def __init__(self,
history_len=arg_or_default("--history-len", default=10),
features=arg_or_default("--input-features",
default="sent latency inflation," +
"latency ratio," + "send ratio")):
self.viewer = None
self.rand = None
self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
self.sock.setblocking(1)
self.sock.bind(("localhost", 9787))
self.conn = None
self.conn_addr = None
self.features = features.split(",")
self.history_len = history_len
self.history = sender_obs.SenderHistory(history_len, self.features, 0)
self.rate = STARTING_RATE
single_obs_min_vec = sender_obs.get_min_obs_vector(self.features)
single_obs_max_vec = sender_obs.get_max_obs_vector(self.features)
self.observation_space = spaces.Box(np.tile(single_obs_min_vec,
self.history_len),
np.tile(single_obs_max_vec,
self.history_len),
dtype=np.float32)
self.action_space = spaces.Box(np.array([-1e12]),
np.array([1e12]),
dtype=np.float32)
self.steps_taken = 0
self.reward_sum = 0.0
self.reward_ewma = 0.0
def __init__(self,
history_len=arg_or_default("--history-len", default=10),
features=arg_or_default("--input-features",
default="sent latency inflation," +
"latency ratio," + "send ratio")):
self.viewer = None
self.rand = None
self.min_bw, self.max_bw = (100, 500) # bandwidth
self.min_lat, self.max_lat = (0.05, 0.5) # latency
self.min_queue, self.max_queue = (0, 8)
self.min_loss, self.max_loss = (0.0, 0.05)
self.history_len = history_len
print("History length: %d" % history_len)
self.features = features.split(",")
print("Features: %s" % str(self.features))
self.links = None
self.senders = None
self.create_new_links_and_senders()
self.net = Network(self.senders, self.links)
self.run_dur = None
self.run_period = 0.1
self.steps_taken = 0
self.max_steps = MAX_STEPS
self.debug_thpt_changes = False
self.last_thpt = None
self.last_rate = None
# define action space
if USE_CWND:
self.action_space = spaces.Box(np.array([-1e12, -1e12]),
np.array([1e12, 1e12]),
dtype=np.float32)
else:
self.action_space = spaces.Box(np.array([-1e12]),
np.array([1e12]),
dtype=np.float32)
# define observation space
use_only_scale_free = True
single_obs_min_vec = sender_obs.get_min_obs_vector(self.features)
single_obs_max_vec = sender_obs.get_max_obs_vector(self.features)
self.observation_space = spaces.Box(np.tile(single_obs_min_vec,
self.history_len),
np.tile(single_obs_max_vec,
self.history_len),
dtype=np.float32)
self.reward_sum = 0.0
self.reward_ewma = 0.0
self.event_record = {"Events": []}
self.episodes_run = -1
def __init__(self, flow_id):
global RESET_RATE_MIN
global RESET_RATE_MAX
self.id = flow_id
self.rate = random.uniform(RESET_RATE_MIN, RESET_RATE_MAX)
self.history_len = arg_or_default("--history-len", 10)
self.features = arg_or_default("--input-features",
default="sent latency inflation," +
"latency ratio," +
"send ratio").split(",")
self.history = sender_obs.SenderHistory(self.history_len,
self.features, self.id)
self.got_data = False
self.agent = loaded_agent.LoadedModelAgent(MODEL_PATH)
PccGymDriver.flow_lookup[flow_id] = self
#to try after LstmPolicy
from stable_baselines.common.policies import MlpLstmPolicy, MlpLnLstmPolicy
#from stable_baselines import PPO1
from stable_baselines import PPO2
import os
import sys
import inspect
from stable_baselines.common.env_checker import check_env
from common.simple_arg_parse import arg_or_default
currentdir = os.path.dirname(
os.path.abspath(inspect.getfile(inspect.currentframe())))
parentdir = os.path.dirname(currentdir)
sys.path.insert(0, parentdir)
arch_str = arg_or_default("--arch", default="32,16")
if arch_str == "":
arch = []
else:
arch = [int(layer_width) for layer_width in arch_str.split(",")]
print("Architecture for vf and pi is: %s" % str(arch))
#64, 64 is (sort of) the default for the shared layers in LstmPolicy according to
#https://stable-baselines.readthedocs.io/en/master/_modules/stable_baselines/common/policies.html#LstmPolicy
#Also, LSTMs are not supported in the shared part
shared_arch_str = arg_or_default("--shared_arch", default="64,64")
if shared_arch_str == "":
shared_arch = []
else:
shared_arch = [
int(layer_width) for layer_width in shared_arch_str.split(",")
]
# Copyright 2019 Nathan Jay and Noga Rotman
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from common.simple_arg_parse import arg_or_default
DELTA_SCALE = arg_or_default("--delta-scale", 0.05)
import tensorflow as tf
from stable_baselines.common.policies import FeedForwardPolicy
from stable_baselines import PPO1
import os
from shutil import rmtree
import sys
import inspect
currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
parentdir = os.path.dirname(currentdir)
sys.path.insert(0, parentdir)
from common.simple_arg_parse import arg_or_default
# arch_str = arg_or_default("--arch", default="32,16")
arch_str = arg_or_default("--arch", default="64,32")
if arch_str == "":
arch = []
else:
arch = [int(layer_width) for layer_width in arch_str.split(",")]
print("Architecture is: %s" % str(arch))
training_sess = None
env = gym.make('PccNs-v0')
# env = gym.make('CartPole-v0')
gamma = arg_or_default("--gamma", default=0.99)
print("gamma = %f" % gamma)
import network_sim
import tensorflow as tf
from stable_baselines.common.policies import MlpPolicy
from stable_baselines.common.policies import FeedForwardPolicy
from stable_baselines import PPO1
import os
import sys
import inspect
currentdir = os.path.dirname(
os.path.abspath(inspect.getfile(inspect.currentframe())))
parentdir = os.path.dirname(currentdir)
sys.path.insert(0, parentdir)
from common.simple_arg_parse import arg_or_default
arch_str = arg_or_default("--arch", default="32,16")
if arch_str == "":
arch = []
else:
arch = [int(layer_width) for layer_width in arch_str.split(",")]
print("Architecture is: %s" % str(arch))
training_sess = None
class MyMlpPolicy(FeedForwardPolicy):
def __init__(self,
sess,
ob_space,
ac_space,
n_env,
import loaded_agent
if not hasattr(sys, 'argv'):
sys.argv = ['']
MIN_RATE = 0.5
MAX_RATE = 300.0
DELTA_SCALE = 0.05
RESET_RATE_MIN = 5.0
RESET_RATE_MAX = 100.0
RESET_RATE_MIN = 6.0
RESET_RATE_MAX = 6.0
MODEL_PATH = arg_or_default("--model-path", "/tmp/")
for arg in sys.argv:
arg_str = "NULL"
try:
arg_str = arg[arg.rfind("=") + 1:]
except:
pass
if "--reset-target-rate=" in arg:
RESET_RATE_MIN = float(arg_str)
RESET_RATE_MAX = float(arg_str)
class PccGymDriver():
from loaded_agent import LoadedModelAgent
if not hasattr(sys, 'argv'):
sys.argv = ['']
MIN_RATE = 0.5
MAX_RATE = 300.0
DELTA_SCALE = 0.05
RESET_RATE_MIN = 5.0
RESET_RATE_MAX = 100.0
RESET_RATE_MIN = 6.0
RESET_RATE_MAX = 6.0
MODEL_PATH = arg_or_default(
"--model-path", "/home/idoye/PycharmProjects/PCC-RL/icml_paper_model")
for arg in sys.argv:
arg_str = "NULL"
try:
arg_str = arg[arg.rfind("=") + 1:]
except:
pass
if "--reset-target-rate=" in arg:
RESET_RATE_MIN = float(arg_str)
RESET_RATE_MAX = float(arg_str)
class PccGymDriver():
flow_lookup = {}
