matlab手动实现基于DQNCartPole问题不用强化学习工具箱的DQN算法案例与matlab代码（二）

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概述

不用强化学习工具箱的DQN算法案例与matlab代码（二）

DQN问题

1.首先DQN与Q-learning算法区别：
MATLAB手动实现DQN最短路径问题
2.：为何为Q网络引入带有标签的数据
DQN问题解答比较好的网址，大家可以点进去好好看看
在这里插入图片描述

cartpole问题

环境设置：CartPole问题描述见链接。参数设置见代码。
奖励设置： 每一步给出的奖励为0，当满足norm(state(3),state(4))<0.01时，奖励为10；当杆子与竖直方向角度大于10或者移动范围大于10，奖励为-10；
状态设置： 随机初始化状态为 [-10*(1-2rand) -1.5(1-2rand) 0.17rand 0.1*rand]；rand表示（0,1）之间的数。
动作设置： 0表示小车向左移动，1表示小车向右移动。

达到下列条件之一片段结束:
：杆子与竖直方向角度超过10度
：小车位置距离中心位置超过10
：片段长度超过最大设定值

DQN部分代码分析

首先用fitnet函数搭建神经网络并初始化参数

  layer1Size = 10;
  layer2Size = 20;            
  obj.net = fitnet([layer1Size layer2Size],'trainlm');
  obj.net_prev = obj.net;
  obj.net.trainParam.lr = 0.1;
  obj.net.trainParam.epochs = 10;
  obj.net.trainParam.showWindow = true;
  obj.net.trainParam.lr_dec = 0.8;
  obj.net = train(obj.net,rand(length(obj.state),25),rand(length(obj.actions),25));
  obj.netWeights = getwb(obj.net);

开始训练

 function QLearningTrain(obj)            
    for epochs = 1:obj.maxEpoch                
         %Reset the parameters
         obj.totalReward = 0;
         obj.bonus = 0;
         % Epsilon Decay                
         obj.epsilon = obj.epsilon*obj.epsilonDecay;                           
         obj.randomInitState();见代码段环境中
%        obj.state
      for itr_no = 1:obj.maxIttr                    
         cartForce = obj.selectAction();                   
         [state,Action,reward,next_state,done] = obj.doAction(cartForce); % Propogate the Plant 见代码段环境中  
        obj.addtoReplaybuffer(state,Action,reward,next_state,done);                  
        obj.state = next_state;
        %Aggregating the Total Reward for every Epoch
        obj.totalReward = obj.totalReward + reward;       
        if obj.resetCode
             break;
        end
                    
     end
                
      % Store the Average Reward and Epoch Length for plotting
      % the network Performance
      obj.epochAvgReward(epochs) = obj.totalReward/itr_no;
      obj.epochLength(epochs) = itr_no;                
      if itr_no == obj.maxIttr                    
         disp(['Episode',num2str(epochs),':Successful Balance Achieved!- Average Reward:', num2str(obj.epochAvgReward(epochs)),'steps:', num2str(obj.epochLength(epochs))]);                    
      elseif obj.resetCode == true                    
         disp(['Episode',num2str(epochs),': Reset Condition reached!!!- Average Reward:', num2str(obj.epochAvgReward(epochs)),'steps:', num2str(obj.epochLength(epochs))]);
         obj.resetCode = false;                    
      end                
              
      obj.trainOnBuffer()               
   end
 end

选择动作，采用softmax策略

function selectedAction = selectAction(obj)            
  if rand <= obj.epsilon                
      actionIndex = randi(obj.actionCardinality,1);                
  else                
      obj.Q = obj.genQvalue(obj.state);                
      [~,actionIndex] = max(obj.Q);                
  end            
  selectedAction = obj.actions(actionIndex);
end

从Buffer中选择批次数据进行训练

 function trainOnBuffer(obj)            
    sampledrawfromBuffer = datasample(obj.replayBuffer,min(obj.sampleSize,length(obj.replayBuffer)));
    stateBatch = sampledrawfromBuffer(:,[1:4]);
    actionBatch = sampledrawfromBuffer(:,5);
    rewardBatch = sampledrawfromBuffer(:,6);
    nextstateBatch = sampledrawfromBuffer(:,[7:10]);
    doneBatch = sampledrawfromBuffer(:,11);
    valueBatch = zeros(length(obj.actions),1);            
    for count = 1:length(sampledrawfromBuffer)                
        value = obj.genQvalue(stateBatch(count,:));
        aIdx = find(~(obj.actions-actionBatch(count)));
        if doneBatch(count)
            value(aIdx) = rewardBatch(count);
        else
            value(aIdx) = rewardBatch(count) + obj.gamma*max(obj.genQvalue(nextstateBatch(count,:)));
        end
        valueBatch(:,count) = value;
    end            
    obj.net = setwb(obj.net,obj.netWeights);
    obj.net = train(obj.net, stateBatch',valueBatch);
    obj.netWeights = getwb(obj.net);           
end