-
Notifications
You must be signed in to change notification settings - Fork 0
/
deep_q_network.py
179 lines (139 loc) · 5.14 KB
/
deep_q_network.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
from __future__ import print_function
import tensorflow as tf
import cv2
import sys
import game.wrapped_flappy_bird as game
import random
import numpy as np
from collections import deque
GAME='bird'
ACTIONS=2
GAMMA=0.99
OBSERVE=100000
EXPLORE=2000000
FINAL_EPSILON=0.0001
INITIAL_EPSILON=0.0001
REPLAY_MEMORY=50000
BATCH_SIZE=32
FRAME_PER_ACTION=1
def weight_variable(shape):
initial=tf.truncated_normal(shape,stddev=0.01)
return tf.Variable(initial)
def bias_variable(shape):
initial=tf.constant(0.01,shape=shape)
return tf.Variable(initial)
def conv2d(x,W,stride):
return tf.nn.conv2d(x,W,strides=[1,stride,stride,1],padding='SAME')
def max_pool_2x2(x):
return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')
def createNetwork():
W_conv1=weight_variable([8,8,4,32])
b_conv1=bias_variable([32])
W_conv2 = weight_variable([4, 4, 32, 64])
b_conv2 = bias_variable([64])
W_conv3 = weight_variable([3, 3, 64, 64])
b_conv3 = bias_variable([64])
W_fc1 = weight_variable([1600,512])
b_fc1 = bias_variable([512])
W_fc2 = weight_variable([512,ACTIONS])
b_fc2 = bias_variable([ACTIONS])
s=tf.placeholder('float',[None,80,80,4])
h_conv1=tf.nn.relu(conv2d(s,W_conv1,4)+b_conv1)
h_pool1=max_pool_2x2(h_conv1)
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2, 2) + b_conv2)
#h_pool2 = max_pool_2x2(h_conv1)
h_conv3 = tf.nn.relu(conv2d(h_conv2, W_conv3, 1) + b_conv3)
#h_pool3 = max_pool_2x2(h_conv1)
h_conv3_flat=tf.reshape(h_conv3,[-1,1600])
h_fc1=tf.nn.relu(tf.matmul(h_conv3_flat,W_fc1)+b_fc1)
readout=tf.matmul(h_fc1,W_fc2)+b_fc2
return s,readout,h_fc1
def trainNetwork(s,readout,h_fc1,sess):
a=tf.placeholder('float',[None,ACTIONS])
y=tf.placeholder('float',[None])
readout_action=tf.reduce_sum(tf.multiply(readout,a),reduction_indices=1)
cost=tf.reduce_mean(tf.square(y-readout_action))
train_step=tf.train.AdamOptimizer(1e-6).minimize(cost)
game_state=game.GameState()
D=deque()
a_file=open('logs_'+GAME+"/readout.txt",'w')
h_file=open('logs_'+GAME+"/hidden.txt",'w')
do_nothing=np.zeros(ACTIONS)
do_nothing[0]=1
x_t,r_0,terminal=game_state.frame_step(do_nothing)
x_t=cv2.cvtColor(cv2.resize(x_t,(80,80)),cv2.COLOR_RGBA2GRAY)
ret,x_t=cv2.threshold(x_t,1,255,cv2.THRESH_BINARY)
s_t=np.stack((x_t,x_t,x_t,x_t),axis=2)
saver=tf.train.Saver()
sess.run(tf.initialize_all_variables())
checkpoint=tf.train.get_checkpoint_state('saved_networks')
if checkpoint and checkpoint.model_checkpoint_path:
saver.restore(sess,checkpoint.model_checkpoint_path)
print('successed')
else:
print('could not find old network weights')
epsilon=INITIAL_EPSILON
t=0
while 'flappy bird'!='angry bird':
readout_t=readout.eval(feed_dict={s:[s_t]})[0]
a_t=np.zeros([ACTIONS])
action_index=0
if t % FRAME_PER_ACTION==0:
if random.random()<=epsilon:
print('random actions')
action_index=random.randrange(ACTIONS)
a_t[random.randrange(ACTIONS)]=1
else:
action_index=np.argmax(readout_t)
a_t[action_index]=1
else:
a_t[0]=1
if epsilon>FINAL_EPSILON and t>OBSERVE:
epsilon-=(INITIAL_EPSILON-FINAL_EPSILON)/EXPLORE
x_t1_colored,r_t,terminal=game_state.frame_step(a_t)
x_t1=cv2.cvtColor(cv2.resize(x_t1_colored,(80,80)),cv2.COLOR_RGB2GRAY)
ret,x_t1=cv2.threshold(x_t1,1,255,cv2.THRESH_BINARY)
x_t1=np.reshape(x_t1,(80,80,1))
s_t1=np.append(x_t1,s_t[:,:,:3],axis=2)
if len(D)>REPLAY_MEMORY:
D.popleft()
D.append((s_t, a_t, r_t, s_t1, terminal))
if t>OBSERVE:
minibatch=random.sample(D,BATCH_SIZE)
s_j_batch=[d[0] for d in minibatch]
a_batch=[d[1] for d in minibatch]
r_batch = [d[2] for d in minibatch]
s_j1_batch = [d[3] for d in minibatch]
y_batch=[]
readout_j1_batch=readout.eval(feed_dict={s:s_j1_batch})
for i in range(0,len(minibatch)):
terminal=minibatch[i][0]
if terminal:
y_batch.append(r_batch[i])
else:
y_batch.append(r_batch[i]+GAMMA*np.max(readout_j1_batch[i]))
train_step.run(feed_dict={
y:y_batch,
a:a_batch,
s:s_j_batch
})
s_t=s_t1
t+=1
if t%100000==0:
saver.save(sess,'saved_networks/'+GAME+'-dqn',global_step=t)
state=''
if t<OBSERVE:
state='observe'
elif t>OBSERVE and t<=OBSERVE+EXPLORE:
state='explore'
else:
state='train'
print('TIMESTEP',t,'/STATE',state,'/EPSILON',epsilon,'/ACTION',action_index,'/REWARD',r_t,'/Q_MAX%e'%np.max(readout_t))
if t%10000<=100:
cv2.imwrite('log_tetris/frame'+str(t)+'.png',x_t1)
def main():
sess=tf.InteractiveSession()
s,readout,h_fc1=createNetwork()
trainNetwork(s,readout,h_fc1,sess)
if __name__ == '__main__':
main()