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#This is a code implementation of the Reinforcement learning based TCP congestion control
#Importing the neccssary libaraies
import gym
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
#atking the current directory from the filename
currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
#making the current directory as parent directory
parentdir = os.path.dirname(currentdir)
#adding the parent directory to the system path
sys.path.insert(0,parentdir)
# makign the arguement passer as parent directory
from common.simple_arg_parse import arg_or_default
arch_str = arg_or_default("--arch", default="32,16")
#if the made arguenment is empty then it will take the default value
if arch_str == "":
arch = []
else:#Else it will take the arguement value
arch = [int(layer_width) for layer_width in arch_str.split(",")]
#Prinitng the architecture
print("Architecture is: %s" % str(arch))
#Making the Initial training session as none
training_sess = None
#Making the feed forward policy
class MyMlpPolicy(FeedForwardPolicy):
#defining the constructor function with parameters (Session, Object space, Action space , Number of environments Number of steps, number of batches , making the reuse as false so that new model can be trained)
def __init__(self, sess, ob_space, ac_space, n_env, n_steps, n_batch, reuse=False, **_kwargs):
super(MyMlpPolicy, self).__init__(sess, ob_space, ac_space, n_env, n_steps, n_batch, reuse, net_arch=[{"pi":arch, "vf":arch}],
feature_extraction="mlp", **_kwargs)
#Now making the training session as global variable
global training_sess
#making the training session as the current session
training_sess = sess
#making the environment of a specified type
env = gym.make('PccNs-v0')
#defining a gamma value for the rate of training session
gamma = arg_or_default("--gamma", default=0.99)
print("gamma = %f" % gamma)
#making the model
model = PPO1(MyMlpPolicy, env, verbose=1, schedule='constant', timesteps_per_actorbatch=8192, optim_batchsize=2048, gamma=gamma)
for i in range(0, 6):
with model.graph.as_default():
#saving the trained model for further use
saver = tf.train.Saver()
saver.save(training_sess, "./pcc_model_%d.ckpt" % i)
#making the model learn in 1600 seconds for 410 epochs
model.learn(total_timesteps=(1600 * 410))
#Now making a deafult directory for exporting the saved model
default_export_dir = "/tmp/pcc_saved_models/model_A/"
#again making the qrguement passer for storing in specific directory or making it save in deafult directory
export_dir = arg_or_default("--model-dir", default=default_export_dir)
#Taking the model as default model
with model.graph.as_default():
pol = model.policy_pi#act_model
obs_ph = pol.obs_ph
#making the action in the action space
act = pol.deterministic_action
#taking out any sample action from the action space
sampled_act = pol.action
# Session, Object space, Action space , Number of environments Number of steps, number of batches , making the reuse as false so that new model can be trained)
obs_input = tf.saved_model.utils.build_tensor_info(obs_ph)
#build_tensor_info
outputs_tensor_info = tf.saved_model.utils.build_tensor_info(act)
stochastic_act_tensor_info = tf.saved_model.utils.build_tensor_info(sampled_act)
#A SignatureDef protocol buffer constructed based on the supplied arguments.
signature = tf.saved_model.signature_def_utils.build_signature_def(
inputs={"ob":obs_input},
outputs={"act":outputs_tensor_info, "stochastic_act":stochastic_act_tensor_info},
method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME)
#"""
signature_map = {tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
signature}
#Building the SavedModel protocol buffer and saving variables and assets.
model_builder = tf.saved_model.builder.SavedModelBuilder(export_dir)
#adding the sessions and tages and the signature map to the model builder
model_builder.add_meta_graph_and_variables(model.sess,
tags=[tf.saved_model.tag_constants.SERVING],
signature_def_map=signature_map,
clear_devices=True)
#now saving the model builder in form of text.
model_builder.save(as_text=True) |