DROO optimization.py
2024-05-29 01:33:21
为方便自己看代码所以放上来，看完就删。完整代码在文章作者的仓库：https://github.com/revenol/DROO。
这个是论文《Deep Reinforcement Learning for Online Offloading in Wireless Powered Mobile-Edge Computing Networks》的tf1.x版本代码。
# -*- coding: utf-8 -*-
"""
Created on Tue Jan 9 10:45:26 2018@author: Administrator
"""
import numpy as np
from scipy import optimize
from scipy.special import lambertw
import scipy.io as sio # import scipy.io for .mat file I/
import timedef plot_gain( gain_his):import matplotlib.pyplot as pltimport pandas as pdimport matplotlib as mplgain_array = np.asarray(gain_his)df = pd.DataFrame(gain_his)mpl.style.use('seaborn')fig, ax = plt.subplots(figsize=(15,8))rolling_intv = 20plt.plot(np.arange(len(gain_array))+1, df.rolling(rolling_intv, min_periods=1).mean(), 'b')plt.fill_between(np.arange(len(gain_array))+1, df.rolling(rolling_intv, min_periods=1).min()[0], df.rolling(rolling_intv, min_periods=1).max()[0], color = 'b', alpha = 0.2)plt.ylabel('Gain ratio')plt.xlabel('learning steps')plt.show()def bisection(h, M, weights=[]):# the bisection algorithm proposed by Suzhi BI# average time to find the optimal: 0.012535839796066284 s# parameters and equationso=100p=3u=0.7eta1=((u*p)**(1.0/3))/oki=10**-26 eta2=u*p/10**-10B=2*10**6Vu=1.1epsilon=B/(Vu*np.log(2))x = [] # a =x[0], and tau_j = a[1:]M0=np.where(M==0)[0]M1=np.where(M==1)[0]hi=np.array([h[i] for i in M0])hj=np.array([h[i] for i in M1])if len(weights) == 0:# default weights [1, 1.5, 1, 1.5, 1, 1.5, ...]weights = [1.5 if i%2==1 else 1 for i in range(len(M))]wi=np.array([weights[M0[i]] for i in range(len(M0))])wj=np.array([weights[M1[i]] for i in range(len(M1))])def sum_rate(x):sum1=sum(wi*eta1*(hi/ki)**(1.0/3)*x[0]**(1.0/3))sum2=0for i in range(len(M1)):sum2+=wj[i]*epsilon*x[i+1]*np.log(1+eta2*hj[i]**2*x[0]/x[i+1])return sum1+sum2def phi(v, j):return 1/(-1-1/(lambertw(-1/(np.exp( 1 + v/wj[j]/epsilon))).real))def p1(v):p1 = 0for j in range(len(M1)):p1 += hj[j]**2 * phi(v, j)return 1/(1 + p1 * eta2)def Q(v):sum1 = sum(wi*eta1*(hi/ki)**(1.0/3))*p1(v)**(-2/3)/3sum2 = 0for j in range(len(M1)):sum2 += wj[j]*hj[j]**2/(1 + 1/phi(v,j))return sum1 + sum2*epsilon*eta2 - vdef tau(v, j):return eta2*hj[j]**2*p1(v)*phi(v,j)# bisection starts heredelta = 0.005UB = 999999999LB = 0while UB - LB > delta:v = (float(UB) + LB)/2if Q(v) > 0:LB = velse:UB = vx.append(p1(v))for j in range(len(M1)):x.append(tau(v, j))return sum_rate(x), x[0], x[1:]def cd_method(h):N = len(h)M0 = np.random.randint(2,size = N)gain0,a,Tj= bisection(h,M0)g_list = []M_list = []while True:for j in range(0,N):M = np.copy(M0)M[j] = (M[j]+1)%2gain,a,Tj= bisection(h,M)g_list.append(gain)M_list.append(M)g_max = max(g_list)if g_max > gain0:gain0 = g_maxM0 = M_list[g_list.index(g_max)]else:breakreturn gain0, M0if __name__ == "__main__":h=np.array([6.06020304235508*10**-6,1.10331933767028*10**-5,1.00213540309998*10**-7,1.21610610942759*10**-6,1.96138838395145*10**-6,1.71456339592966*10**-6,5.24563569673585*10**-6,5.89530717142197*10**-7,4.07769429231962*10**-6,2.88333185798682*10**-6])M=np.array([1,0,0,0,1,0,0,0,0,0])
# h=np.array([1.00213540309998*10**-7,1.10331933767028*10**-5,6.06020304235508*10**-6,1.21610610942759*10**-6,1.96138838395145*10**-6,1.71456339592966*10**-6,5.24563569673585*10**-6,5.89530717142197*10**-7,4.07769429231962*10**-6,2.88333185798682*10**-6])
# M=np.array([0,0,1,0,1,0,0,0,0,0])# h = np.array([4.6368924987170947*10**-7, 1.3479411763648968*10**-7, 7.174945246007612*10**-6, 2.5590719803595445*10**-7, 3.3189928740379023*10**-6, 1.2109071327755575*10**-5, 2.394278475886022*10**-6, 2.179121774067472*10**-6, 5.5213902658478367*10**-8, 2.168778154948169*10**-7, 2.053227965874453*10**-6, 7.002952297466865*10**-8, 7.594077851181444*10**-8, 7.904048961975136*10**-7, 8.867218892023474*10**-7, 5.886007653360979*10**-6, 2.3470565740563855*10**-6, 1.387049627074303*10**-7, 3.359475870531776*10**-7, 2.633733784949562*10**-7, 2.189895264149453*10**-6, 1.129177795302099*10**-5, 1.1760290137191366*10**-6, 1.6588656719735275*10**-7, 1.383637788476638*10**-6, 1.4485928387351664*10**-6, 1.4262265958416598*10**-6, 1.1779725004265418*10**-6, 7.738218993031842*10**-7, 4.763534225174186*10**-6])
# M =np.array( [0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1,])# time the average speed of bisection algorithm
# repeat = 1
# M =np.random.randint(2, size=(repeat,len(h)))
# start_time=time.time()
# for i in range(repeat):
# gain,a,Tj= bisection(h,M[i,:])
# total_time=time.time()-start_time
# print('time_cost:%s'%(total_time/repeat))gain,a,Tj= bisection(h,M)print('y:%s'%gain)print('a:%s'%a)print('Tj:%s'%Tj)# test CD method. Given h, generate the max modegain0, M0 = cd_method(h)print('max y:%s'%gain0)print(M0)# test all dataK = [10, 20, 30] # number of usersN = 1000 # number of channelfor k in K:# Load datachannel = sio.loadmat('./data/data_%d' %int(k))['input_h']gain = sio.loadmat('./data/data_%d' %int(k))['output_obj']start_time=time.time()gain_his = []gain_his_ratio = []mode_his = []for i in range(N):if i % (N//10) == 0:print("%0.1f"%(i/N))i_idx = i h = channel[i_idx,:]# the CD methodgain0, M0 = cd_method(h)# memorize the largest rewardgain_his.append(gain0)gain_his_ratio.append(gain_his[-1] / gain[i_idx][0])mode_his.append(M0)total_time=time.time()-start_timeprint('time_cost:%s'%total_time)print('average time per channel:%s'%(total_time/N))plot_gain(gain_his_ratio)print("gain/max ratio: ", sum(gain_his_ratio)/N)DROO optimization.py相关推荐
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