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train_ac.py
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import argparse
import os
import numpy as np
import random
from tqdm import tqdm
import torch
import torch.optim as optim
from torch.autograd import Variable
import logging
from model_torch import ActorCritic
from test_torch import valid, test
import env
import load_trace
from replay_memory import ReplayMemory
RANDOM_SEED = 52
S_INFO = 6
S_LEN = 8
A_DIM = 6
LEARNING_RATE = 0.0001
# TRAIN_SEQ_LEN = 100 # take as a train batch
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
# GAMMA = 0.90
# ENTROPY_WEIGHT = 0.99
UPDATE_INTERVAL = 1000
RAND_RANGE = 1000
ENTROPY_EPS = 1e-6
SUMMARY_DIR = './Results/sim/ac'
LOG_FILE = './Results/sim/ac/log'
# TEST_PATH = './models/A3C/BC/360_a3c_240000.model'
parser = argparse.ArgumentParser(description='ac_pytorch')
parser.add_argument('--test', action='store_true', help='Evaluate only')
USE_CUDA = torch.cuda.is_available()
dtype = torch.cuda.FloatTensor if torch.cuda.is_available() else torch.FloatTensor
dlongtype = torch.cuda.LongTensor if torch.cuda.is_available() else torch.LongTensor
def train_ac():
logging.basicConfig(filename=LOG_FILE + '_central',
filemode='w',
level=logging.INFO)
with open(LOG_FILE + '_record', 'w') as log_file, open(LOG_FILE + '_test', 'w') as test_log_file:
# entropy_weight = ENTROPY_WEIGHT
# value_loss_coef = 0.5
torch.manual_seed(RANDOM_SEED)
all_cooked_time, all_cooked_bw, _ = load_trace.load_trace()
# net_env = env.Environment(all_cooked_time=all_cooked_time,
# all_cooked_bw=all_cooked_bw)
model_ac = ActorCritic(A_DIM).type(dtype)
model_ac.train()
optimizer = optim.Adam(model_ac.parameters(), lr=LEARNING_RATE)
# max_grad_norm = MAX_GRAD_NORM
state = np.zeros((S_INFO,S_LEN))
state = torch.from_numpy(state)
last_bit_rate = DEFAULT_QUALITY
# bit_rate = DEFAULT_QUALITY
# action_vec = np.zeros(A_DIM)
# action_vec[bit_rate] = 1
done = True
epoch = 0
time_stamp = 0
agent_num = 16
episode_steps = 20
# update_num = 1
# batch_size = exploration_size * episode_steps #64
gamma = 0.95
gae_param = 0.90
ent_coeff = 2.6
cl_coeff = 0.2
memory = ReplayMemory(agent_num * episode_steps)
env_abr = [env.Environment(all_cooked_time=all_cooked_time,all_cooked_bw=all_cooked_bw) for i in range(agent_num)]
# memory = ReplayMemory()
state_ini = [state for i in range(agent_num)]
last_bit_rate_ini = [last_bit_rate for i in range(agent_num)]
# bit_rate_ini = [bit_rate for i in range(agent_num)]
while True:
for agent in range(agent_num):
states = []
actions = []
rewards_comparison = []
rewards = []
values = []
returns = []
advantages = []
# get initial state and bitrate
state = state_ini[agent]
last_bit_rate = last_bit_rate_ini[agent]
# bit_rate = bit_rate_ini[agent]
for step in range(episode_steps):
prob, v = model_ac(state.unsqueeze(0).type(dtype))
action = prob.multinomial(num_samples=1).detach()
v = v.detach().cpu()
# seed_ = np.random.uniform(0,1)
# if np.random.uniform(0,1) <= exploration_threhold:
# action = random.randint(0, 5)
# action = torch.tensor([[action]]).type(dlongtype)
# else:
# action = prob.multinomial(num_samples=1)
values.append(v)
bit_rate = int(action.squeeze().cpu().numpy())
actions.append(torch.tensor([action]))
states.append(state.unsqueeze(0))
delay, sleep_time, buffer_size, rebuf, \
video_chunk_size, next_video_chunk_sizes, \
end_of_video, video_chunk_remain = \
env_abr[agent].get_video_chunk(bit_rate) ## sample in the environment of virtual player
time_stamp += delay # in ms
time_stamp += sleep_time # in ms
# reward is video quality - rebuffer penalty - smooth penalty
# -- lin scale reward --
# 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)
reward_max = 2.67
reward = float(max(min(reward, reward_max), -10*reward_max) / reward_max)
rewards.append(reward)
rewards_comparison.append(torch.tensor([reward]))
last_bit_rate = bit_rate
# retrieve previous state
if end_of_video:
state = np.zeros((S_INFO, S_LEN))
state = torch.from_numpy(state)
last_bit_rate = DEFAULT_QUALITY
break
# 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)
state = torch.from_numpy(state)
# log time_stamp, bit_rate, buffer_size, reward
log_file.write(str(time_stamp) + '\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()
# restore the initial state
state_ini[agent] = state
last_bit_rate_ini[agent] = last_bit_rate
# one last step
R = torch.zeros(1, 1)
if end_of_video == False:
_, v = model_ac(state.unsqueeze(0).type(dtype))
v = v.detach().cpu()
R = v.data
#================================结束一个ep========================================
# compute returns and GAE(lambda) advantages:
values.append(Variable(R))
R = Variable(R)
A = Variable(torch.zeros(1, 1))
for i in reversed(range(len(rewards))):
td = rewards[i] + gamma * values[i + 1].data[0, 0] - values[i].data[0, 0]
A = float(td) + gamma * gae_param * A
advantages.insert(0, A)
# R = A + values[i]
R = gamma * R + rewards[i]
# R = Variable(R)
returns.insert(0, R)
# store usefull info:
# memory.push([states[1:], actions[1:], rewards_comparison[1:], returns[1:], advantages[1:]])
# memory.push([states[1:], actions[1:], returns[1:], advantages[1:]])
if torch.eq(states[0][0], torch.from_numpy(np.zeros((S_INFO,S_LEN)))).sum() == S_INFO * S_LEN: ## judge if states[0] equals to torch.from_numpy(np.zeros((S_INFO,S_LEN)))
memory.push([states[1:], actions[1:], returns[1:], advantages[1:]])
else:
memory.push([states, actions, returns, advantages])
# policy grad updates:
model_ac.zero_grad()
# mini_batch
batch_size = memory.return_size()
batch_states, batch_actions, batch_returns, batch_advantages = memory.pop(batch_size)
probs_pre, values_pre = model_ac(batch_states.type(dtype))
# actor_loss
prob_value = torch.gather(probs_pre, dim=1, index=batch_actions.unsqueeze(1).type(dlongtype))
policy_loss = -torch.mean(prob_value * batch_advantages.type(dtype))
loss_ent = ent_coeff * torch.mean(probs_pre * torch.log(probs_pre + 1e-5))
actor_loss = policy_loss + loss_ent
# critic_loss
vf_loss = (values_pre - batch_returns.type(dtype)) ** 2 # V_\theta - Q'
critic_loss = cl_coeff * 0.5 * torch.mean(vf_loss)
# update
total_loss = critic_loss + policy_loss + loss_ent
optimizer.zero_grad()
total_loss.backward(retain_graph=True)
optimizer.step()
## test and save the model
epoch += 1
memory.clear()
logging.info('Epoch: ' + str(epoch) +
' Avg_policy_loss: ' + str(policy_loss.detach().cpu().numpy()) +
' Avg_value_loss: ' + str(critic_loss.detach().cpu().numpy()) +
' Avg_entropy_loss: ' + str(loss_ent.detach().cpu().numpy()))
if epoch % UPDATE_INTERVAL == 0:
logging.info("Model saved in file")
valid(model_ac, epoch, test_log_file, SUMMARY_DIR, 'ac')
# entropy_weight = 0.95 * entropy_weight
ent_coeff = 0.95 * ent_coeff
def main():
train_ac()
if __name__ == '__main__':
main()