Step function in BasicGridWorld Environment

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Ankita Tondwalkar 2022-4-26

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1705730-step-function-in-basicgridworld-environment

编辑： Simar 2023-10-5

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

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Ankita Tondwalkar 2022-4-26

编辑：Ankita Tondwalkar 2022-4-26

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

Ankita Tondwalkar 2022-4-28

The link below explains in detail.

https://www.mathworks.com/help/reinforcement-learning/ug/create-custom-matlab-environment-from-template.html

Thanks,

Ankita

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Answer 1

Simar 2023-10-4

0
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此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1705730-step-function-in-basicgridworld-environment#answer_1324888

编辑：Simar 2023-10-5

在 MATLAB Online 中打开

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!