Use Reinforcement Learning instead of genetic algorithm for optimization I want to use RL instead of genetic or any other evolutionary algorithm in order to find the best parameter for a function.
Here is the problem:
Given a function $$f(x,y,z,data)$$
x,y and z are some integeres from 1 to 50. So I can say I have a 3-dimensional array which is a way to save fitness values:
$$parameters=[[1..50], [1..50], [1..50]]$$ The $$data$$ is another input which is the f needed to do some calculation on.
Currently, I am optimizing it using a genetic algorithm with $$Cost(fitness) = f(x,y,z,data)$$ which is customized cost function.
any value for x,y, and z will result in a cost for example:
$$f(1, 5, 8, X) = 15$$
$$
parameters: [1, 5, 8] = 15$$
or
$$ parameters: [2, 9, 11] = 30
$$
In the provided example 2, 9, and 11 is a better set of parameters.
So I run a genetic algorithm and make some children with a sequence of x,y, and z. Then I calculate the cost(fitness) and then select them and so on.
I want to know is there any alternative or method in reinforcement learning which I can use instead of a genetic algorithm? If yes, please provide the name or any helpful link.
Note that F is completely defined by the user and should be changed in other contexts.
Thanks.
In order to have anything resembling reinforcement learning you must at the very least have a set of states $S$ and a set of actions $A$.
In your formulation I can vaguely identify the set of states $S$ as all possible $(x,y,z)$ triplets. But don't see anything in your description that could be interpreted as a set of actions $A$. You either oversimplified the description of your problem or reinforcement learning is not applicable here by lack of very basic ingredients for it.