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I want to use this q-learning (reinforcement learning) code. It seems like the code is correct, but I am getting errors and I don't know why:
function q=ReinforcementLearning
clc;
format short;
format compact;
int state=0;
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=0.8;
q=zero(size(R));
q1=one(size(R))*inf;
count=0;
for episode = 0:20000;
y=randperm(size(R,1));
state=y(1);
x=find(R(state,:)>=0);
if size(x,1)>0,
x1=RandomPermutation(x);
x1=x1(1);
end
qMax=max(q,[],2);
q(state,x1)=R(state,x1)+ gamma* qMax(x1);
int state=x1;
if sum(sum(abs(q1-q)))<0.0001 && sum(sum(q>0))
if count > 1000;
break
else
count=count+1;
end
else
q1=q;
count=0;
end
end
But I am getting the following warning and error:
enter code here
Warning: The method char/int will be removed in a
future relase. Use sym/int instead. For example
int(sym('x^2')).
> In char.int at 10
In ReinforcementLearning at 6
Error using mupadmex
Error in MuPAD command: Invalid integrand. [int]
Error in sym/int (line 107)
rSym =
mupadmex('symobj::intindef',f.s,x.s,options);
Error in char/int (line 12)
y = int(sym(f),varargin{:});
Error in ReinforcementLearning (line 6)
int state=0;
This code can be found in the following link: http://people.revoledu.com/kardi/tutorial/ReinforcementLearning/Q-Learning-Matlab.htm
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Hence, we can say that "Reinforcement learning is a type of machine learning method where an intelligent agent (computer program) interacts with the environment and learns to act within that." How a Robotic dog learns the movement of his arms is an example of Reinforcement learning.
Thus, reinforcement learning is particularly well-suited to problems that include a long-term versus short-term reward trade-off. It has been applied successfully to various problems, including robot control, elevator scheduling, telecommunications, backgammon, checkers and Go (AlphaGo).
Reinforcement Learning (RL) refers to a kind of Machine Learning method in which the agent receives a delayed reward in the next time step to evaluate its previous action. It was mostly used in games (e.g. Atari, Mario), with performance on par with or even exceeding humans.
Two types of reinforcement learning are 1) Positive 2) Negative. Two widely used learning model are 1) Markov Decision Process 2) Q learning. Reinforcement Learning method works on interacting with the environment, whereas the supervised learning method works on given sample data or example.
There is no
int state=0;
int state=x1;
in matlab. That is C style. In matlab int is a built-in function meaning something else. Besides, it should be
q=zeros(size(R));
q1=ones(size(R))*inf;
Remember to download his RandomPermutation function, otherwise just use randperm instead.
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