'+str(save_idx))
sys.stdout.write('$$$ Model saved : %s\n\n' % ('ckpt/model_'+self.params['network_type']+'_'+str(save_idx)))
sys.stdout.flush()
#输出显示
if self.training and self.step > 0 and self.step % self.params['disp_freq'] == 0 and self.DB.get_size() > self.params['train_start'] :
self.write_log_train()
#进行测试，这个时候相当于，只是用targetnet玩游戏，测试嘛。。qnet就被搁置了
if self.training and self.step > 0 and self.step % self.params['eval_freq'] == 0 and self.DB.get_size() > self.params['train_start'] :
self.reset_game()
if self.step % self.params['steps_per_epoch'] == 0 : self.reset_statistics('all')
else: self.reset_statistics('eval')
self.training = False
#TODO : add video recording
continue
#训练时，每self.params['steps_per_epoch']步，重新开始游戏，因为往后对权值更新的作用不大
#这里为解释一下，为什么会有这玩意，因为深度强化学习采用的是贝尔曼迭代法，有一个咖马参数，当当前游戏玩的步数比较多时，对当前的最大未来奖励就不是很大了，具体见论文
if self.training and self.step > 0 and self.step % self.params['steps_per_epoch'] == 0 and self.DB.get_size() > self.params['train_start']:
self.reset_game()
self.reset_statistics('all')
#self.training = False
continue
if not self.training and self.step_eval >= self.params['steps_per_eval'] :
self.write_log_eval()
self.reset_game()
self.reset_statistics('eval')
self.training = True
continue
#判断游戏是否over
if self.terminal :
self.reset_game()
if self.training :
self.num_epi_train += 1
self.total_reward_train += self.epi_reward_train
self.epi_reward_train = 0
else :
self.num_epi_eval += 1
self.total_reward_eval += self.epi_reward_eval
self.epi_reward_eval = 0
continue
'''这个函数表示选择下一步执行的action,select_action()这个函数采用了模拟退火算法的思想'''
self.action_idx,self.action, self.maxQ = self.select_action(self.state_proc)
#根据select_action函数获得的动作 执行，并且返回执行该动作后，所产生的状态，奖励，是否中断游戏等参数（构成马尔可夫链的一个新节点）
self.state, self.reward, self.terminal = self.engine.next(self.action)
self.reward_scaled = self.reward // max(1,abs(self.reward))#归一化奖励
if self.training : self.epi_reward_train += self.reward ; self.total_Q_train += self.maxQ#累计奖励
else : self.epi_reward_eval += self.reward ; self.total_Q_eval += self.maxQ
#以下程序段对新产生的状态进行处理，便于归入DB(replay memory,)
self.state_gray_old = np.copy(self.state_gray)
self.state_proc[:,:,0:3] = self.state_proc[:,:,1:4]
self.state_resized = cv2.resize(self.state,(84,110))
self.state_gray = cv2.cvtColor(self.state_resized, cv2.COLOR_BGR2GRAY)
self.state_proc[:,:,3] = self.state_gray[26:110,:]/self.params['img_scale']
#TODO : add video recording
def reset_game(self):
self.state_proc = np.zeros((84,84,4)); self.action = -1; self.terminal = False; self.reward = 0
self.state = self.engine.newGame()
self.state_resized = cv2.resize(self.state,(84,110))
self.state_gray = cv2.cvtColor(self.state_resized, cv2.COLOR_BGR2GRAY)
self.state_gray_old = None
self.state_proc[:,:,3] = self.state_gray[26:110,:]/self.params['img_scale']
def reset_statistics(self,mode):
if mode == 'all':
self.epi_reward_train = 0
self.epi_Q_train = 0
self.num_epi_train = 0
self.total_reward_train = 0
self.total_Q_train = 0
self.total_cost_train = 0
self.steps_train = 0
self.train_cnt_for_disp = 0
self.step_eval = 0
self.epi_reward_eval = 0
self.epi_Q_eval = 0
self.num_epi_eval = 0
self.total_reward_eval = 0
self.total_Q_eval = 0
def write_log_train(self):
sys.stdout.write('### Training (Step : %d , Minibatch update : %d , Epoch %d)\n' % (self.step,self.train_cnt,self.step//self.params['steps_per_epoch'] ))
sys.stdout.write(' Num.Episodes : %d , Avg.reward : %.3f , Avg.Q : %.3f, Avg.loss : %.3f\n' % (self.num_epi_train,float(self.total_reward_train)/max(1,self.num_epi_train),float(self.total_Q_train)/max(1,self.steps_train),self.total_cost_train/max(1,self.train_cnt_for_disp)))
sys.stdout.write(' Epsilon : %.3f , Elapsed time : %.1f\n\n' % (self.params['eps'],time.time()-self.s))
sys.stdout.flush()
self.log_train.write(str(self.step) + ',' + str(self.step//self.params['steps_per_epoch']) + ',' + str(self.train_cnt) + ',')
self.log_train.write(str(float(self.total_reward_train)/max(1,self.num_epi_train)) +','+ str(float(self.total_Q_train)/max(1,self.steps_train)) +',')
self.log_train.write(str(self.params['eps']) +','+ str(time.time()-self.s) + '\n')
self.log_train.flush()
def write_log_eval(self):
sys.stdout.write('@@@ Evaluation (Step : %d , Minibatch update : %d , Epoch %d)\n' % (self.step,self.train_cnt,self.step//self.params['steps_per_epoch'] ))
sys.stdout.write(' Num.Episodes : %d , Avg.reward : %.3f , Avg.Q : %.3f\n' % (self.num_epi_eval,float(self.total_reward_eval)/max(1,self.num_epi_eval),float(self.total_Q_eval)/max(1,self.params['steps_per_eval'])))
sys.stdout.write(' Epsilon : %.3f , Elapsed time : %.1f\n\n' % (self.params['eps'],time.time()-self.s))
sys.stdout.flush()
self.log_eval.write(str(self.step) + ',' + str(self.step//self.params['steps_per_epoch']) + ',' + str(self.train_cnt) + ',')
self.log_eval.write(str(float(self.total_reward_eval)/max(1,self.num_epi_eval)) +','+ str(float(self.total_Q_eval)/max(1,self.params['steps_per_eval'])) +',')
self.log_eval.write(str(self.params['eps']) +','+ str(time.time()-self.s) + '\n')
self.log_eval.flush()
def select_action(self,st):
if np.random.rand() > self.params['eps']:#产生随机数，若self.params['eps']小于该随机数，则用qnet决定下一步执行的动作，注意这里采用的是qnet
#greedy with random tie-breaking
Q_pred = self.sess.run(self.qnet.y, feed_dict = {self.qnet.x: np.reshape(st, (1,84,84,4))})[0]
a_winner = np.argwhere(Q_pred == np.amax(Q_pred))
if len(a_winner) > 1:
act_idx = a_winner[np.random.randint(0, len(a_winner))][0]
return act_idx,self.engine.legal_actions[act_idx], np.amax(Q_pred)
else:
act_idx = a_winner[0][0]
return act_idx,self.engine.legal_actions[act_idx], np.amax(Q_pred)
#若小于该随机数则随机产生一个动作进行执行
else:
#random
act_idx = np.random.randint(0,len(self.engine.legal_actions))
Q_pred = self.sess.run(self.qnet.y, feed_dict = {self.qnet.x: np.reshape(st, (1,84,84,4))})[0]
return act_idx,self.engine.legal_actions[act_idx], Q_pred[act_idx]
def get_onehot(self,actions):
actions_onehot = np.zeros((self.params['batch'], self.params['num_act']))
for i in range(self.params['batch']):
actions_onehot[i,actions[i]] = 1
return actions_onehot
if __name__ == "__main__":
dict_items = params.items()
for i in range(1,len(sys.argv),2):
if sys.argv[i] == '-weight' :params['ckpt_file'] = sys.argv[i+1]
elif sys.argv[i] == '-network_type' :params['network_type'] = sys.argv[i+1]
elif sys.argv[i] == '-visualize' :
if sys.argv[i+1] == 'y' : params['visualize'] = True
elif sys.argv[i+1] == 'n' : params['visualize'] = False
else:
print 'Invalid visualization argument!!! Available arguments are'
print ' y or n'
raise ValueError()
elif sys.argv[i] == '-gpu_fraction' : params['gpu_fraction'] = float(sys.argv[i+1])
elif sys.argv[i] == '-db_size' : params['db_size'] = int(sys.argv[i+1])
elif sys.argv[i] == '-only_eval' : params['only_eval'] = sys.argv[i+1]
else :
print 'Invalid arguments!!! Available arguments are'
print ' -weight (filename)'
print ' -network_type (nips or nature)'
print ' -visualize (y or n)'
print ' -gpu_fraction (0.1~0.9)'
print ' -db_size (integer)'
raise ValueError()
if params['network_type'] == 'nips':
from DQN_nips import *
elif params['network_type'] == 'nature':
from DQN_nature import *
params['steps_per_epoch']= 200000
params['eval_freq'] = 100000
params['steps_per_eval'] = 10000
params['copy_freq'] = 10000
params['disp_freq'] = 20000
params['save_interval'] = 20000
params['learning_interval'] = 1
params['discount'] = 0.99
params['lr'] = 0.00025
params['rms_decay'] = 0.95
params['rms_eps']=0.01
params['clip_delta'] = 1.0
params['train_start']=50000
params['batch_accumulator'] = 'sum'
params['eps_step'] = 1000000
params['num_epochs'] = 250
params['batch'] = 32
else :
print 'Invalid network type! Available network types are'
print ' nips or nature'
raise ValueError()
if params['only_eval'] == 'y' : only_eval = True
elif params['only_eval'] == 'n' : only_eval = False
else :
print 'Invalid only_eval option! Available options are'
print ' y or n'
raise ValueError()
if only_eval:
params['eval_freq'] = 1
params['train_start'] = 100
da = deep_atari(params)
da.start()
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database类 该类实现了论文里面的replay memory
import numpy as np
import gc
import time
import cv2
class database:
def __init__(self, params):
self.size = params['db_size']
self.img_scale = params['img_scale']
self.states = np.zeros([self.size,84,84],dtype='uint8') #image dimensions
self.actions = np.zeros(self.size,dtype='float32')
self.terminals = np.zeros(self.size,dtype='float32')
self.rewards = np.zeros(self.size,dtype='float32')
self.bat_size = params['batch']
self.bat_s = np.zeros([self.bat_size,84,84,4])
self.bat_a = np.zeros([self.bat_size])
self.bat_t = np.zeros([self.bat_size])
self.bat_n = np.zeros([self.bat_size,84,84,4])
self.bat_r = np.zeros([self.bat_size])
self.counter = 0 #keep track of next empty state
self.flag = False
return
def get_batches(self):#get random replay memory
for i in range(self.bat_size):#从replay memory提取，batch_size=32的序列数据
idx = 0
while idx < 3 or (idx > self.counter-2 and idx < self.counter+3):
idx = np.random.randint(3,self.get_size()-1)#get_size()返回的是当前replay memory的状态个数，随机选取
#以下是提取相应idx对应的值，并返回
self.bat_s[i] = np.transpose(self.states[idx-3:idx+1,:,:],(1,2,0))/self.img_scale
self.bat_n[i] = np.transpose(self.states[idx-2:idx+2,:,:],(1,2,0))/self.img_scale
self.bat_a[i] = self.actions[idx]
self.bat_t[i] = self.terminals[idx]
self.bat_r[i] = self.rewards[idx]
#self.bat_s[0] = np.transpose(self.states[10:14,:,:],(1,2,0))/self.img_scale
#self.bat_n[0] = np.transpose(self.states[11:15,:,:],(1,2,0))/self.img_scale
#self.bat_a[0] = self.actions[13]
#self.bat_t[0] = self.terminals[13]
#self.bat_r[0] = self.rewards[13]
return self.bat_s,self.bat_a,self.bat_t,self.bat_n,self.bat_r
def insert(self, prevstate_proc,reward,action,terminal):#更新马尔可夫链
self.states[self.counter] = prevstate_proc
self.rewards[self.counter] = reward
self.actions[self.counter] = action
self.terminals[self.counter] = terminal
#update counter
self.counter += 1
if self.counter >= self.size:
self.flag = True
self.counter = 0
return
def get_size(self):#返回当前replay 马尔可夫链的大小
if self.flag == False:
return self.counter
else:
return self.size
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DQN网络，该代码的核心部分，定义了网络结构，贝尔曼函数以及损失函数
import numpy as np
import tensorflow as tf
import cv2
class DQN:
def __init__(self,params,name):
# 用tensorflow为马尔可夫节点的各个元素分配内存空间，输入[32,84,84,4](注：params['batch']=32)
self.network_type = 'nature'
self.params = params
self.network_name = name
self.x = tf.placeholder('float32',[None,84,84,4],name=self.network_name + '_x')
self.q_t = tf.placeholder('float32',[None],name=self.network_name + '_q_t')
self.actions = tf.placeholder("float32", [None, params['num_act']],name=self.network_name + '_actions')
self.rewards = tf.placeholder("float32", [None],name=self.network_name + '_rewards')
self.terminals = tf.placeholder("float32", [None],name=self.network_name + '_terminals')
#conv1，[32,84,84,4]-->[32,w1,h1,32](卷积后的w,h，根据公式：h=(h+2*padding-stride)/stride+1,自行计算)
layer_name = 'conv1' ; size = 8 ; channels = 4 ; filters = 32 ; stride = 4
self.w1 = tf.Variable(tf.random_normal([size,size,channels,filters], stddev=0.01),name=self.network_name + '_'+layer_name+'_weights')
self.b1 = tf.Variable(tf.constant(0.1, shape=[filters]),name=self.network_name + '_'+layer_name+'_biases')
self.c1 = tf.nn.conv2d(self.x, self.w1, strides=[1, stride, stride, 1], padding='VALID',name=self.network_name + '_'+layer_name+'_convs')
self.o1 = tf.nn.relu(tf.add(self.c1,self.b1),name=self.network_name + '_'+layer_name+'_activations')
#self.n1 = tf.nn.lrn(self.o1, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75)
#conv2，[32,w1,h1,32]-->[32,w2,h2,64]
layer_name = 'conv2' ; size = 4 ; channels = 32 ; filters = 64 ; stride = 2
self.w2 = tf.Variable(tf.random_normal([size,size,channels,filters], stddev=0.01),name=self.network_name + '_'+layer_name+'_weights')
self.b2 = tf.Variable(tf.constant(0.1, shape=[filters]),name=self.network_name + '_'+layer_name+'_biases')
self.c2 = tf.nn.conv2d(self.o1, self.w2, strides=[1, stride, stride, 1], padding='VALID',name=self.network_name + '_'+layer_name+'_convs')
self.o2 = tf.nn.relu(tf.add(self.c2,self.b2),name=self.network_name + '_'+layer_name+'_activations')
#self.n2 = tf.nn.lrn(self.o2, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75)
#conv3,[32,w2,h2,64]-->[32,w3,h3,64]
layer_name = 'conv3' ; size = 3 ; channels = 64 ; filters = 64 ; stride = 1
self.w3 = tf.Variable(tf.random_normal([size,size,channels,filters], stddev=0.01),name=self.network_name + '_'+layer_name+'_weights')
self.b3 = tf.Variable(tf.constant(0.1, shape=[filters]),name=self.network_name + '_'+layer_name+'_biases')
self.c3 = tf.nn.conv2d(self.o2, self.w3, strides=[1, stride, stride, 1], padding='VALID',name=self.network_name + '_'+layer_name+'_convs')
self.o3 = tf.nn.relu(tf.add(self.c3,self.b3),name=self.network_name + '_'+layer_name+'_activations')
#self.n2 = tf.nn.lrn(self.o2, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75)
#flat,将矩阵压缩成向量
o3_shape = self.o3.get_shape().as_list()
#fc3，[32,w3*h3*64]-->[32,512]
layer_name = 'fc4' ; hiddens = 512 ; dim = o3_shape[1]*o3_shape[2]*o3_shape[3]
self.o3_flat = tf.reshape(self.o3, [-1,dim],name=self.network_name + '_'+layer_name+'_input_flat')
self.w4 = tf.Variable(tf.random_normal([dim,hiddens], stddev=0.01),name=self.network_name + '_'+layer_name+'_weights')
self.b4 = tf.Variable(tf.constant(0.1, shape=[hiddens]),name=self.network_name + '_'+layer_name+'_biases')
self.ip4 = tf.add(tf.matmul(self.o3_flat,self.w4),self.b4,name=self.network_name + '_'+layer_name+'_ips')
self.o4 = tf.nn.relu(self.ip4,name=self.network_name + '_'+layer_name+'_activations')
#fc4,[32,512]-->[32,number_act]
layer_name = 'fc5' ; hiddens = params['num_act'] ; dim = 512
self.w5 = tf.Variable(tf.random_normal([dim,hiddens], stddev=0.01),name=self.network_name + '_'+layer_name+'_weights')
self.b5 = tf.Variable(tf.constant(0.1, shape=[hiddens]),name=self.network_name + '_'+layer_name+'_biases')
''' 在这里说一说，deep-Q-Learning的核心思想之一，就是在玩游戏时，使下一步的最大未来奖励最大，但是在实际过程，
不可能从未来向现在求累加，于是就用了贝尔曼迭代公式，就是假设下一个状态的最大未来奖励已经求解出来了，
这样就可以通过target神经网络求出该状态下的最大未来奖励（这个值做为qnet神经网络训练样本的期望值，但是,把它叫做label），
这种思想，类似于算法里面的递归，数学里面的归纳法，好了，现在再来说说深度神经网络的作用，它的输入就是当前状态（是一个连续的4张图像），
输出是下一状态下，不同action对应的最大未来收益,选取最大的输出值，就是该状态下的最大未来收益值，也就是神经网络的预测值，这里有意思的是期望值和预测值都是通过神经网络
求出来的（两个神经网络），训练的过程就和正常卷积神经网络的也就一样了，选择损失函数，误差反向传递，更新权值'''
self.y = tf.add(tf.matmul(self.o4,self.w5),self.b5,name=self.network_name + '_'+layer_name+'_outputs')
#Q,Cost,Optimizer
self.discount = tf.constant(self.params['discount'])#贝尔曼迭代公式的咖马
#贝尔曼迭代公式，计算出来的结果叫做打折后的的最大未来奖励
self.yj = tf.add(self.rewards, tf.mul(1.0-self.terminals, tf.mul(self.discount, self.q_t)))
self.Qxa = tf.mul(self.y,self.actions)
self.Q_pred = tf.reduce_max(self.Qxa, reduction_indices=1)
#self.yjr = tf.reshape(self.yj,(-1,1))
#self.yjtile = tf.concat(1,[self.yjr,self.yjr,self.yjr,self.yjr])
#self.yjax = tf.mul(self.yjtile,self.actions)
#half = tf.constant(0.5)
self.diff = tf.sub(self.yj, self.Q_pred)
if self.params['clip_delta'] > 0 :
self.quadratic_part = tf.minimum(tf.abs(self.diff), tf.constant(self.params['clip_delta']))###?????
self.linear_part = tf.sub(tf.abs(self.diff),self.quadratic_part)
self.diff_square = 0.5 * tf.pow(self.quadratic_part,2) + self.params['clip_delta']*self.linear_part
else:
self.diff_square = tf.mul(tf.constant(0.5),tf.pow(self.diff, 2))
if self.params['batch_accumulator'] == 'sum':
self.cost = tf.reduce_sum(self.diff_square)
else:
self.cost = tf.reduce_mean(self.diff_square)
self.global_step = tf.Variable(0, name='global_step', trainable=False)
self.rmsprop = tf.train.RMSPropOptimizer(self.params['lr'],self.params['rms_decay'],0.0,self.params['rms_eps']).minimize(self.cost,global_step=self.global_step)- 1
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emulator类 定义了atari的游戏接口，可以获取当前的状态（图像），reward，重置游戏函数，新建游戏函数
import numpy as np
import copy
import sys
from ale_python_interface import ALEInterface
import cv2
import time
#import scipy.misc
class emulator:
def __init__(self, rom_name, vis,windowname='preview'):
self.ale = ALEInterface()
self.max_frames_per_episode = self.ale.getInt("max_num_frames_per_episode");
self.ale.setInt("random_seed",123)
self.ale.setInt("frame_skip",4)
self.ale.loadROM('roms/' + rom_name )
self.legal_actions = self.ale.getMinimalActionSet()
self.action_map = dict()
self.windowname = windowname
for i in range(len(self.legal_actions)):
self.action_map[self.legal_actions[i]] = i
# print(self.legal_actions)
self.screen_width,self.screen_height = self.ale.getScreenDims()
print("width/height: " +str(self.screen_width) + "/" + str(self.screen_height))
self.vis = vis
if vis:
cv2.startWindowThread()
cv2.namedWindow(self.windowname)
def get_image(self):#读取画面的图像
numpy_surface = np.zeros(self.screen_height*self.screen_width*3, dtype=np.uint8)
self.ale.getScreenRGB(numpy_surface)
image = np.reshape(numpy_surface, (self.screen_height, self.screen_width, 3))
return image
def newGame(self):
self.ale.reset_game()#开始新的游戏
return self.get_image()#得到初始的游戏画面
def next(self, action_indx):
reward = self.ale.act(action_indx)
nextstate = self.get_image()
# scipy.misc.imsave('test.png',nextstate)
if self.vis:
cv2.imshow(self.windowname,nextstate)
return nextstate, reward, self.ale.game_over()
if __name__ == "__main__":
engine = emulator('breakout.bin',True)
engine.next(0)
time.sleep(5)
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论文链接：https://arxiv.org/pdf/1312.5602.pdf
代码链接：https://github.com/gliese581gg/DQN_tensorflow
晚点附上，这篇论文的论文笔记
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