import numpy as np import tensorflow as tf import math import cPickle import graph import RLAgent import httpConec as hC import matplotlib.pyplot as plt import geneTopo topo, hosts, nodes, links = geneTopo.getDCtopo() ag = RLAgent.PGNAgent(links, nodes * 2 + 1) atest = graph.graf(nodes, links, initopo=topo, inihost=hosts) atest.initial() #atest.printTopo() batch_size = 16 batch_number = 0 total_episodes = 640 episode_number = 0 valid_action = 0 invalid_action = 0 xs, ys, rs = [], [], [] epslon = 0.9 plt_x = range(total_episodes / batch_size) plt_y = [] r_batch = [] valid_action_combo = 0 gradBuffer = ag.sess.run(ag.tvars)
import numpy as np import tensorflow as tf import config import csv import graph import RLAgent import httpConec2 as hC import matplotlib.pyplot as plt from matplotlib.pyplot import draw topo,hosts,nodes,hostnum,links=config.topo print "host num:",hostnum print "nodes num:",nodes ag=RLAgent.PGNAgent(links,(nodes-hostnum)*2+1) atest=graph.graf(nodes,links,initopo=topo,inihost=hosts) atest.initial() atest.printTopo() stepnum=2000 batch_size=config.batchsize batch_sum=0 episode_number=0 valid_action=0 total_episodes=config.episodes xs,ys,rs=[],[],[] epslon=config.explore_rate plt_ANPB=[] plt_ENPB=[] plt_RNPB=[] plt_rb=[] overlink_record=[] rsp=hC.sendTopo(atest.E,atest.host)