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gamut_comparison_SGA.py
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gamut_comparison_SGA.py
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import numpy as np
from utils import *
from tensorflow.contrib.kfac.python.ops.utils import fwd_gradients
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 = 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
self.Lambda = tf.placeholder(dtype=tf.float32)
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 jac_vec(self, ys, xs, vs):
return fwd_gradients(ys, xs, grad_xs=vs, stop_gradients=xs)
def jac_tran_vec(self, ys, xs, vs):
dydxs = tf.gradients(ys, xs, grad_ys=vs, stop_gradients=xs)
return [tf.zeros_like(x) if dydx is None else dydx for (x, dydx) in zip(xs, dydxs)]
def get_sym_adj(self, Ls, xs):
self.xi = [tf.gradients(l, x)[0] for (l, x) in zip(Ls, xs)]
H_xi = self.jac_vec(self.xi, xs, self.xi)
Ht_xi = self.jac_tran_vec(self.xi, xs, self.xi)
At_xi = [(ht - h) / 2 for (h, ht) in zip(H_xi, Ht_xi)]
return At_xi
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.player3_action_pure_ = tf.Variable(initial_value=tf.random_uniform(shape=[game_dimension], minval=-0.2, maxval=0.2), name='pure_p3')
self.player4_action_pure_ = tf.Variable(initial_value=tf.random_uniform(shape=[game_dimension], minval=-0.2, maxval=0.2), name='pure_p4')
self.player1_action_pure = tf.nn.tanh(self.player1_action_pure_)
self.player2_action_pure = tf.nn.tanh(self.player2_action_pure_)
self.player3_action_pure = tf.nn.tanh(self.player3_action_pure_)
self.player4_action_pure = tf.nn.tanh(self.player4_action_pure_)
player_action_pure = tf.expand_dims(tf.concat([self.player1_action_pure, self.player2_action_pure, self.player3_action_pure, self.player4_action_pure], axis = 0), axis = 0)
self.expected_utility1_pure = tf.reduce_mean(tf.matmul(tf.matmul(player_action_pure, self.game_matrix_1),
tf.transpose(player_action_pure, perm = [1,0])))
self.expected_utility2_pure = tf.reduce_mean(tf.matmul(tf.matmul(player_action_pure, self.game_matrix_2),
tf.transpose(player_action_pure, perm = [1,0])))
self.expected_utility3_pure = tf.reduce_mean(tf.matmul(tf.matmul(player_action_pure, self.game_matrix_3),
tf.transpose(player_action_pure, perm=[1, 0])))
self.expected_utility4_pure = tf.reduce_mean(tf.matmul(tf.matmul(player_action_pure, self.game_matrix_4),
tf.transpose(player_action_pure, perm=[1, 0])))
Ls = [self.expected_utility1_pure, self.expected_utility2_pure, self.expected_utility3_pure, self.expected_utility4_pure]
xs = [self.player1_action_pure_, self.player2_action_pure_, self.player3_action_pure_, self.player4_action_pure_]
self.At_xi = self.get_sym_adj(Ls, xs)
self.sym_gradient = self.xi + self.At_xi
def player_network(self, scope):
if scope == 'player_number_1':
self.b7_player1 = tf.placeholder(dtype = tf.float32, name = scope + '_b7')
elif scope == 'player_number_2':
self.b7_player2 = tf.placeholder(dtype=tf.float32, name = scope + '_b7')
elif scope == 'player_number_3':
self.b7_player3 = tf.placeholder(dtype=tf.float32, name = scope + '_b7')
else:
self.b7_player4 = 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
elif scope == 'player_number_2':
self.b7 = self.b7_player2
elif scope == 'player_number_3':
self.b7 = self.b7_player3
else:
self.b7 = self.b7_player4
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))
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]
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):
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_pure(self):
self.file_out = open('SGA_gamut_3.txt', 'w')
tf.global_variables_initializer().run()
Lambda = 1e-3
for epoch in range(self.epoch_num):
self.player1_action_pure_ = self.player1_action_pure_ + 1e-2 * self.sym_gradient[0]
self.player2_action_pure_ = self.player2_action_pure_ + 1e-2 * self.sym_gradient[1]
self.player3_action_pure_ = self.player3_action_pure_ + 1e-2 * self.sym_gradient[2]
self.player4_action_pure_ = self.player4_action_pure_ + 1e-2 * self.sym_gradient[3]
player1_pure_ = self.sess.run(self.player1_action_pure_)
player2_pure_ = self.sess.run(self.player2_action_pure_)
player3_pure_ = self.sess.run(self.player3_action_pure_)
player4_pure_ = self.sess.run(self.player4_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_, self.b7_player3:player3_pure_, self.b7_player4:player4_pure_})
print('------------------ epoch '+str(epoch)+' has finished ---------------')
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------------- #
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.build_network_pure()
quadratic.player_network_setting()
quadratic.build_model()
quadratic.optimize_model_pure()
if __name__ == '__main__':
main()
# train = optimizer.minimize(loss, var_list=tf.trainable_variables())