Nash_equilibrium_mixed/bimatrix_comparison_GNI.py at master · Odinnnnnnn/Nash_equilibrium_mixed · GitHub

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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 = 5
epoch_num = 10000
inner_loop = 1
player_num = 2
network_input_dimension = 20
# ------------------------------------------- #

class BiMatrix(object):
    def __init__(self, sess):
        self.model_name = 'BiMatrix'
        self.player_num = player_num
        self.epoch_num = epoch_num
        self.game_matrix = np.random.random((game_dimension, game_dimension))
        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):
        if self.game_if_set == 0:
            self.game_matrix = game_matrix
            self.game_if_set = 1
            print('[*] Game Matrix Setting Complete.')
        else:
            print('[!!!] Detected Double Setting. Setting has been paused.')

    def build_network_pure(self):
        self.tensor_game_matrix = tf.convert_to_tensor(self.game_matrix, dtype=tf.float32)

        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.tanh(self.player1_action_pure_)
        self.player2_action_pure = tf.nn.tanh(self.player2_action_pure_)
        self.expected_utility1_pure = tf.matmul(tf.expand_dims(self.player1_action_pure, axis = 0), tf.matmul(self.tensor_game_matrix, tf.expand_dims(self.player2_action_pure,axis=1)))
        self.expected_utility2_pure = -self.expected_utility1_pure
        self.player1_action_pure_local = tf.nn.tanh(self.player1_action_pure_ - self.Lambda * tf.gradients(self.expected_utility1_pure, self.player1_action_pure_)[0])
        self.player2_action_pure_local = tf.nn.tanh(self.player2_action_pure_ - self.Lambda * tf.gradients(self.expected_utility2_pure,self.player2_action_pure_)[0])
        self.local_expected_utility1_pure = tf.reduce_mean(tf.matmul(tf.expand_dims(self.player1_action_pure_local, axis = 0), tf.matmul(self.tensor_game_matrix, tf.expand_dims(self.player2_action_pure,axis=1))))
        self.local_expected_utility2_pure = -tf.reduce_mean(tf.matmul(tf.expand_dims(self.player1_action_pure, axis = 0), tf.matmul(self.tensor_game_matrix, tf.expand_dims(self.player2_action_pure_local,axis=1))))
        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.tanh(hidden_layer_7)

        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]
            output_layer = tf.nn.tanh(tf.matmul(hidden_layer_6, W7_) + tf.tile(tf.expand_dims(b7_, axis = 0), [self.batch_size, 1]))


        return output_layer


    def build_model(self):
        self.file_out = open('comparsion_bimatrix_5.txt','w')
        self.player1_action_batch = self.player_network_dict0
        self.player2_action_batch = self.player_network_dict1


        score_matrix = tf.matmul(tf.matmul(self.player1_action_batch, self.tensor_game_matrix), tf.transpose(self.player2_action_batch, perm = [1,0]))
        self.expected_utility1 = tf.reduce_mean(tf.diag_part(score_matrix))
        self.expected_utility2 = -tf.reduce_mean(tf.diag_part(score_matrix))


        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_score_matrix_1 = tf.matmul(tf.matmul(self.player1_action_local, self.tensor_game_matrix), tf.transpose(self.player2_action_batch, perm = [1,0]))
        local_score_matrix_2 = tf.matmul(tf.matmul(self.player1_action_batch, self.tensor_game_matrix),
                                         tf.transpose(self.player2_action_local, perm=[1, 0]))
        self.local_expected_utility1 = tf.reduce_mean(tf.diag_part(local_score_matrix_1))
        self.local_expected_utility2 = -tf.reduce_mean(tf.diag_part(local_score_matrix_2))
        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------------- #
        bimatrix = BiMatrix(sess)
        # game_matrix = np.random.random((game_dimension, game_dimension))
        # game_matrix_PositiveDefinite = (game_matrix.T + game_matrix) / 2.0
        game_matrix = np.eye(game_dimension, game_dimension)
        bimatrix.game_setting(game_matrix)
        # ------------------------------------- #

        bimatrix.build_network_pure()
        bimatrix.player_network_setting()
        bimatrix.build_model()
        bimatrix.optimize_model_pure()

if __name__ == '__main__':
    main()

    # train = optimizer.minimize(loss, var_list=tf.trainable_variables())