def run_fourier_sarsa_experiments(transfer_episodes, transfer_epsilon, params):
statistics = {"errors": [], "stopping_points": [], "utilities": []}
filename = RESULTS_DIRECTORY + "fourier-sarsa-transfer-[{}]-[{}]-[{}].json".format(
params["alpha"], params["epsilon"], params["order"])
print("Training on {} with [increment = {}]".format(
PROBLEM_DIRECTORY + PROBLEM_FILES[0][0], PROBLEM_FILES[0][1]))
metareasoning_env = env.Environment(
PROBLEM_DIRECTORY + PROBLEM_FILES[0][0], ALPHA, BETA,
PROBLEM_FILES[0][1])
prakhar = fourier_agent.Agent(metareasoning_env, params)
prakhar.run_sarsa(statistics)
for problem_file in PROBLEM_FILES[1:]:
problem_file_path = PROBLEM_DIRECTORY + problem_file[0]
increment = problem_file[1]
params["episodes"] = transfer_episodes
params["epsilon"] = transfer_epsilon
print("Shifting to {} with [increment = {}]".format(
problem_file_path, increment))
metareasoning_env = env.Environment(problem_file_path, ALPHA, BETA,
increment)
prakhar = fourier_agent.Agent(
metareasoning_env, params, prakhar.function_approximator.weights,
prakhar.function_approximator.action_value_function)
prakhar.run_sarsa(statistics)
utils.save(filename, statistics)
return utils.get_results(statistics["errors"], WINDOW_SIZE,
PLOT_WINDOW_SIZE)
def main():
np.random.seed(42)
os.system('rm ' + TEST_LOG_PATH)
ta_q = Tabular_Q()
all_cooked_time, all_cooked_bw, _ = load_trace.load_trace()
epoch = 0
time_stamp = 0
net_env = env.Environment(all_cooked_time=all_cooked_time,
all_cooked_bw=all_cooked_bw)
last_bit_rate = DEFAULT_QUALITY
bit_rate = DEFAULT_QUALITY
state = [0, 0, 0, 0]
while True:
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 = 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
epoch += 1
bw = float(video_chunk_size) / float(
delay) / M_IN_K * BITS_IN_BYTE # Mbit/sec
bw = min(int(bw / D_BW) * D_BW, BW_MAX)
bf = min(int(buffer_size / D_BF) * D_BF, BF_MAX)
br = bit_rate
c = min(video_chunk_remain, N_CHUNK - 1)
next_state = [bw, bf, br, c]
ta_q.train_q(state, bit_rate, reward, next_state, end_of_video)
state = next_state
last_bit_rate = bit_rate
bit_rate = ta_q.get_q_action(state)
if end_of_video:
last_bit_rate = DEFAULT_QUALITY
bit_rate = DEFAULT_QUALITY
state = [0, 0, 0, 0]
if epoch % TEST_INTERVAL == 0:
testing(ta_q, epoch)
np.save(TEST_LOG_PATH + '_q_table.npy', ta_q.q_table)
def main():
agents = []
for i in range(NUM_AGENTS):
actions = ['defect', 'cooperate']
localRewards = [
np.random.randint(0, high=100),
np.random.randint(0, high=100)
]
agent = env.CitizenAgent(actions, localRewards, i)
agents.append(agent)
leader = env.LeaderAgent(agents)
testEnv = env.Environment(agents, leader, globalRewardFunc)
for _ in range(NUM_EPOCHS):
for _ in range(NUM_STEPS):
actions = testEnv.getActions()
globalReward = testEnv.getRewards(actions)
testEnv.updateQ(globalReward)
leader.penalize()
testEnv.printAgents()
#test stage
actions = testEnv.getActions()
globalReward = testEnv.getRewards(actions)
print("Global Reward: " + str(globalReward))
return 0
def setUp(self):
self.src = source.StringSource()
self.lex = lexer.Lexer(self.src)
self.environment = env.Environment()
self.parser = pars.Parser(self.lex, self.environment)
self.program = interpreter.Interpreter(self.environment, self.parser)
self.output = io.StringIO()
sys.stdout = self.output
def main():
src = source.StreamSource()
fo = open("ThirdExample", "r", encoding='utf-8', newline='\n')
environment = env.Environment()
src.set_data(fo)
lex = lexer.Lexer(src)
parser = pars.Parser(lex, environment)
inter = interpreter.Interpreter(environment, parser)
inter.interpret()
def execute_file(file_name, existing_env=None):
if existing_env is None:
existing_env = environment.Environment()
file_name = existing_env.set_correct_directory(file_name)
if not existing_env.is_already_imported(file_name):
existing_env.add_import(file_name)
try:
execute_program(file_to_str(file_name), existing_env)
except IOError:
print('Could not read file: ' + file_name)
def __init__(self):
self._init_hyperparameters()
self.env = env.Environment()
self.obs_dim = self.env.observation_shape
self.act_dim = self.env.action_space.shape[0]
self.actor = self.create_model(self.act_dim)
self.actor.compile(optimizer=Adam(learning_rate=self.lr))
self.critic = self.create_model(1)
self.critic.compile(optimizer=Adam(learning_rate=self.lr))
self.con_mat = tf.eye(self.act_dim)*0.500
def main():
params = {
"episodes": 2000,
"batch_size": 10,
"gamma": 1,
"learning_rate": 0.01
}
metareasoning_env = env.Environment('problems/test.json', 200, 0.3, 1)
agent = Agent(params, metareasoning_env)
statistics = {"stopping_points": [], "utilities": []}
agent.run_reinforce(statistics)
def __init__(self, random_seed=RANDOM_SEED):
np.random.seed(RANDOM_SEED)
self.action_space = spaces.Discrete(A_DIM)
self.observation_space = spaces.Box(0,
10.0, [S_INFO, S_LEN],
dtype=np.float32)
all_cooked_time, all_cooked_bw, _ = load_trace.load_trace()
self.net_env = env.Environment(all_cooked_time=all_cooked_time,
all_cooked_bw=all_cooked_bw)
self.last_bit_rate = DEFAULT_QUALITY
self.state = np.zeros((S_INFO, S_LEN))
self.reset()
def repl(existing_env=None, get_input=None):
"""
Read-eval-print loop. Whole programs can be run by using
the ':program' directive, ending with ':end'.
Use the 'this' keyword to see the current environment frames.
"""
if existing_env is None:
env = environment.Environment()
else:
env = existing_env
if get_input is None:
get_input = raw_input
brace_matcher = prepare_program.BraceMatcher()
while True:
brace_matcher.reset()
expr = get_input('Capacita> ')
expr = expr.strip()
if expr == 'exit()':
break
elif len(expr) == 0:
continue
elif expr == ':program':
prgm = store_program(get_input)
execute_program(prgm)
elif expr == ':code':
prgm = store_program(get_input)
execute_program(prgm, env)
elif expr.startswith('when ') or is_clause_opener(expr) or \
not brace_matcher.match_line(expr).is_complete():
prgm = store_code_block(get_input, expr, brace_matcher)
if prgm.rstrip('\n').count('\n') == 0 and \
not line_manager.is_statement(prgm):
print_evaluated_expr(prgm, env)
else:
execute_program(prgm, env)
elif expr == 'this':
print(env.frames)
else:
# Since expr could contain semicolon-separated lines of code,
# extract all the lines:
line_mgr, _ = convert_program_to_lines(expr)
line_mgr.classify_statements()
if len(line_mgr) > 1:
leading_lines = line_mgr[:-1]
execution.execute_lines(leading_lines, env)
last_expr_data = line_mgr.get_line_data(-1)
last_expr = last_expr_data.line
if last_expr_data.is_statement:
execution.execute_statement(last_expr_data, False, env)
else:
print_evaluated_expr(last_expr, env)
def run_fourier_q_learning_experiments(params):
statistics = {"errors": [], "stopping_points": [], "utilities": []}
filename = RESULTS_DIRECTORY + "fourier-q-[{}]-[{}]-[{}]-{}".format(
params["alpha"], params["epsilon"], params["order"], PROBLEM_FILE)
metareasoning_env = env.Environment(PROBLEM_FILE_PATH, ALPHA, BETA,
INCREMENT)
prakhar = fourier_agent.Agent(params, metareasoning_env)
prakhar.run_q_learning(statistics)
utils.save(filename, statistics)
return utils.get_results(statistics["errors"], WINDOW_SIZE,
PLOT_WINDOW_SIZE)
def __init__(self, random_seed=RANDOM_SEED):
np.random.seed(RANDOM_SEED)
self.action_space = spaces.Box(low=0.,
high=60.,
shape=(2, ),
dtype=np.float32)
self.observation_space = spaces.Box(0,
10.0, (S_LEN * S_INFO, ),
dtype=np.float32)
all_cooked_time, all_cooked_bw, _ = load_trace.load_trace()
self.net_env = env.Environment(all_cooked_time=all_cooked_time,
all_cooked_bw=all_cooked_bw)
self.last_bit_rate = DEFAULT_QUALITY
self.buffer_size = 0.
self.state = np.zeros((S_INFO, S_LEN))
self.reset()
def test():
metareasoning_env = env.Environment(PROBLEM_FILE_PATH, ALPHA, BETA,
INCREMENT)
quality, time = metareasoning_env.reset()
qualities = [quality]
utility = utils.get_time_dependent_utility(quality, time, ALPHA, BETA)
utilities = [utility]
while True:
(quality, time), _, is_episode_done = metareasoning_env.step(
metareasoning_env.CONTINUE_ACTION)
qualities.append(quality)
utilities.append(
utils.get_time_dependent_utility(quality, time, ALPHA, BETA))
if is_episode_done:
break
plt.figure(figsize=(7, 3))
plt.rcParams["font.family"] = "Times New Roman"
plt.rcParams["font.size"] = 14
plt.rcParams["grid.linestyle"] = "-"
plt.xlabel("Steps")
plt.ylabel("Utilities")
plt.grid(True)
axis = plt.gca()
axis.spines["top"].set_visible(False)
# axis.set_xlim([0, 2 * utilities.index(max(utilities))])
# axis.set_ylim([utilities[0], 1.05 * max(utilities)])
plt.plot(range(len(utilities)), utilities, color="r")
plt.tight_layout()
plt.show()
def main():
"""Main function - includes tests and runs the REPL."""
argc = len(sys.argv)
if argc > 1:
first_arg = sys.argv[1]
if first_arg == '--test':
env = environment.Environment()
execution.execute_statement('x = 3', env)
execution.execute_statement('x+=7', env)
execution.execute_statement('y=9.23', env)
env.new_frame()
execution.execute_statement('x = 5', env)
print(env.frames)
execution.execute_statement('z="hello world"', env)
execution.execute_statement('z +="!!!"', env)
execution.execute_statement('a= `gelatin`', env)
print(env.frames)
ast = AST("3*4+5 ^ 7")
print(ast.parse())
print(ast.collapse_indices(ast.build_indices()))
ast = AST("18+15*9:3+10")
print(ast.parse())
print(ast.collapse_indices(ast.build_indices()))
print(
execution.evaluate_expression('1+2+3+4',
environment.Environment()))
print(
execution.evaluate_expression('45+7*8',
environment.Environment()))
print(
execution.evaluate_expression('3.2+18^2-7',
environment.Environment()))
print(
execution.evaluate_expression('1:2 + 1:3 + 1:5',
environment.Environment()))
print(
execution.evaluate_expression('2:3 + 3^3 - 1:5',
environment.Environment()))
print(
execution.evaluate_expression('1234',
environment.Environment()))
ast = AST("3 + 1 == 4")
print(ast.parse())
ast = AST("3 + 1 > 4")
print(ast.parse())
ast = AST("18:1 != 18.2")
print(ast.parse())
ast = AST("x = 4")
print(ast.parse())
ast = AST("y = 3 > 4")
print(ast.parse())
env2 = environment.Environment()
execution.execute_statement('x = 3+5*4', env2)
execution.execute_statement('y = x + 19 - 3*6', env2)
print(env2.frames)
elif first_arg == '--test2':
ast = AST('x = "ice cream, eggs, and milk" + "...alpha or beta"')
print(ast.parse())
ast = AST('y = f(1 + 1, 2 + 2, 3 + 3) - g((9+7)*2, 128/(2+2))')
print(ast.parse())
ast = AST(
'z = f("ice cream", "eggs and milk") * g("alpha or beta", 3:8, "gamma or delta")'
)
print(ast.parse())
ast = AST('makeList(1,2,3) + makeList(4,5,6)')
print(ast.parse())
ast = AST('[max(16, 25), max(36, max(49, 64))]')
print(ast.parse())
ast = AST('[concat_lists([10], [20]), concat_lists([30], [40])]')
print(ast.parse())
elif first_arg == '--test3':
ast = AST('[1, 2, 3]')
print(ast.split_list_elems())
ast = AST('[f(2), f(3), f(4)]')
print(ast.split_list_elems())
ast = AST('[f(2, 3), f(3, 4, 5), f(4, 1)]')
print(ast.split_list_elems())
ast = AST('1 + 2 * 3')
print(ast.split_list_elems())
print(ast.parse())
elif first_arg == '--test4':
ast = AST('x.length()')
print(ast.parse())
ast = AST('[1,2,3].length()')
print(ast.parse())
ast = AST('3.01')
print(ast.parse())
ast = AST('3.1')
print(ast.parse())
elif first_arg == '--test5':
env = environment.Environment()
env.new_type(['Number'], 'ComplexNumber')
c = {'$type': 'ComplexNumber', 'real': 1, 'imag': 2}
print(env.value_is_a(c, 'ComplexNumber'))
print(env.value_is_a(c, 'Number'))
print(env.value_is_a(c, 'Int'))
print("")
env.new_type(['Object'], 'Food')
env.new_type(['Food'], 'Pizza')
env.new_type(['Food'], 'Dessert')
env.new_type(['Dessert'], 'ChocolateItem')
env.new_type(['Pizza'], 'PepperoniPizza')
env.new_type(['Pizza', 'ChocolateItem'], 'ChocolatePizza')
pepperoni_pizza = {'$type': 'PepperoniPizza'}
chocolate_pizza = {'$type': 'ChocolatePizza'}
print(env.value_is_a(pepperoni_pizza, 'PepperoniPizza'))
print(env.value_is_a(pepperoni_pizza, 'Pizza'))
print(env.value_is_a(pepperoni_pizza, 'Food'))
print(env.value_is_a(pepperoni_pizza, 'Dessert'))
print(env.value_is_a(pepperoni_pizza, 'ChocolateItem'))
print("")
print(env.value_is_a(chocolate_pizza, 'PepperoniPizza'))
print(env.value_is_a(chocolate_pizza, 'Pizza'))
print(env.value_is_a(chocolate_pizza, 'Food'))
print(env.value_is_a(chocolate_pizza, 'Dessert'))
print(env.value_is_a(chocolate_pizza, 'ChocolateItem'))
print("")
env.new_type(['ChocolatePizza'], 'HugeChocolatePizza')
huge_chocolate_pizza = {'$type': 'HugeChocolatePizza'}
print(env.value_is_a(huge_chocolate_pizza, 'PepperoniPizza'))
print(env.value_is_a(huge_chocolate_pizza, 'Pizza'))
print(env.value_is_a(huge_chocolate_pizza, 'Food'))
print(env.value_is_a(huge_chocolate_pizza, 'Dessert'))
print(env.value_is_a(huge_chocolate_pizza, 'ChocolateItem'))
print(env.value_is_a(huge_chocolate_pizza, 'ChocolatePizza'))
print("")
elif first_arg == '--test6':
ast = AST('{1, 2 | 3, 4}')
print(ast.parse())
elif first_arg == '--test7':
ast = AST('throw "something"')
print(ast.parse())
elif first_arg == '--test8':
ast = AST('true and not false')
print(ast.parse())
print(ast.collapse_indices(ast.build_indices()))
elif first_arg == '--test9':
sample = """
x = 5 // comment
// comment
/* multi
line
comment
*/y = 6
z = "https://example.com"
"""
print(prepare_program.preprocess(sample))
elif first_arg == '--test10':
ast = AST('-3.0e5 + 186e-20 * 1e-6 / 28.8e+6 + 34.4e+99')
print(ast.parse())
ast = AST('-3.0E5 + 186E-20 * 1E-6 / 28.8e+6 + 34.4E+99')
print(ast.parse())
elif first_arg == '--test11':
print(execution.is_assignment_statement('a = 5'))
print(execution.is_assignment_statement('a=5==6'))
print(execution.is_assignment_statement('not (5==6) and (8>=7)'))
print(execution.is_assignment_statement('z='))
elif first_arg == '--test12':
lines = [
'sub this + that', 'func Int x + this', 'func x + this',
'func this * y', 'func Int -this', 'sub -this', 'sub not this',
'sub Boolean not this', 'sub this-b', 'sub b-this',
'func Int-this', 'func Int- this', 'sub Int - this'
]
print(prepare_program.replace_op_overload_syntax(lines))
elif first_arg == '--test-tree-merge':
tests.test_tree_merge()
elif first_arg == '--test-all':
tests.test_all('capacita_programs')
elif first_arg == '--test-all-fast':
tests.test_all('capacita_programs', has_delay=False)
elif first_arg == '--test-repl':
tests.test_all('capacita_programs', has_delay=True, use_repl=True)
elif first_arg == '--test-repl-fast':
tests.test_all('capacita_programs', has_delay=False, use_repl=True)
elif first_arg == '--test-file' and argc > 2:
if argc == 4 and sys.argv[2] == '--repl':
tests.test_file(sys.argv[3], use_repl=True)
else:
tests.test_file(sys.argv[2], use_repl=False)
else:
# Run a program from a text file:
file_name = first_arg
execute_file(file_name)
exit()
repl()
def agent(agent_id, net_params_queue, exp_queue):
net_env = env.Environment(random_seed=agent_id,
fixed_env=False,
trace_folder=TRAIN_TRACES)
with tf.Session() as sess, open(LOG_FILE + '_agent_' + str(agent_id),
'wb') as log_file:
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)
# initial synchronization of the network parameters from the coordinator
actor_net_params, critic_net_params = net_params_queue.get()
actor.set_network_params(actor_net_params)
critic.set_network_params(critic_net_params)
mask = net_env.video_masks[net_env.video_idx]
last_bit_rate = DEFAULT_QUALITY
bit_rate = DEFAULT_QUALITY
action = bitrate_to_action(bit_rate, mask)
last_action = action
action_vec = np.zeros(np.sum(mask))
action_vec[bit_rate] = 1
s_batch = [np.zeros((S_INFO, S_LEN))]
a_batch = [action_vec]
r_batch = []
entropy_record = []
time_stamp = 0
while True: # experience video streaming 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, end_of_video, \
video_chunk_remain, video_num_chunks, \
next_video_chunk_size, mask = \
net_env.get_video_chunk(bit_rate)
time_stamp += delay # in ms
time_stamp += sleep_time # in ms
reward = VIDEO_BIT_RATE[action] / M_IN_K \
- REBUF_PENALTY * rebuf \
- SMOOTH_PENALTY * np.abs(VIDEO_BIT_RATE[action] -
VIDEO_BIT_RATE[last_action]) / M_IN_K
r_batch.append(reward)
last_bit_rate = bit_rate
last_action = action
# 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[action] / float(
np.max(VIDEO_BIT_RATE)) # last quality
state[1, -1] = buffer_size / BUFFER_NORM_FACTOR
state[2, -1] = float(video_chunk_size) / float(
delay) / M_IN_K # kilo byte / ms
state[3, -1] = float(delay) / M_IN_K
state[4, -1] = video_chunk_remain / float(video_num_chunks)
state[5, :] = -1
nxt_chnk_cnt = 0
for i in xrange(A_DIM):
if mask[i] == 1:
state[5, i] = next_video_chunk_size[nxt_chnk_cnt] / M_IN_B
nxt_chnk_cnt += 1
assert (nxt_chnk_cnt) == np.sum(mask)
state[6, -A_DIM:] = mask
# compute action probability vector
action_prob = actor.predict(np.reshape(state, (1, S_INFO, S_LEN)))
# the action probability should correspond to number of bit rates
assert len(action_prob[0]) == np.sum(mask)
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
action = bitrate_to_action(bit_rate, mask)
entropy_record.append(a3c.compute_entropy(action_prob[0]))
# log time_stamp, bit_rate, buffer_size, reward
log_file.write(
str(time_stamp) + '\t' + str(VIDEO_BIT_RATE[action]) + '\t' +
str(buffer_size) + '\t' + str(rebuf) + '\t' +
str(video_chunk_size) + '\t' + str(delay) + '\t' +
str(reward) + '\n')
log_file.flush()
# report experience to the coordinator
if len(r_batch) >= TRAIN_SEQ_LEN or end_of_video:
exp_queue.put([
s_batch[1:], # ignore the first chuck
a_batch[1:], # since we don't have the
r_batch[1:], # control over it
end_of_video,
{
'entropy': entropy_record
}
])
# synchronize the network parameters from the coordinator
actor_net_params, critic_net_params = net_params_queue.get()
actor.set_network_params(actor_net_params)
critic.set_network_params(critic_net_params)
del s_batch[:]
del a_batch[:]
del r_batch[:]
del entropy_record[:]
log_file.write(
'\n') # so that in the log we know where video ends
# store the state and action into batches
if end_of_video:
last_bit_rate = DEFAULT_QUALITY
bit_rate = DEFAULT_QUALITY # use the default action here
action = bitrate_to_action(bit_rate, mask)
last_action = action
action_vec = np.zeros(np.sum(mask))
action_vec[bit_rate] = 1
s_batch.append(np.zeros((S_INFO, S_LEN)))
a_batch.append(action_vec)
else:
s_batch.append(state)
action_vec = np.zeros(np.sum(mask))
action_vec[bit_rate] = 1
a_batch.append(action_vec)
import json
from collections import defaultdict
import git
import env
import log
from base import ProcessName, CommitBuilder, iter_tree, iter_process_names
from repos import pygit2_get
logger = log.get_logger(__name__)
env = env.Environment()
class Index(object):
name = None
key_fields = ()
unique = False
value_cls = tuple
# Overridden in subclasses using the constructor
# This provides a kind of borg pattern where all instances of
# the class have the same changes data
changes = None
def __init__(self, repo):
if self.__class__.changes is None:
self.reset()
self.repo = repo
self.pygit2_repo = pygit2_get(repo)
def main():
np.random.seed(RANDOM_SEED)
assert len(VIDEO_BIT_RATE) == A_DIM
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, 'w')
actor = a2c_torch.ActorNet(s_dim=[S_INFO, S_LEN],
a_dim=A_DIM,
lr=ACTOR_LR_RATE)
critic = a2c_torch.CriticNet(s_dim=[S_INFO, S_LEN], lr=CRITIC_LR_RATE)
# restore neural net parameters
if NN_MODEL is not None: # NN_MODEL is the path to file
# saver.restore(sess, NN_MODEL)
print(NN_MODEL)
actor.load_state_dict(torch.load(NN_MODEL))
print("Testing 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
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.get_actor_out(
convert_torch(np.reshape(state, (1, S_INFO, S_LEN))))
action_prob = action_prob.numpy()
action_cumsum = np.cumsum(action_prob)
bit_rate = (action_cumsum > np.random.randint(1, RAND_RANGE) /
float(RAND_RANGE)).argmax()
# action_prob = actor.predict(np.reshape(state, (1, S_INFO, S_LEN)))
# action_cumsum = np.cumsum(action_prob)
# print('action:', action_cumsum)
# bit_rate = (action_cumsum > np.random.randint(1, RAND_RANGE) / float(RAND_RANGE)).argmax()
# bit_rate = np.argmax(action_prob)
# 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(a2c_torch.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 = []
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, 'w')
def local_train(index, args, global_model, actor_optimizer, critic_optimizer, save=False):
torch.manual_seed(614 + index)
if save:
start_time = timeit.default_timer()
writer = SummaryWriter(args.log_path)
all_cooked_time, all_cooked_bw, _ = load_trace.load_trace(args.train_traces)
net_env = env.Environment(all_cooked_time=all_cooked_time,
all_cooked_bw=all_cooked_bw,
random_seed=index)
local_model = a3c.ActorCritic(state_dim=[S_INFO, S_LEN],
action_dim=A_DIM,
learning_rate=[ACTOR_LR_RATE, CRITIC_LR_RATE],
islstm = args.islstm)
# local_model = a3c.A3C(state_dim=[S_INFO, S_LEN],
# action_dim=A_DIM,
# learning_rate=[ACTOR_LR_RATE, CRITIC_LR_RATE])
local_model.train()
local_model._initialize_weights()
if args.use_gpu:
local_model.cuda()
done = True
curr_step = 0
curr_episode = 0
last_bit_rate = DEFAULT_QUALITY
bit_rate = DEFAULT_QUALITY
time_stamp = 0
interval_aloss = 0
interval_closs = 0
interval_entropy = 0
interval_reward = []
sum_reward = 0
count_reware = 0
while True:
curr_episode += 1
local_model.load_state_dict(global_model.state_dict())
state = torch.zeros(S_INFO, S_LEN)
if done:
cx = torch.zeros(1, 128)
hx = torch.zeros(1, 128)
else:
cx = cx.detach()
hx = hx.detach()
if args.use_gpu:
state = state.cuda()
log_policies = []
values = []
rewards = []
entropies = []
# One video
while True:
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
# -- linear reward --
# 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[-1]))
# log_last_bit_rate = np.log(VIDEO_BIT_RATE[last_bit_rate] / float(VIDEO_BIT_RATE[-1]))
# reward = log_bit_rate \
# - REBUF_PENALTY * rebuf \
# - SMOOTH_PENALTY * np.abs(log_bit_rate - log_last_bit_rate)
# -- HD reward --
# reward = HD_REWARD[bit_rate] \
# - REBUF_PENALTY * rebuf \
# - SMOOTH_PENALTY * np.abs(HD_REWARD[bit_rate] - HD_REWARD[last_bit_rate])
last_bit_rate = bit_rate
state = torch.roll(state, -1)
# Fill in the state vector with normalization
state[0, -1] = torch.Tensor([VIDEO_BIT_RATE[last_bit_rate] / float(max(VIDEO_BIT_RATE))]) # last quality
state[1, -1] = torch.Tensor([buffer_size / BUFFER_NORM_FACTOR]) # buffer size
state[2, -1] = torch.Tensor([float(video_chunk_size) / float(delay) / M_IN_K]) # kilo byte / ms
state[3, -1] = torch.Tensor([float(delay) / M_IN_K / BUFFER_NORM_FACTOR]) # /10 sec
state[4, :A_DIM] = torch.Tensor([next_video_chunk_sizes]) / M_IN_K / M_IN_K # mega byte
# remaining chunk number
state[5, -1] = torch.Tensor([min(video_chunk_remain, CHUNK_TIL_VIDEO_END_CAP) / float(CHUNK_TIL_VIDEO_END_CAP)])
if args.islstm == 0:
logits, value = local_model(state.unsqueeze(dim=0))
else:
logits, value, hx, cx = local_model((state.unsqueeze(dim=0),hx,cx))
# print(f"index {index}, state {state}, logits {logits}, value {value}",sep="\n")
# print(state,logits)
try:
cate = Categorical(logits)
bit_rate = cate.sample().item()
except Exception as e:
print(e)
print(f"walking into an error of all null distribution in step {curr_step}")
print(logits, state)
exit()
policy = logits
log_policy = torch.log(logits)
entropy = (policy * log_policy).sum(1, keepdim=True)
if curr_step > args.num_global_steps:
done = True
curr_step += 1
values.append(value)
rewards.append(reward)
log_policies.append(log_policy[0, bit_rate])
entropies.append(entropy)
if end_of_video:
last_bit_rate = DEFAULT_QUALITY
bit_rate = DEFAULT_QUALITY # use the default action here
break
score = torch.zeros((1, 1), dtype=torch.float)
if args.use_gpu:
score = score.cuda()
if not done:
_, score = local_model(state.unsqueeze(dim=0))
gae = torch.zeros((1, 1), dtype=torch.float)
if args.use_gpu:
gae = gae.cuda()
actor_loss = 0
critic_loss = 0
entropy_loss = 0
next_value = score
# for value, log_policy, reward, entropy in list(zip(values, log_policies, rewards, entropies))[::-1]:
# gae = gae * args.gamma * args.tau
# gae = gae + reward + args.gamma * next_value.detach() - value.detach()
# next_value = value
# actor_loss = actor_loss + log_policy * gae
# score = score * args.gamma + reward
# critic_loss = critic_loss + (score - value) ** 2 / 2
# entropy_loss = entropy_loss + entropy
for value, log_policy, reward, entropy in list(zip(values, log_policies, rewards, entropies))[::-1]:
gae = gae * args.gamma * args.tau
gae = gae + reward + args.gamma * next_value.detach() - value.detach()
next_value = value
actor_loss = actor_loss + log_policy * gae
score = score * args.gamma + reward
critic_loss = critic_loss + (score - value) ** 2 / 2
entropy_loss = entropy_loss + entropy
entropy_loss = args.beta * (entropy_loss )
actor_loss = -actor_loss + args.beta * entropy_loss
# total_loss = -actor_loss + critic_loss - entropy_loss
writer.add_scalar("Train_{}/Loss".format(index), actor_loss, critic_loss, curr_episode)
actor_optimizer.zero_grad()
critic_optimizer.zero_grad()
actor_loss.backward()
critic_loss.backward()
# gradient clipping
torch.nn.utils.clip_grad_norm_(global_model.parameters(), args.max_grad_norm)
# total_loss.backward()
# (-critic_loss).backward()
# (actor_loss+args.beta*entropy_loss).backward()
for local_param, global_param in zip(local_model.parameters(), global_model.parameters()):
if global_param.grad is not None:
break
global_param._grad = local_param.grad
actor_optimizer.step()
critic_optimizer.step()
interval_aloss += actor_loss.data.item()
interval_closs += critic_loss.data.item()
interval_entropy += entropy_loss.data.item()
interval_reward.append(np.sum(rewards))
if curr_episode % print_interval == 0 :
print("---------")
print(f"Process {index}, episode {curr_episode}\n"+
f"actor_loss [{interval_aloss/print_interval:4f}] "
f"critic_loss [{interval_closs/print_interval:4f}] "
f"entropy [{interval_entropy/print_interval:4f}]\n"
f"reward [{interval_reward}]")
if save and curr_episode % args.save_interval == 0 and curr_episode > 0:
torch.save(global_model.state_dict(),
f"{args.saved_path}/a3c_{curr_episode}_reward_{sum_reward/count_reware:4f}.pkl")
sum_reward += np.sum(interval_reward)
count_reware += 1
interval_aloss = 0
interval_closs = 0
interval_entropy = 0
interval_reward = []
if curr_episode == int(args.num_global_steps / args.num_local_steps):
print("Training process {} terminated".format(index))
if save:
end_time = timeit.default_timer()
print('The code runs for %.2f s ' % (end_time - start_time))
return
c_ent=opts["c_ent"])
# OPTIONAL, LOAD AGENT
if "load" in opts.keys():
model.load(path=opts["load"], ext="_last")
# INITIALIZE ROSNODE
rospy.init_node("training_node", anonymous=True)
rate = rospy.Rate(100)
rospy.sleep(1.0)
robot = torobo_wrapper.Torobo()
rospy.sleep(1.0)
# INITIALIZE ENVIRONMENT
world = env.Environment(robot=robot,
objects=opts["objects"],
rng_ranges=opts["ranges"])
rospy.sleep(0.5)
world.initialize()
print("=" * 10 + "POLICY NETWORK" + "=" * 10)
print(model.policy)
print("=" * 10 + "VALUE NETWORK" + "=" * 10)
print(model.value)
print("Training starts...")
reward_history = []
temp_history = []
it = 0
update_count = 0
while update_count < opts["episode"]:
random_seed = 2
video_count = 0
FPS = 25
frame_time_len = 0.04
#init the environment
#setting one:
# 1,all_cooked_time : timestamp
# 2,all_cooked_bw : throughput
# 3,all_cooked_rtt : rtt
# 4,agent_id : random_seed
# 5,logfile_path : logfile_path
# 6,VIDEO_SIZE_FILE : Video Size File Path
# 7,Debug Setting : Debug
net_env = env.Environment(all_cooked_time=all_cooked_time,
all_cooked_bw=all_cooked_bw,
random_seed=random_seed,
logfile_path=LogFile_Path,
VIDEO_SIZE_FILE=video_size_file,
Debug=DEBUG)
BIT_RATE = [500.0, 850.0, 1200.0, 1850.0] # kpbs
TARGET_BUFFER = [2.0, 3.0] # seconds
# ABR setting
RESEVOIR = 0.5
CUSHION = 2
cnt = 0
# defalut setting
last_bit_rate = 0
bit_rate = 0
target_buffer = 0
# QOE setting
def agent(agent_id, all_cooked_time, all_cooked_bw, net_params_queue,
exp_queue, epoch_queue):
net_env = env.Environment(all_cooked_time=all_cooked_time,
all_cooked_bw=all_cooked_bw,
random_seed=agent_id)
with tf.Session() as sess, open(LOG_FILE + '_agent_' + str(agent_id),
'wb') as log_file:
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)
# 1.从center同步最新的模型参数 initial synchronization of the network parameters from the coordinator
actor_net_params, critic_net_params = net_params_queue.get()
epoch_num = epoch_queue.get()
actor.set_network_params(actor_net_params)
critic.set_network_params(critic_net_params)
last_bit_rate = DEFAULT_QUALITY
bit_rate = DEFAULT_QUALITY
action_vec = np.zeros(A_DIM) #初始化 动作空间A个actions
action_vec[bit_rate] = 1
s_batch = [np.zeros((S_INFO, S_LEN))]
a_batch = [action_vec]
r_batch = []
entropy_record = []
time_stamp = 0
while True: # experience video streaming forever
# 和环境Env交互 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)
assert bit_rate >= 0
assert bit_rate < A_DIM
bitrate_send_last, lossrate_recv_last, bitrate_real_recovery,\
bitrate_send_last_probe, lossrate_recv_last_probe, bitrate_real_recovery_probe,\
end_of_video \
= net_env.action_dispatch_and_report_svr(VIDEO_BIT_RATE[bit_rate])
time_stamp += 2
# -- linear reward --
# reward is video quality - rebuffer penalty - smoothness
#print '1', net_env.netbw
#print '2', bitrate_send_last_probe * (1 - lossrate_recv_last_probe)
x_funtion_top = (bitrate_send_last_probe *
(1 - lossrate_recv_last_probe) -
VIDEO_BIT_RATE[bit_rate]) / M_IN_K
reward = -x_funtion_top * x_funtion_top # 0.1 0.2 ... 1.1 1.2
r_batch.append(reward)
last_bit_rate = bit_rate
# 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] = bitrate_send_last / 1000.0 # last quality
#state[1, -1] = lossrate_recv_last # 丢包率0.1 0.2 0.3 0.4
#state[2, -1] = bitrate_real_recovery / 1000.0 # kilo byte / ms
state = np.roll(state, -1, axis=1)
state[0, -1] = bitrate_send_last_probe / 1000.0 # last quality
state[1, -1] = lossrate_recv_last_probe # 丢包率0.1 0.2 0.3 0.4
state[2,
-1] = bitrate_real_recovery_probe / 1000.0 # kilo byte / ms
state[3, :A_DIM] = np.array(
VIDEO_BIT_RATE[:]) / 1000.0 # kilo byte / ms
state[4, -1] = bitrate_send_last / 1000.0 # kilo byte / ms
# print state[3, :A_DIM]
# ================== Predict BandWidth =========================
# compute action probability vector
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
entropy_record.append(a3c.compute_entropy(action_prob[0]))
# log time_stamp, bit_rate, buffer_size, reward
log_file.write(
str(time_stamp) + '\t' + str(VIDEO_BIT_RATE[bit_rate]) + '\t' +
str(bitrate_send_last) + '\t' + str(lossrate_recv_last) +
'\t' + str(bitrate_real_recovery) + '\t' + str(reward) + '\n')
log_file.flush()
# report experience to the coordinator
if len(r_batch) >= TRAIN_SEQ_LEN or end_of_video:
exp_queue.put([
s_batch[1:], # ignore the first chuck
a_batch[1:], # since we don't have the
r_batch[1:], # control over it
end_of_video,
{
'entropy': entropy_record
}
])
# synchronize the network parameters from the coordinator
actor_net_params, critic_net_params = net_params_queue.get()
actor.set_network_params(actor_net_params)
critic.set_network_params(critic_net_params)
epoch_num = epoch_queue.get()
del s_batch[:]
del a_batch[:]
del r_batch[:]
del entropy_record[:]
log_file.write(
'\n') # so that in the log we know where video ends
# store the state and action into batches
if end_of_video:
last_bit_rate = DEFAULT_QUALITY
bit_rate = DEFAULT_QUALITY # use the default action here
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)
else:
s_batch.append(state)
action_vec = np.zeros(A_DIM)
action_vec[bit_rate] = 1
a_batch.append(action_vec)
def main():
np.random.seed(RANDOM_SEED)
assert len(VIDEO_BIT_RATE) == A_DIM
all_cooked_time, all_cooked_bw, _ = load_trace.load_trace()
if not os.path.exists(SUMMARY_DIR):
os.makedirs(SUMMARY_DIR)
net_env = env.Environment(all_cooked_time=all_cooked_time,
all_cooked_bw=all_cooked_bw)
with tf.Session() as sess, open(LOG_FILE, 'wb') as log_file:
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)
summary_ops, summary_vars = a3c.build_summaries()
sess.run(tf.global_variables_initializer())
writer = tf.summary.FileWriter(SUMMARY_DIR,
sess.graph) # training monitor
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.")
epoch = 0
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 = []
actor_gradient_batch = []
critic_gradient_batch = []
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 - smooth penalty
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
r_batch.append(reward)
last_bit_rate = bit_rate
# 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
entropy_record.append(a3c.compute_entropy(action_prob[0]))
# 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()
if len(r_batch
) >= TRAIN_SEQ_LEN or end_of_video: # do training once
actor_gradient, critic_gradient, td_batch = \
a3c.compute_gradients(s_batch=np.stack(s_batch[1:], axis=0), # ignore the first chuck
a_batch=np.vstack(a_batch[1:]), # since we don't have the
r_batch=np.vstack(r_batch[1:]), # control over it
terminal=end_of_video, actor=actor, critic=critic)
td_loss = np.mean(td_batch)
actor_gradient_batch.append(actor_gradient)
critic_gradient_batch.append(critic_gradient)
print "===="
print "Epoch", epoch
print "TD_loss", td_loss, "Avg_reward", np.mean(
r_batch), "Avg_entropy", np.mean(entropy_record)
print "===="
summary_str = sess.run(summary_ops,
feed_dict={
summary_vars[0]: td_loss,
summary_vars[1]: np.mean(r_batch),
summary_vars[2]:
np.mean(entropy_record)
})
writer.add_summary(summary_str, epoch)
writer.flush()
entropy_record = []
if len(actor_gradient_batch) >= GRADIENT_BATCH_SIZE:
assert len(actor_gradient_batch) == len(
critic_gradient_batch)
# assembled_actor_gradient = actor_gradient_batch[0]
# assembled_critic_gradient = critic_gradient_batch[0]
# assert len(actor_gradient_batch) == len(critic_gradient_batch)
# for i in xrange(len(actor_gradient_batch) - 1):
# for j in xrange(len(actor_gradient)):
# assembled_actor_gradient[j] += actor_gradient_batch[i][j]
# assembled_critic_gradient[j] += critic_gradient_batch[i][j]
# actor.apply_gradients(assembled_actor_gradient)
# critic.apply_gradients(assembled_critic_gradient)
for i in xrange(len(actor_gradient_batch)):
actor.apply_gradients(actor_gradient_batch[i])
critic.apply_gradients(critic_gradient_batch[i])
actor_gradient_batch = []
critic_gradient_batch = []
epoch += 1
if epoch % MODEL_SAVE_INTERVAL == 0:
# Save the neural net parameters to disk.
save_path = saver.save(
sess, SUMMARY_DIR + "/nn_model_ep_" + str(epoch) +
".ckpt")
print("Model saved in file: %s" % save_path)
del s_batch[:]
del a_batch[:]
del r_batch[:]
if end_of_video:
last_bit_rate = DEFAULT_QUALITY
bit_rate = DEFAULT_QUALITY # use the default action here
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)
else:
s_batch.append(state)
action_vec = np.zeros(A_DIM)
action_vec[bit_rate] = 1
a_batch.append(action_vec)
import env
import numpy as np
e = env.Environment(8, 8)
# 4(a)
def valueIteration(e, pe, l):
pi = dict()
V = np.zeros((e.L, e.W, 12))
threshold = 1e-4
while True:
delta = 0
for s in e.getStates():
vmax = -np.Inf
for a in e.getActions():
v = env.expectation(e, s, a, V, pe, l)
if v > vmax :
vmax = v
pi[s] = a
delta = max(delta, np.abs(V[s] - vmax))
V[s] = vmax
print(delta)
if delta < threshold:
break
return pi, V
# 4(b)
pistar, V = valueIteration(e, 0, 0.9)
tr = env.gen_traj(e, pistar, (1, 6, 6), 0)
# 4(c)
parser.add_argument('--mode', '-m')
parser.add_argument('--model')
#no training episodes
parser.add_argument('--eps')
parser.add_argument('--render')
args = parser.parse_args()
if args.tensorboard:
writer = SummaryWriter()
write_proc = subprocess.Popen(['tensorboard', '--logdir', '{}'.format(args.tensorboard)])
env = env.Environment(args.env)
if args.alg == 'DQN':
agent = agent.DQNAgent(env, args.mode, args.model, writer)
try:
if args.mode == 'train':
agent.train(int(args.eps), args.render)
elif args.mode == 'play':
agent.play(int(args.eps))
except KeyboardInterrupt:
print('PROCESS KILLED BY USER')
finally:
env.close()
if args.tensorboard:
write_proc.terminate()
def agent(agent_id, all_cooked_time, all_cooked_bw, net_params_queue,
exp_queue):
net_env = env.Environment(time=all_cooked_time,
bandwidth=all_cooked_bw,
random_seed=agent_id)
with tf.Session() as sess, open(LOG_FILE + '_agent_' + str(agent_id),
'wb') as log_file:
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)
# initial synchronization of the network parameters from the coordinator
actor_net_params, critic_net_params = net_params_queue.get()
actor.set_network_params(actor_net_params)
critic.set_network_params(critic_net_params)
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 = []
#need to initialize, and get before simulation step
track_index = []
hm = head_movement.move_prediction()
time_stamp = 0
while True: # experience video streaming forever
# the action is from the last decision
# this is to make the framework similar to the real
# xgw 20180918: need to modify here
estimate_track_index = hm.get_head_movement_prediction()
# actual_track_index = hm.get_head_movement_current()
actual_track_index = [2, 3, 5, 6]
delay, rebuf, buffer_size, sleep_time, video_chunk_size, end_of_video = \
net_env.get_video_chunk(bit_rate, estimate_track_index)
time_stamp += delay # in ms
time_stamp += sleep_time # in ms
# -- linear reward --
# reward is video quality - rebuffer penalty - smoothness
# xgw 20180918: need to modify the reward, add the qualiy consistency in viewport
# and the buffer
# actually the consistency of quality in viewport is the error of head movement prediction error
# so it's not sure that whether add the "quality consistency" here
# don't know how to modelized the qp as first input
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
# bit_rate_log_reward = np.log((bit_rate + 1) / A_DIM) * BIT_RATE_REWARD_PARAMETER
# smooth_p = np.exp(np.abs(last_bit_rate - bit_rate) / A_DIM) * SMOOTH_PENALTY
# reward = bit_rate - REBUF_PENALTY * rebuf - smooth_p
r_batch.append(reward)
last_bit_rate = bit_rate
# 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 # 6 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[0, -1] = float(video_chunk_size) / float(
delay) / M_IN_K # kilo byte / ms
state[1, -1] = buffer_size / BUFFER_NORM_FACTOR # 6 sec
state[2, :4] = np.array(actual_track_index)
state[3, -1] = VIDEO_BIT_RATE[bit_rate] / float(
np.max(VIDEO_BIT_RATE)) # last chunk's bitrate
# compute action probability vector
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
entropy_record.append(a3c.compute_entropy(action_prob[0]))
# log time_stamp, bit_rate, buffer_size, reward
log_file.write('time_stamp: ' + str(time_stamp) + '\t' +
'VIDEO_BIT_RATE: ' + str(VIDEO_BIT_RATE[bit_rate]) +
'\t' + 'buffer_size: ' + str(buffer_size) + '\t' +
'rebuf: ' + str(rebuf) + '\t' +
'video_chunk_size: ' + str(video_chunk_size) +
'\t' + 'delay: ' + str(delay) + '\t' +
'avg throughtput: ' +
str(video_chunk_size / delay) + '\t' + 'reward: ' +
str(reward) + '\n')
log_file.flush()
# report experience to the coordinator
if len(r_batch) >= TRAIN_SEQ_LEN or end_of_video:
exp_queue.put([
s_batch[1:], # ignore the first chuck
a_batch[1:], # since we don't have the
r_batch[1:], # control over it
end_of_video,
{
'entropy': entropy_record
}
])
# synchronize the network parameters from the coordinator
actor_net_params, critic_net_params = net_params_queue.get()
actor.set_network_params(actor_net_params)
critic.set_network_params(critic_net_params)
del s_batch[:]
del a_batch[:]
del r_batch[:]
del entropy_record[:]
log_file.write(
'\n') # so that in the log we know where video ends
# store the state and action into batches
if end_of_video:
last_bit_rate = DEFAULT_QUALITY
bit_rate = DEFAULT_QUALITY # use the default action here
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)
else:
s_batch.append(state)
action_vec = np.zeros(A_DIM)
action_vec[bit_rate] = 1
a_batch.append(action_vec)
parser = argparse.ArgumentParser("Record states.")
parser.add_argument("-s", help="state file", type=str, required=True)
parser.add_argument("-o", help="output folder", type=str, required=True)
args = parser.parse_args()
if not os.path.exists(args.o):
os.makedirs(args.o)
# INITIALIZE ROSNODE
rospy.init_node("test_node", anonymous=True)
rate = rospy.Rate(100)
rospy.sleep(1.0)
robot = torobo_wrapper.Torobo()
rospy.sleep(1.0)
# INITIALIZE ENVIRONMENT
objects = ["target_plate", "small_cube"]
random_ranges = {
"target_plate": np.array([[0.32, 0.52], [0.30, 0.50], [1.125, 1.125]]),
"small_cube": np.array([[0.32, 0.52], [0.0, 0.15], [1.155, 1.155]]),
}
world = env.Environment(robot=robot, objects=objects, rng_ranges=random_ranges)
rospy.sleep(0.5)
states = torch.load(args.s)
for i, s in enumerate(states):
s = s.tolist()
world.set_model_state("target_plate", s[:3], s[3:7])
world.set_model_state("small_cube", s[7:10], s[10:14])
os.system("import -window Gazebo %s/%d.jpg" % (args.o, i))
def agent(agent_id, all_cooked_time, all_cooked_bw, net_params_queue, exp_queue):
net_env = env.Environment(all_cooked_time=all_cooked_time,
all_cooked_bw=all_cooked_bw,
random_seed=agent_id)
with tf.Session() as sess, open(LOG_FILE + '_agent_' + str(agent_id), 'w') as log_file:
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)
# initial synchronization of the network parameters from the coordinator
actor_net_params, critic_net_params = net_params_queue.get()
actor.set_network_params(actor_net_params)
critic.set_network_params(critic_net_params)
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 = []
time_stamp = 0
while True: # experience video streaming 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
# -- linear reward --
# 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[-1]))
#log_last_bit_rate = np.log(VIDEO_BIT_RATE[last_bit_rate] / float(VIDEO_BIT_RATE[-1]))
#reward = log_bit_rate \
# - REBUF_PENALTY * rebuf \
# - SMOOTH_PENALTY * np.abs(log_bit_rate - log_last_bit_rate)
# -- HD reward --
reward = HD_REWARD[bit_rate] \
- REBUF_PENALTY * rebuf \
- SMOOTH_PENALTY * np.abs(HD_REWARD[bit_rate] - HD_REWARD[last_bit_rate])
r_batch.append(reward)
last_bit_rate = bit_rate
# 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)
# compute action probability vector
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
entropy_record.append(a3c.compute_entropy(action_prob[0]))
# 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()
# report experience to the coordinator
if len(r_batch) >= TRAIN_SEQ_LEN or end_of_video:
exp_queue.put([s_batch[1:], # ignore the first chuck
a_batch[1:], # since we don't have the
r_batch[1:], # control over it
end_of_video,
{'entropy': entropy_record}])
# synchronize the network parameters from the coordinator
actor_net_params, critic_net_params = net_params_queue.get()
actor.set_network_params(actor_net_params)
critic.set_network_params(critic_net_params)
del s_batch[:]
del a_batch[:]
del r_batch[:]
del entropy_record[:]
log_file.write('\n') # so that in the log we know where video ends
# store the state and action into batches
if end_of_video:
last_bit_rate = DEFAULT_QUALITY
bit_rate = DEFAULT_QUALITY # use the default action here
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)
else:
s_batch.append(state)
action_vec = np.zeros(A_DIM)
action_vec[bit_rate] = 1
a_batch.append(action_vec)
def agent(agent_id, all_cooked_time, all_cooked_bw, net_params_queue,
exp_queue, model_type):
torch.set_num_threads(1)
net_env = env.Environment(all_cooked_time=all_cooked_time,
all_cooked_bw=all_cooked_bw,
random_seed=agent_id)
with open(LOG_FILE + '_agent_' + str(agent_id), 'w') as log_file:
net = A3C(NO_CENTRAL, model_type, [S_INFO, S_LEN], A_DIM,
ACTOR_LR_RATE, CRITIC_LR_RATE)
# initial synchronization of the network parameters from the coordinator
time_stamp = 0
for epoch in range(TOTALEPOCH):
actor_net_params = net_params_queue.get()
net.hardUpdateActorNetwork(actor_net_params)
last_bit_rate = DEFAULT_QUALITY
bit_rate = DEFAULT_QUALITY
s_batch = []
a_batch = []
r_batch = []
entropy_record = []
state = torch.zeros((1, S_INFO, S_LEN))
# 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
while not end_of_video and len(s_batch) < TRAIN_SEQ_LEN:
last_bit_rate = bit_rate
state = state.clone().detach()
state = torch.roll(state, -1, dims=-1)
state[0, 0, -1] = VIDEO_BIT_RATE[bit_rate] / float(
np.max(VIDEO_BIT_RATE)) # last quality
state[0, 1, -1] = buffer_size / BUFFER_NORM_FACTOR # 10 sec
state[0, 2, -1] = float(video_chunk_size) / float(
delay) / M_IN_K # kilo byte / ms
state[
0, 3,
-1] = float(delay) / M_IN_K / BUFFER_NORM_FACTOR # 10 sec
state[0, 4, :A_DIM] = torch.tensor(
next_video_chunk_sizes) / M_IN_K / M_IN_K # mega byte
state[0, 5, -1] = min(
video_chunk_remain,
CHUNK_TIL_VIDEO_END_CAP) / float(CHUNK_TIL_VIDEO_END_CAP)
bit_rate = net.actionSelect(state)
# 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
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)
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
s_batch.append(state)
a_batch.append(bit_rate)
r_batch.append(reward)
entropy_record.append(3)
# 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()
exp_queue.put([
s_batch, # ignore the first chuck
a_batch, # since we don't have the
r_batch, # control over it
end_of_video,
{
'entropy': entropy_record
}
])
log_file.write('\n') # so that in the log we know where video ends
args = parser.parse_args()
# loading/problem
load = HalfBeam(nelx, nely)
# optimizer
verbose = True
fesolver = CooFESolver(verbose=verbose)
optimizer = None
density_constraint = None
if args.optimizer:
if str(args.optimizer) == 'mas':
optimizer = env.Environment(fesolver,
young,
poisson,
verbose=verbose)
# constraints
density_constraint = DensityConstraint(volume_frac=1.0,
density_min=xmin,
density_max=xmax)
if str(args.optimizer) == 'mas_ke':
optimizer = env_ke.Environment(fesolver,
young,
poisson,
verbose=verbose)
# constraints
density_constraint = DensityConstraint(volume_frac=1.0,
density_min=xmin,
density_max=xmax)
if str(args.optimizer) == 'oc':
if done:
self.dispatch.stop()
self.env.reset()
self.solver.experience_replay(iteration, self.episodes)
self.solver.save_model()
if (self.episodes % 2 == 0):
self.solver.update_model()
self.episodes += 1
# #######################
# # Main control center #
# #######################
# This object centralizes everything
theDispatcher = dispatcher.Dispatcher()
# Provide a new environment (maze + agent)
theDispatcher.setEnvironment(env.Environment(12, 8, 15))
# Provide also the simulation stepper, which needs access to the
# agent and maze in the dispatcher.
theDispatcher.setStepper(TrainSolver(theDispatcher))
# Start the GUI and run it until quit is selected
# (Remember Ctrl+\ forces python to quit, in case it is necessary)
theDispatcher.run()
