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Q-Learning Using Matlab

by Kardi Teknomo

Q-Learning e-book

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Read this tutorial comfortably off-line. Click here to purchase the complete E-book of this tutorial

I have made simple Matlab Code below for this tutorial example and you can modify it for your need. You can copy and paste the two functions into separate text files and run it as ReinforcementLearning.

To model the environment you need to make the instant reward matrix R. Put zero for any door that is not directly to the goal and put value 100 to the door that lead directly to the goal. For unconnected states, use minus Infinity (-Inf) so that it become very negative number. We want to maximize the Q values, thus very negative number will not be considered at all.

The state is numbered 1 to N (in our previous example N = 6). The result of the code is only normalized Q matrix.

You may experiment in the effect of parameter gamma to see how it influences the results.



%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% Q learning of single agent move in N rooms 

% Matlab Code companion of 

% Q Learning by Example, by Kardi Teknomo 

% (http://people.revoledu.com/kardi/)

%

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 

function q=ReinforcementLearning

clc;

format short

format compact
% Two input: R and gamma

% immediate reward matrix; 

% row and column = states; -Inf = no door between room

R=[-inf,-inf,-inf,-inf,   0, -inf;

   -inf,-inf,-inf,   0,-inf, 100;

   -inf,-inf,-inf,   0,-inf, -inf;

   -inf,   0,   0,-inf,   0, -inf;

      0,-inf,-inf,   0,-inf, 100;

   -inf,   0,-inf,-inf,   0, 100];



gamma=0.80;            % learning parameter

goalState=6;           
q=zeros(size(R));      % initialize Q as zero

q1=ones(size(R))*inf;  % initialize previous Q as big number

count=0;               % counter
for episode=0:50000

   % random initial state

   y=randperm(size(R,1));

   state=y(1);

   

   while state~=goalState,            % loop until reach goal state

        % select any action from this state

        x=find(R(state,:)>=0);        % find possible action of this state

        if size(x,1)>0,

           x1=RandomPermutation(x);   % randomize the possible action

           x1=x1(1);                  % select an action 

        end

        qMax=max(q,[],2);

        q(state,x1)= R(state,x1)+gamma*qMax(x1);   % get max of all actions 

        state=x1;

   end



   

   % break if convergence: small deviation on q for 1000 consecutive

   if sum(sum(abs(q1-q)))<0.0001 & sum(sum(q >0))

      if count>1000,

         episode        % report last episode

         break          % for

      else

         count=count+1; % set counter if deviation of q is small

      end

   else

      q1=q;

      count=0; % reset counter when deviation of q from previous q is large

   end

end 
%normalize q

g=max(max(q));

if g>0, 

   q=100*q/g;

end

  


The Matlab code above is using my own function RandomPermutation which return random permutation of matrix A. Unlike built-in Matlab function randperm(n) that give permutation of integer 1:n only, RandomPermutation rearrange member of matrix A randomly. This function is useful for MonteCarlo Simulation, Bootstrap sampling, game, etc.

Download the complete Matlab code of this tutorial here

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This tutorial is copyrighted.

Preferable reference for this tutorial is

Teknomo, Kardi. 2005. Q-Learning by Examples. http://people.revoledu.com/kardi/tutorial/ReinforcementLearning/index.html

 
© 2015 Kardi Teknomo. All Rights Reserved.