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Step function in BasicGridWorld Environment

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Ankita Tondwalkar
Ankita Tondwalkar am 26 Apr. 2022
Bearbeitet: Simar am 5 Okt. 2023
Hello all,
I am working on writing a custom step function for my reinforcement learning environment in MATLAB and I wanted to access the step function.m file written for the BasicGridWorld in MATLAB?
Could anyone tell me how can I access it?
Thanks,
Ankita
  2 Kommentare
Ankita Tondwalkar
Ankita Tondwalkar am 26 Apr. 2022
Bearbeitet: Ankita Tondwalkar am 26 Apr. 2022
I think the path to it is root/usr/local/MATLAB/MATLABversion/toolbox/rl/rl/+rl/+env/GridWorld.m
But I cannot find the step function in the GridWorld.m

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Antworten (1)

Simar
Simar am 4 Okt. 2023
Bearbeitet: Simar am 5 Okt. 2023
Hi Ankita,
I understand that you want to access the step function.m file written for the BasicGridWorld in MATLAB.
While going through the path, there is no specific step function file provided for the BasicGridWorld environment in the Reinforcement Learning Toolbox.
If you are working on creating a custom reinforcement learning environment and want to implement a step function, you will need to define it yourself based on the specific dynamics and rules of your environment. Here is an example skeleton of how created custom class environment would be like-
classdef CustomEnvironment < rl.env.MATLABEnvironment
properties
% Define your environment properties here
end
methods
function this = CustomEnvironment()
% Initialize your environment here
end
function observation = reset(this)
% Reset the environment to its initial state
% Return the initial observation
end
function [nextObservation, reward, isDone] = step(this, action)
% Update the environment state based on the action
% Return the next observation, reward, and termination information
end
end
end
The step function typically takes an action as input and updates the state of the environment and accordingly returning the next state, reward, and termination information. It might be helpful to refer to the documentation below and examples provided with Reinforcement Learning Toolbox
Hope it helps!

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