卫星网络q学习matlab仿真.zip

clear clc states=8; %初始数量 actions=8; %动作数量 final_state=8; %目标状态 episode=100; %迭代次数 gamma=1; %折扣系数 alpha=0.8; %学习速率 p_max=zeros(states,actions);%按Q值表选最大值进行动作的概率 T=5; %退火温度 reward=-1*[inf inf 47 inf inf 24 inf inf; inf inf 55 31 31 inf 74 79; 47 55 inf 88 23 25 66 inf; inf 31 88 inf inf 120 inf 29; inf 31 23 inf inf inf 42 inf; 24 inf 25 120 inf inf inf inf; inf 74 66 inf 42 inf inf 66; inf 79 inf 29 inf inf 66 inf]; tic Q_table=zeros(states,states); for i=1:episode current_state=randperm(states,1); %随机指定一个起始节点 while current_state~=final_state; %当前节点是否为目标节点 optinal_action=find(reward(current_state,:)>=-1000); %当前状态的可能动作集 if length(optinal_action)==0 %判断当前状态的可能动作集是否为空 print("not connected") break else next_action=choose_next_action(Q_table,current_state,optinal_action,p_max); %根据退火算法选择当前状态的一个动作 next_state_optinal=find(reward(next_action,:)>=-1000); maxQ=max(Q_table(next_action,next_state_optinal)); Q_current=Q_table(current_state,next_action); Q_table(current_state,next_action)=(1-alpha)*Q_table(current_state,next_action)+alpha*(reward(current_state,next_action)+gamma*maxQ); Q_next=Q_table(current_state,next_action); p_max(current_state,next_action)=pmax(Q_current,Q_next,T); current_state=next_action; end end if i==episode/2 reward=-1*[inf inf 47 inf inf 24 inf inf; inf inf 55 31 31 inf 74 79; 47 55 inf 88 23 25 66 inf; inf 31 88 inf inf 120 inf 59; inf 31 23 inf inf inf 42 inf; 24 inf 25 120 inf inf inf inf; inf 74 66 inf 42 inf inf 66; inf 79 inf 59 inf inf 66 inf]; Q_table end end Q_table toc function [next_action]=choose_next_action(Q_table,current_state,optinal_action,p_max) [max_Q,max_action]=max(Q_table(current_state,optinal_action)); if rand<=p_max(current_state,optinal_action(max_action)) next_action=optinal_action(randperm(length(optinal_action),1)); else next_action=optinal_action(max_action); end end function [p_max]=pmax(Q_current,Q_next,T) p_max=exp(-abs((Q_current-Q_next)/T/Q_next)); end