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rl_no_training.py
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rl_no_training.py
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import os
os.environ['CUDA_VISIBLE_DEVICES']=''
import numpy as np
import tensorflow as tf
import fixed_env as env
import a3c
import load_trace
import matplotlib.pyplot as plt
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
ACTOR_LR_RATE = 0.0001
CRITIC_LR_RATE = 0.001
VIDEO_BIT_RATE = [300,750,1200,1850,2850,4300] # Kbps
BUFFER_NORM_FACTOR = 10.0
CHUNK_TIL_VIDEO_END_CAP = 48.0
M_IN_K = 1000.0
REBUF_PENALTY = 2.66 # 1 sec rebuffering -> 3 Mbps
SMOOTH_PENALTY = 1
DEFAULT_QUALITY = 1 # default video quality without agent
RANDOM_SEED = 42
RAND_RANGE = 1000
TEST_TRACES = './test_traces/'
SUMMARY_DIR = './Results/test'
LOG_FILE = './Results/test/log_test_pensieve'
# log in format of time_stamp bit_rate buffer_size rebuffer_time chunk_size download_time reward
# NN_MODEL = './model/a3c/nn_model_ep_202000.ckpt'
NN_MODEL = './model/a3c/pretrain_linear_reward.ckpt'
def main():
np.random.seed(RANDOM_SEED)
assert len(VIDEO_BIT_RATE) == A_DIM
if not os.path.exists(SUMMARY_DIR):
os.makedirs(SUMMARY_DIR)
all_cooked_time, all_cooked_bw, all_file_names = load_trace.load_trace(TEST_TRACES)
net_env = env.Environment(all_cooked_time=all_cooked_time,
all_cooked_bw=all_cooked_bw)
log_path = LOG_FILE + '_' + all_file_names[net_env.trace_idx]
log_file = open(log_path, 'wb')
with tf.Session() as sess:
actor = a3c.ActorNetwork(sess,
state_dim=[S_INFO, S_LEN], action_dim=A_DIM,
learning_rate=ACTOR_LR_RATE)
critic = a3c.CriticNetwork(sess,
state_dim=[S_INFO, S_LEN],
learning_rate=CRITIC_LR_RATE)
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver() # save neural net parameters
# restore neural net parameters
nn_model = NN_MODEL
if nn_model is not None: # nn_model is the path to file
saver.restore(sess, nn_model)
print("Model restored.")
time_stamp = 0
last_bit_rate = DEFAULT_QUALITY
bit_rate = DEFAULT_QUALITY
action_vec = np.zeros(A_DIM)
action_vec[bit_rate] = 1
s_batch = [np.zeros((S_INFO, S_LEN))]
a_batch = [action_vec]
r_batch = []
entropy_record = []
video_count = 0
while True: # serve video forever
# the action is from the last decision
# this is to make the framework similar to the real
delay, sleep_time, buffer_size, rebuf, \
video_chunk_size, next_video_chunk_sizes, \
end_of_video, video_chunk_remain = \
net_env.get_video_chunk(bit_rate)
time_stamp += delay # in ms
time_stamp += sleep_time # in ms
# reward is video quality - rebuffer penalty - smoothness
# reward = VIDEO_BIT_RATE[bit_rate] / M_IN_K \
# - REBUF_PENALTY * rebuf \
# - SMOOTH_PENALTY * np.abs(VIDEO_BIT_RATE[bit_rate] -
# VIDEO_BIT_RATE[last_bit_rate]) / M_IN_K
# -- log scale reward --
log_bit_rate = np.log(VIDEO_BIT_RATE[bit_rate] / float(VIDEO_BIT_RATE[0]))
log_last_bit_rate = np.log(VIDEO_BIT_RATE[last_bit_rate] / float(VIDEO_BIT_RATE[0]))
reward = log_bit_rate \
- REBUF_PENALTY * rebuf \
- SMOOTH_PENALTY * np.abs(log_bit_rate - log_last_bit_rate)
r_batch.append(reward)
last_bit_rate = bit_rate
# log time_stamp, bit_rate, buffer_size, reward
log_file.write(str(time_stamp / M_IN_K) + '\t' +
str(VIDEO_BIT_RATE[bit_rate]) + '\t' +
str(buffer_size) + '\t' +
str(rebuf) + '\t' +
str(video_chunk_size) + '\t' +
str(delay) + '\t' +
str(reward) + '\n')
log_file.flush()
# retrieve previous state
if len(s_batch) == 0:
state = [np.zeros((S_INFO, S_LEN))]
else:
state = np.array(s_batch[-1], copy=True)
# dequeue history record
state = np.roll(state, -1, axis=1)
# this should be S_INFO number of terms
state[0, -1] = VIDEO_BIT_RATE[bit_rate] / float(np.max(VIDEO_BIT_RATE)) # last quality
state[1, -1] = buffer_size / BUFFER_NORM_FACTOR # 10 sec
state[2, -1] = float(video_chunk_size) / float(delay) / M_IN_K # kilo byte / ms
state[3, -1] = float(delay) / M_IN_K / BUFFER_NORM_FACTOR # 10 sec
state[4, :A_DIM] = np.array(next_video_chunk_sizes) / M_IN_K / M_IN_K # mega byte
state[5, -1] = np.minimum(video_chunk_remain, CHUNK_TIL_VIDEO_END_CAP) / float(CHUNK_TIL_VIDEO_END_CAP)
action_prob = actor.predict(np.reshape(state, (1, S_INFO, S_LEN)))
action_cumsum = np.cumsum(action_prob)
bit_rate = (action_cumsum > np.random.randint(1, RAND_RANGE) / float(RAND_RANGE)).argmax()
# Note: we need to discretize the probability into 1/RAND_RANGE steps,
# because there is an intrinsic discrepancy in passing single state and batch states
s_batch.append(state)
entropy_record.append(a3c.compute_entropy(action_prob[0]))
if end_of_video:
log_file.write('\n')
log_file.close()
last_bit_rate = DEFAULT_QUALITY
bit_rate = DEFAULT_QUALITY # use the default action here
del s_batch[:]
del a_batch[:]
del r_batch[:]
action_vec = np.zeros(A_DIM)
action_vec[bit_rate] = 1
s_batch.append(np.zeros((S_INFO, S_LEN)))
a_batch.append(action_vec)
entropy_record = []
print("video count", video_count)
video_count += 1
if video_count >= len(all_file_names):
break
log_path = LOG_FILE + '_' + all_file_names[net_env.trace_idx]
log_file = open(log_path, 'wb')
if __name__ == '__main__':
main()