Reinforcement Learning toolbox step function

Greetings everyone, I hope you're having a good time. In reinforcement learning toolbox there's a functin named "step(env, Action)", I wanted to know what is the role of the input "Action" in this function?

[Observation, Reward, IsDone, LoggedSignals] = step(env, Action)

Answer 1

[Stephan]

The action the agent has choosen in the last step, usually has an impact on the environment. To let the step function know what action was choosen the step before, you have to refer the last action to the next call of the step function, which then - based on this informations calculates the next observation, the reward and the iSDone flag.

See this example:

https://de.mathworks.com/help/reinforcement-learning/ug/create-custom-reinforcement-learning-environment-in-matlab.html

In the example given in the link above the action is a directed force that is applied to the system in the following step to calculate the new observations from the current step.

Building on that the step function can calculate the reward and if the IsDone value is true. Using these informations the agent gets a new information from the environment, which is the basis for the choice of the next action.

[Mostafa Nazmi]

Thanks a lot for your explanation

Best Regards

[Maha Mosalam]

Hi, what about the xact role of IsDone flag it it shuld be true or false or what?

[Kamalova Albina]

IsDone flag means the episode is finished or not. It should have a condition logic. For example, let's say you are hungry and you decide to eat something. In step function, you are continuously eating while do the actions to choose fry potato or tomato (maybe). How to know you are done and full already?! IsDone is this flag for showing you should stop this eating episode