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main_multithread.py
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main_multithread.py
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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()
params = {
'visualize' : True,
'network_type':'nips',
'ckpt_file':None,
'steps_per_epoch': 50000,
'num_epochs': 250,
'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' : 0.00,
'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' : 0.9,
'batch_accumulator':'mean',
#'num_threads' : 4,
'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)
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)
self.build_net()
self.training = True
self.lock = thread.allocate_lock()
def build_net(self):
print 'Building QNet and targetnet...'
self.qnet = DQN(self.params,'qnet')
self.targetnet = DQN(self.params,'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}
self.saver = tf.train.Saver(saver_dict)
#self.saver = tf.train.Saver()
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:
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 do_training(self,th_idx):
#print 'Training thread ' + str(th_idx) + ' initiated'
print 'Training thread initiated'
while True:
if self.training and self.step % self.params['learning_interval'] == 0 and self.DB.get_size() > self.params['train_start'] :
bat_s,bat_a,bat_t,bat_n,bat_r = self.DB.get_batches()
bat_a = self.get_onehot(bat_a)
if self.params['copy_freq'] > 0 :
feed_dict={self.targetnet.x: bat_n}
self.lock.acquire()
q_t = self.sess.run(self.targetnet.y,feed_dict=feed_dict)
self.lock.release()
else:
feed_dict={self.qnet.x: bat_n}
self.lock.acquire()
q_t = self.sess.run(self.qnet.y,feed_dict=feed_dict)
self.lock.release()
q_t = np.amax(q_t,axis=1)
#print str(th_idx) + '_qt_old : '
#print q_t
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}
#print str(th_idx) + '_old : '
#print self.sess.run(self.qnet.b4)
self.lock.acquire()
_,self.train_cnt,self.cost = self.sess.run([self.qnet.rmsprop,self.qnet.global_step,self.qnet.cost],feed_dict=feed_dict)
#print str(th_idx) + '_new : '
#print self.sess.run(self.qnet.b4)
self.lock.release()
self.total_cost_train += np.sqrt(self.cost)
self.train_cnt_for_disp += 1
def start(self):
self.reset_game()
self.step = 0
self.reset_statistics('all')
self.train_cnt = self.sess.run(self.qnet.global_step)
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')
#for ii in range(self.params['num_threads']):
thread.start_new_thread(self.do_training,(0,))
time.sleep(1.5)
self.s = time.time()
print self.params
print 'Start training!'
print 'Collecting replay memory for ' + str(self.params['train_start']) + ' steps'
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
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)
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.lock.acquire()
self.sess.run(self.cp_ops)
self.lock.release()
if self.training :
self.params['eps'] = max(self.params['eps_min'],1.0 - float(self.step)/float(self.params['eps_step']))
else:
self.params['eps'] = 0.05
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.lock.acquire()
self.saver.save(self.sess,'ckpt/model_'+self.params['network_type']+'_'+str(save_idx))
self.lock.release()
sys.stdout.write('$$$ Model saved : %s\n\n' % ('ckpt/model_'+self.params['network_type']+'_'+str(save_idx)))
sys.stdout.flush()
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()
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
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
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
self.action_idx,self.action, self.maxQ = self.select_action(self.state_proc)
self.state, self.reward, self.terminal = self.engine.next(self.action)
self.reward_scaled = self.reward // max(1,abs(self.reward))
if self.training : self.epi_reward_train += self.reward ; self.total_Q_train += self.maxQ
else : self.epi_reward_eval += self.reward ; self.total_Q_eval += self.maxQ
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
if self.params['only_eval'] == 'y':
time.sleep(0.01)
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']:
#greedy with random tie-breaking
self.lock.acquire()
Q_pred = self.sess.run(self.qnet.y, feed_dict = {self.qnet.x: np.reshape(st, (1,84,84,4))})[0]
self.lock.release()
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)
else:
#random
act_idx = np.random.randint(0,len(self.engine.legal_actions))
self.lock.acquire()
Q_pred = self.sess.run(self.qnet.y, feed_dict = {self.qnet.x: np.reshape(st, (1,84,84,4))})[0]
self.lock.release()
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] == '-num_threads' : params['num_threads'] = 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)'
#print ' -num_threads (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'] = 200000
params['steps_per_eval'] = 10000
params['copy_freq'] = 40000
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']=10000
params['batch_accumulator'] = 'sum'
params['eps_step'] = 4000000
params['num_epochs'] = 1000
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()