forked from Odinnnnnnn/Nash_equilibrium_mixed
-
Notifications
You must be signed in to change notification settings - Fork 0
/
blotto_comparison_GNI.py
171 lines (143 loc) · 9.91 KB
/
blotto_comparison_GNI.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
import numpy as np
from utils import *
import os
'''In this blotto game, '''
os.environ['CUDA_VISIBLE_DEVICES'] = '0,1'
# ------------------hypers------------------- #
batch_size = 100
game_dimension = 3
epoch_num = 2000
inner_loop = 1
player_num = 2
network_input_dimension = 20
# ------------------------------------------- #
class Blotto(object):
def __init__(self, sess):
self.model_name = 'Blotto'
self.player_num = player_num
self.epoch_num = epoch_num
self.sess = sess
self.inner_loop = inner_loop
self.player_network_dict = {}
self.sdim = network_input_dimension
self.batch_size = batch_size
self.Lambda = tf.placeholder(dtype=tf.float32)
def build_network_pure(self):
self.player1_action_pure_ = tf.Variable(initial_value = tf.random_uniform(shape = [game_dimension], minval = -0.2, maxval = 0.2), name = 'pure_p1')
self.player2_action_pure_ = tf.Variable(initial_value=tf.random_uniform(shape=[game_dimension], minval=-0.2, maxval=0.2), name = 'pure_p2')
self.player1_action_pure = tf.nn.softmax(self.player1_action_pure_)
self.player2_action_pure = tf.nn.softmax(self.player2_action_pure_)
self.expected_utility1_pure = tf.reduce_sum(tf.nn.tanh(self.player1_action_pure - self.player2_action_pure))
self.expected_utility2_pure = -tf.reduce_sum(tf.nn.tanh(self.player1_action_pure - self.player2_action_pure))
self.player1_action_pure_local = tf.nn.softmax(self.player1_action_pure_ - self.Lambda * tf.gradients(self.expected_utility1_pure, self.player1_action_pure_)[0])
self.player2_action_pure_local = tf.nn.softmax(self.player2_action_pure_ - self.Lambda * tf.gradients(self.expected_utility2_pure,self.player2_action_pure_)[0])
self.local_expected_utility1_pure = tf.reduce_sum(tf.nn.tanh(self.player1_action_pure_local - self.player2_action_pure))
self.local_expected_utility2_pure = -tf.reduce_sum(tf.nn.tanh(self.player1_action_pure - self.player2_action_pure_local))
self.loss_function_pure = tf.abs(self.expected_utility1_pure + self.expected_utility2_pure - self.local_expected_utility1_pure - self.local_expected_utility2_pure)
def player_network(self, scope):
if scope == 'player_number_1':
self.b7_player1 = tf.placeholder(dtype = tf.float32, name = scope + '_b7')
else:
self.b7_player2 = tf.placeholder(dtype=tf.float32, name = scope + '_b7')
random_sample = tf.random_uniform(shape = [self.batch_size, self.sdim], minval = -1.0, maxval = 1.0)
# self.random_sample = tf.placeholder(shape = [self.batch_size, self.sdim], dtype = tf.float32, name = scope + '_input')
self.W1 = tf.zeros(shape = [self.sdim, 2 * self.sdim], name = scope + '_W1')
self.b1 = tf.zeros(shape = [2 * self.sdim], name = scope + '_b1')
hidden_layer_1 = tf.nn.tanh(tf.matmul(random_sample, self.W1) + tf.tile(tf.expand_dims(self.b1, axis = 0), [self.batch_size, 1]))
self.W2 = tf.zeros(shape = [2 * self.sdim, 4 * self.sdim], name = scope + '_W2')
self.b2 = tf.zeros(shape = [4 * self.sdim], name = scope + '_b2')
hidden_layer_2 = tf.nn.tanh(tf.matmul(hidden_layer_1, self.W2) + tf.tile(tf.expand_dims(self.b2, axis = 0), [self.batch_size, 1]))
self.W3 = tf.zeros(shape = [4 * self.sdim, 16 * self.sdim], name = scope + '_W3')
self.b3 = tf.zeros(shape = [16 * self.sdim], name = scope + '_b3')
hidden_layer_3 = tf.nn.tanh(tf.matmul(hidden_layer_2, self.W3) + tf.tile(tf.expand_dims(self.b3, axis = 0), [self.batch_size, 1]))
self.W4 = tf.zeros(shape = [16 * self.sdim, 16 * self.sdim], name = scope + '_W4')
self.b4 = tf.zeros(shape = [16 * self.sdim], name = scope + '_b4')
hidden_layer_4 = tf.nn.relu(tf.matmul(hidden_layer_3, self.W4) + tf.tile(tf.expand_dims(self.b4, axis = 0), [self.batch_size, 1]))
self.W5 = tf.zeros(shape = [16 * self.sdim, 4 * self.sdim], name = scope + '_W5')
self.b5 = tf.zeros(shape = [4 * self.sdim], name = scope + '_b5')
hidden_layer_5 = tf.nn.tanh(tf.matmul(hidden_layer_4, self.W5) + tf.tile(tf.expand_dims(self.b5, axis = 0), [self.batch_size, 1]))
self.W6 = tf.zeros(shape = [4 * self.sdim, 2 * self.sdim], name = scope + '_W6')
self.b6 = tf.zeros(shape = [2 * self.sdim], name = scope + '_b6')
hidden_layer_6 = tf.nn.tanh(tf.matmul(hidden_layer_5, self.W6) + tf.tile(tf.expand_dims(self.b6, axis = 0), [self.batch_size, 1]))
self.W7 = tf.zeros(shape = [2 * self.sdim, game_dimension], name = scope + '_W7')
if scope == 'player_number_1':
self.b7 = self.b7_player1
else:
self.b7 = self.b7_player2
hidden_layer_7 = tf.matmul(hidden_layer_6, self.W7) + tf.tile(tf.expand_dims(self.b7, axis = 0), [self.batch_size, 1])
output_layer = tf.nn.softmax(hidden_layer_7, axis = 1)
return output_layer
def player_network_setting(self):
self.player_network_dict0 = self.player_network(scope = 'player_number_' + str(1))
self.player_network_dict1 = self.player_network(scope = 'player_number_' + str(2))
def player_network_local(self, scope):
if scope == 'player_number_1':
L = self.expected_utility1
else:
L = self.expected_utility2
with tf.name_scope(scope):
random_sample = tf.random_uniform(shape = [self.batch_size, self.sdim], minval = -1.0, maxval = 1.0)
W1_ = self.W1 - self.Lambda * tf.gradients(L, self.W1)[0]
b1_ = self.b1 - self.Lambda * tf.gradients(L, self.b1)[0]
hidden_layer_1 = tf.nn.tanh(tf.matmul(random_sample, W1_) + tf.tile(tf.expand_dims(b1_, axis = 0), [self.batch_size, 1]))
W2_ = self.W2 - self.Lambda * tf.gradients(L, self.W2)[0]
b2_ = self.b2 - self.Lambda * tf.gradients(L, self.b2)[0]
hidden_layer_2 = tf.nn.tanh(tf.matmul(hidden_layer_1, W2_) + tf.tile(tf.expand_dims(b2_, axis = 0), [self.batch_size, 1]))
W3_ = self.W3 - self.Lambda * tf.gradients(L, self.W3)[0]
b3_ = self.b3 - self.Lambda * tf.gradients(L, self.b3)[0]
hidden_layer_3 = tf.nn.tanh(tf.matmul(hidden_layer_2, W3_) + tf.tile(tf.expand_dims(b3_, axis = 0), [self.batch_size, 1]))
W4_ = self.W4 - self.Lambda * tf.gradients(L, self.W4)[0]
b4_ = self.b4 - self.Lambda * tf.gradients(L, self.b4)[0]
hidden_layer_4 = tf.nn.relu(tf.matmul(hidden_layer_3, W4_) + tf.tile(tf.expand_dims(b4_, axis = 0), [self.batch_size, 1]))
W5_ = self.W5 - self.Lambda * tf.gradients(L, self.W5)[0]
b5_ = self.b5 - self.Lambda * tf.gradients(L, self.b5)[0]
hidden_layer_5 = tf.nn.tanh(tf.matmul(hidden_layer_4, W5_) + tf.tile(tf.expand_dims(b5_, axis = 0), [self.batch_size, 1]))
W6_ = self.W6 - self.Lambda * tf.gradients(L, self.W6)[0]
b6_ = self.b6 - self.Lambda * tf.gradients(L, self.b6)[0]
hidden_layer_6 = tf.nn.tanh(tf.matmul(hidden_layer_5, W6_) + tf.tile(tf.expand_dims(b6_, axis = 0), [self.batch_size, 1]))
W7_ = self.W7 - self.Lambda * tf.gradients(L, self.W7)[0]
b7_ = self.b7 - self.Lambda * tf.gradients(L, self.b7)[0]
hidden_layer_7 = tf.nn.tanh(tf.matmul(hidden_layer_6, W7_) + tf.tile(tf.expand_dims(b7_, axis = 0), [self.batch_size, 1]))
output_layer = tf.nn.softmax(hidden_layer_7, axis=1)
return output_layer
def build_model(self):
self.file_out = open('comparison_blotto_3.txt','w')
player1_action_batch = self.player_network_dict0
player2_action_batch = self.player_network_dict1
score = tf.reduce_mean(tf.reduce_sum(tf.nn.tanh(player1_action_batch - player2_action_batch), axis = 1))
self.expected_utility1 = score
self.expected_utility2 = -score
self.player1_action_local = self.player_network_local(scope='player_number_1')
self.player2_action_local = self.player_network_local(scope='player_number_2')
local_score1 = tf.reduce_mean(tf.reduce_sum(tf.nn.tanh(self.player1_action_local - player2_action_batch), axis = 1))
local_score2 = tf.reduce_mean(tf.reduce_sum(tf.nn.tanh(player1_action_batch - self.player2_action_local), axis = 1))
self.local_expected_utility1 = local_score1
self.local_expected_utility2 = -local_score2
self.loss_function = tf.abs(self.expected_utility1 + self.expected_utility2 - self.local_expected_utility1 - self.local_expected_utility2)
def optimize_model_pure(self):
self.optimizer_pure = tf.train.MomentumOptimizer(1e-2,0.9)
self.minimize_pure = self.optimizer_pure.minimize(self.loss_function_pure, var_list=tf.trainable_variables(scope = 'pure'))
tf.global_variables_initializer().run()
Lambda = 1e-3
for epoch in range(self.epoch_num):
self.sess.run(self.minimize_pure, feed_dict = {self.Lambda: Lambda})
loss = self.sess.run(self.loss_function_pure, feed_dict = {self.Lambda: Lambda})
player1_pure_ = self.sess.run(self.player1_action_pure_)
player2_pure_ = self.sess.run(self.player2_action_pure_)
mixed_loss = self.sess.run(self.loss_function, feed_dict = {self.Lambda:Lambda, self.b7_player1:player1_pure_, self.b7_player2:player2_pure_})
print('------------------ epoch '+str(epoch)+' has finished ---------------')
print('loss:' + str(loss))
self.file_out.write(str(mixed_loss) + '\n')
print('mixed_loss:' + str(mixed_loss))
def main():
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
# ------------game setting------------- #
blotto = Blotto(sess)
# ------------------------------------- #
blotto.build_network_pure()
blotto.player_network_setting()
blotto.build_model()
blotto.optimize_model_pure()
if __name__ == '__main__':
main()
# train = optimizer.minimize(loss, var_list=tf.trainable_variables())