-
Notifications
You must be signed in to change notification settings - Fork 3
/
gamut_4players.py
182 lines (157 loc) · 12.2 KB
/
gamut_4players.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
176
177
178
179
180
181
182
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 = 50
player_num = 4
network_input_dimension = 20
# ------------------------------------------- #
class Quadratic(object):
def __init__(self, sess):
self.model_name = 'Quadratic'
self.player_num = player_num
self.epoch_num = epoch_num
self.game_if_set = 0
self.sess = sess
self.inner_loop = inner_loop
self.player_network_dict = {}
self.sdim = network_input_dimension
self.batch_size = batch_size
def game_setting(self, game_matrix_1, game_matrix_2, game_matrix_3, game_matrix_4):
if self.game_if_set == 0:
self.game_matrix_1 = tf.convert_to_tensor(game_matrix_1, dtype = tf.float32)
self.game_matrix_2 = tf.convert_to_tensor(game_matrix_2, dtype = tf.float32)
self.game_matrix_3 = tf.convert_to_tensor(game_matrix_3, dtype=tf.float32)
self.game_matrix_4 = tf.convert_to_tensor(game_matrix_4, dtype=tf.float32)
self.game_if_set = 1
print('[*] Game Matrix Setting Complete.')
else:
print('[!!!] Detected Double Setting. Setting has been paused.')
def player_network(self, scope):
random_sample = tf.random_uniform(shape=[self.batch_size, self.sdim], minval=-1.0, maxval=1.0)
self.W1 = tf.Variable(initial_value=tf.random_uniform(shape=[self.sdim, 2 * self.sdim], minval=-0.2, maxval=0.2),name=scope + '_W1')
self.b1 = tf.Variable(initial_value=tf.random_uniform(shape=[2 * self.sdim], minval=-0.2, maxval=0.2),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.Variable(initial_value=tf.random_uniform(shape=[2 * self.sdim, 4 * self.sdim], minval=-0.2, maxval=0.2),name=scope + '_W2')
self.b2 = tf.Variable(initial_value=tf.random_uniform(shape=[4 * self.sdim], minval=-0.2, maxval=0.2),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.Variable(initial_value=tf.random_uniform(shape=[4 * self.sdim, 16 * self.sdim], minval=-0.2, maxval=0.2),name=scope + '_W3')
self.b3 = tf.Variable(initial_value=tf.random_uniform(shape=[16 * self.sdim], minval=-0.2, maxval=0.2),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.Variable(initial_value=tf.random_uniform(shape=[16 * self.sdim, 16 * self.sdim], minval=-0.1, maxval=0.1),name=scope + '_W4')
self.b4 = tf.Variable(initial_value=tf.random_uniform(shape=[16 * self.sdim], minval=-0.1, maxval=0.1),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.Variable(initial_value=tf.random_uniform(shape=[16 * self.sdim, 4 * self.sdim], minval=-0.2, maxval=0.2),name=scope + '_W5')
self.b5 = tf.Variable(initial_value=tf.random_uniform(shape=[4 * self.sdim], minval=-0.2, maxval=0.2),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.Variable(initial_value=tf.random_uniform(shape=[4 * self.sdim, 2 * self.sdim], minval=-0.2, maxval=0.2),name=scope + '_W6')
self.b6 = tf.Variable(initial_value=tf.random_uniform(shape=[2 * self.sdim], minval=-0.2, maxval=0.2),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.Variable(initial_value=tf.random_uniform(shape=[2 * self.sdim, game_dimension], minval=-0.2, maxval=0.2),name=scope + '_W7')
self.b7 = tf.Variable(initial_value=tf.random_uniform(shape=[game_dimension], minval=-0.2, maxval=0.2),name=scope + '_b7')
output_layer = tf.matmul(hidden_layer_6, self.W7) + tf.tile(tf.expand_dims(self.b7, axis=0),[self.batch_size, 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))
self.player_network_dict2 = self.player_network(scope='player_number_' + str(3))
self.player_network_dict3 = self.player_network(scope='player_number_' + str(4))
def player_network_local(self, scope):
if scope == 'player_number_1':
L = self.expected_utility1
elif scope == 'player_number_2':
L = self.expected_utility2
elif scope == 'player_number_3':
L = self.expected_utility3
else:
L = self.expected_utility4
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]
output_layer = tf.nn.tanh(tf.matmul(hidden_layer_6, W7_) + tf.tile(tf.expand_dims(b7_, axis = 0), [self.batch_size, 1]))
# output_layer = 2 * tf.nn.sigmoid(hidden_layer_7) - tf.ones(shape = [self.batch_size, game_dimension], dtype = tf.float32)
return output_layer
def build_model(self):
self.Lambda = tf.placeholder(dtype = tf.float32)
player1_action_batch = self.player_network_dict0
player2_action_batch = self.player_network_dict1
player3_action_batch = self.player_network_dict2
player4_action_batch = self.player_network_dict3
player_action_batch = tf.concat([player1_action_batch, player2_action_batch, player3_action_batch, player4_action_batch], axis = 1)
self.expected_utility1 = tf.reduce_mean(tf.diag_part(tf.matmul(tf.matmul(player_action_batch, self.game_matrix_1),
tf.transpose(player_action_batch, perm = [1,0]))))
self.expected_utility2 = tf.reduce_mean(tf.diag_part(tf.matmul(tf.matmul(player_action_batch, self.game_matrix_2),
tf.transpose(player_action_batch, perm = [1,0]))))
self.expected_utility3 = tf.reduce_mean(tf.diag_part(tf.matmul(tf.matmul(player_action_batch, self.game_matrix_3),
tf.transpose(player_action_batch, perm=[1, 0]))))
self.expected_utility4 = tf.reduce_mean(tf.diag_part(tf.matmul(tf.matmul(player_action_batch, self.game_matrix_4),
tf.transpose(player_action_batch, perm=[1, 0]))))
self.player1_action_local = self.player_network_local(scope='player_number_1')
self.player2_action_local = self.player_network_local(scope='player_number_2')
self.player3_action_local = self.player_network_local(scope='player_number_3')
self.player4_action_local = self.player_network_local(scope='player_number_4')
local_player1_batch = tf.concat([self.player1_action_local, player2_action_batch, player3_action_batch, player4_action_batch], axis = 1)
local_player2_batch = tf.concat([player1_action_batch, self.player2_action_local, player3_action_batch, player4_action_batch], axis=1)
local_player3_batch = tf.concat([player1_action_batch, player2_action_batch, self.player3_action_local, player4_action_batch], axis=1)
local_player4_batch = tf.concat([player1_action_batch, player2_action_batch, player3_action_batch, self.player4_action_local], axis=1)
self.local_expected_utility1 = tf.reduce_mean(tf.diag_part(tf.matmul(tf.matmul(local_player1_batch, self.game_matrix_1),
tf.transpose(local_player1_batch, perm = [1,0]))))
self.local_expected_utility2 = tf.reduce_mean(tf.diag_part(tf.matmul(tf.matmul(local_player2_batch, self.game_matrix_2),
tf.transpose(local_player2_batch, perm = [1,0]))))
self.local_expected_utility3 = tf.reduce_mean(tf.diag_part(tf.matmul(tf.matmul(local_player3_batch, self.game_matrix_3),
tf.transpose(local_player3_batch, perm=[1, 0]))))
self.local_expected_utility4 = tf.reduce_mean(tf.diag_part(tf.matmul(tf.matmul(local_player4_batch, self.game_matrix_4),
tf.transpose(local_player4_batch, perm=[1, 0]))))
self.loss_function = tf.abs(self.expected_utility1 + self.expected_utility2 + self.expected_utility3 + self.expected_utility4
- self.local_expected_utility1 - self.local_expected_utility2 - self.local_expected_utility3 -self.local_expected_utility4)
def optimize_model(self):
file_out_min = open('output_gamut_3.txt','w')
self.optimizer = tf.train.MomentumOptimizer(1e-3,0.9)
self.minimize = self.optimizer.minimize(self.loss_function,var_list = tf.trainable_variables())
tf.global_variables_initializer().run()
Lambda = 1e-3
for epoch in range(self.epoch_num):
self.sess.run(self.minimize, feed_dict = {self.Lambda : Lambda})
loss = self.sess.run(self.loss_function, feed_dict = {self.Lambda : Lambda})
file_out_min.write(str(loss) + '\n')
print('------------------ epoch '+str(epoch)+' has finished ---------------')
print(loss)
def main():
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
# ------------game setting------------- #
quadratic = Quadratic(sess)
# ------------------------------------- #
game_matrix_1 = np.random.rand(4 * game_dimension, 4 * game_dimension)
game_matrix_2 = np.random.rand(4 * game_dimension, 4 * game_dimension)
game_matrix_3 = np.random.rand(4 * game_dimension, 4 * game_dimension)
game_matrix_4 = np.random.rand(4 * game_dimension, 4 * game_dimension)
quadratic.game_setting(game_matrix_1, game_matrix_2, game_matrix_3, game_matrix_4)
quadratic.player_network_setting()
quadratic.build_model()
quadratic.optimize_model()
if __name__ == '__main__':
main()