Skip to content

Commit

Permalink
hi
Browse files Browse the repository at this point in the history
  • Loading branch information
@godka
godka committed Sep 21, 2018
1 parent fb630c5 commit 0d45650
Show file tree
Hide file tree
Showing 4 changed files with 50 additions and 13,210 deletions.
100 changes: 47 additions & 53 deletions RL_brain.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -14,7 +14,10 @@
import pandas as pd
import tensorflow as tf
import tflearn

S_INFO = 6 # bit_rate, buffer_size, next_chunk_size, bandwidth_measurement(throughput and time), chunk_til_video_end
S_LEN = 8 # take how many frames in the past
A_DIM = 6
FEATURE_NUM = 64
np.random.seed(1)
tf.set_random_seed(1)

Expand Down Expand Up @@ -72,35 +75,28 @@ def _build_net(self):
self.q_target = tf.placeholder(
tf.float32, [None, self.n_actions], name='Q_target') # for calculating loss
with tf.variable_scope('eval_net'):
net = tf.expand_dims(self.s, -1)
net = tflearn.conv_1d(net, 32, 3, activation='relu')
net = tflearn.conv_1d(net, 64, 3, activation='relu')
net = tflearn.conv_1d(net, 64, 3, activation='relu')
net = tflearn.fully_connected(net, 4096, activation='relu')
net = tflearn.fully_connected(net, 1024, activation='relu')
net = tflearn.fully_connected(net, 128, activation='relu')
inputs = tf.reshape(self.s, (-1, S_INFO, S_LEN))
split_0 = tflearn.fully_connected(
inputs[:, 0:1, :], FEATURE_NUM, activation='relu')
split_1 = tflearn.fully_connected(
inputs[:, 1:2, :], FEATURE_NUM, activation='relu')
split_2 = tflearn.conv_1d(
inputs[:, 2:3, :], FEATURE_NUM, 4, activation='relu')
split_3 = tflearn.conv_1d(
inputs[:, 3:4, :], FEATURE_NUM, 4, activation='relu')
split_4 = tflearn.conv_1d(
inputs[:, 4:5, :self.n_actions], FEATURE_NUM, 4, activation='relu')
split_5 = tflearn.fully_connected(
inputs[:, 5:6, :], FEATURE_NUM, activation='relu')

split_2_flat = tflearn.flatten(split_2)
split_3_flat = tflearn.flatten(split_3)
split_4_flat = tflearn.flatten(split_4)


net = tf.stack([split_0, split_1, split_2_flat, split_3_flat, split_4_flat, split_5], axis=1)
net = tflearn.fully_connected(net, FEATURE_NUM, activation='relu')
self.q_eval = tflearn.fully_connected(net, self.n_actions, activation='softmax')
# # c_names(collections_names) are the collections to store variables
# c_names, n_l1, w_initializer, b_initializer = \
# ['eval_net_params', tf.GraphKeys.GLOBAL_VARIABLES], 10, \
# tf.random_normal_initializer(
# 0., 0.3), tf.constant_initializer(0.1) # config of layers

# # first layer. collections is used later when assign to target net
# with tf.variable_scope('l1'):
# w1 = tf.get_variable(
# 'w1', [self.n_features, n_l1], initializer=w_initializer, collections=c_names)
# b1 = tf.get_variable(
# 'b1', [1, n_l1], initializer=b_initializer, collections=c_names)
# l1 = tf.nn.relu(tf.matmul(self.s, w1) + b1)

# # second layer. collections is used later when assign to target net
# with tf.variable_scope('l2'):
# w2 = tf.get_variable(
# 'w2', [n_l1, self.n_actions], initializer=w_initializer, collections=c_names)
# b2 = tf.get_variable(
# 'b2', [1, self.n_actions], initializer=b_initializer, collections=c_names)
# self.q_eval = tf.matmul(l1, w2) + b2

with tf.variable_scope('loss'):
self.loss = tf.reduce_mean(
Expand All @@ -113,31 +109,29 @@ def _build_net(self):
self.s_ = tf.placeholder(
tf.float32, [None, self.n_features], name='s_') # input
with tf.variable_scope('target_net'):
# c_names(collections_names) are the collections to store variables
c_names = ['target_net_params', tf.GraphKeys.GLOBAL_VARIABLES]
net = tf.expand_dims(self.s, -1)
net = tflearn.conv_1d(net, 32, 3, activation='relu')
net = tflearn.conv_1d(net, 64, 3, activation='relu')
net = tflearn.conv_1d(net, 64, 3, activation='relu')
net = tflearn.fully_connected(net, 4096, activation='relu')
net = tflearn.fully_connected(net, 1024, activation='relu')
net = tflearn.fully_connected(net, 128, activation='relu')

inputs = tf.reshape(self.s_, (-1, S_INFO, S_LEN))
split_0 = tflearn.fully_connected(
inputs[:, 0:1, :], FEATURE_NUM, activation='relu')
split_1 = tflearn.fully_connected(
inputs[:, 1:2, :], FEATURE_NUM, activation='relu')
split_2 = tflearn.conv_1d(
inputs[:, 2:3, :], FEATURE_NUM, 4, activation='relu')
split_3 = tflearn.conv_1d(
inputs[:, 3:4, :], FEATURE_NUM, 4, activation='relu')
split_4 = tflearn.conv_1d(
inputs[:, 4:5, :self.n_actions], FEATURE_NUM, 4, activation='relu')
split_5 = tflearn.fully_connected(
inputs[:, 5:6, :], FEATURE_NUM, activation='relu')

split_2_flat = tflearn.flatten(split_2)
split_3_flat = tflearn.flatten(split_3)
split_4_flat = tflearn.flatten(split_4)


net = tf.stack([split_0, split_1, split_2_flat, split_3_flat, split_4_flat, split_5], axis=1)
net = tflearn.fully_connected(net, FEATURE_NUM, activation='relu')
self.q_next = tflearn.fully_connected(net, self.n_actions, activation='softmax')
# # first layer. collections is used later when assign to target net
# with tf.variable_scope('l1'):
# w1 = tf.get_variable(
# 'w1', [self.n_features, n_l1], initializer=w_initializer, collections=c_names)
# b1 = tf.get_variable(
# 'b1', [1, n_l1], initializer=b_initializer, collections=c_names)
# l1 = tf.nn.relu(tf.matmul(self.s_, w1) + b1)

# # second layer. collections is used later when assign to target net
# with tf.variable_scope('l2'):
# w2 = tf.get_variable(
# 'w2', [n_l1, self.n_actions], initializer=w_initializer, collections=c_names)
# b2 = tf.get_variable(
# 'b2', [1, self.n_actions], initializer=b_initializer, collections=c_names)
# self.q_next = tf.matmul(l1, w2) + b2

def store_transition(self, s, a, r, s_):
if not hasattr(self, 'memory_counter'):
Expand Down
Binary file modified RL_brain.pyc
View file
Binary file not shown.
4 changes: 2 additions & 2 deletions main.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -10,9 +10,9 @@

env = gym.make('ABR-v0')

RL = DeepQNetwork(n_actions=A_DIM, n_features=S_INFO*S_LEN, learning_rate=0.001, e_greedy=0.9,
RL = DeepQNetwork(n_actions=A_DIM, n_features=S_INFO*S_LEN, learning_rate=1e-4, e_greedy=0.99,
replace_target_iter=100, memory_size=3000,
e_greedy_increment=1e-8,)
e_greedy_increment=1e-6,)

_file = open('test.csv', 'w')
step = 0
Expand Down
Loading

0 comments on commit 0d45650

Please sign in to comment.