Reinforcement Learning toolbox step function

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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)

Accepted Answer

Stephan
Stephan on 7 Sep 2020
Edited: Stephan on 7 Sep 2020
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:
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.
  3 Comments
Kamalova Albina
Kamalova Albina on 21 Feb 2022
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

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