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Read this tutorial comfortably offline. Click here to purchase the complete Ebook 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
Download the complete Matlab code of this tutorial here% 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; % counterfor 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(q1q)))<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 builtin 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.
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Preferable reference for this tutorial is
Teknomo, Kardi. 2005. QLearning by Examples. http://people.revoledu.com/kardi/tutorial/ReinforcementLearning/index.html