- https://www.mathworks.com/videos/reinforcement-learning-for-field-oriented-control-of-a-permanent-magnet-synchronous-motor-1587727861081.html
- https://www.mathworks.com/videos/getting-started-with-reinforcement-learning-1643977291095.html
- https://www.mathworks.com/videos/reinforcement-learning-part-2-understanding-the-environment-and-rewards-1551976590603.html
- https://www.mathworks.com/videos/practical-reinforcement-learning-for-controls-design-test-and-deployment-1657031639527.html
Reinforcement learning with action updated once every few (say 100) time steps
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Hello,
I am trying to learn a controller in Simulink environment. I am tryng to use reinforcement learning where the action determined by the agent is updated once every few time steps, i.e., an action once determined by the agent is used for by Simulink to run the simulation for a few time steps before it is updated again. Please provide me with suggestions on this. Thank you.
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Answers (1)
Shivani
on 17 Jun 2024
Edited: Shivani
on 17 Jun 2024
Hello @Suyash Agrawal
The workflow you wish to implement is discussed in detail in the folowing MATLAB documentation link: https://www.mathworks.com/help/reinforcement-learning/ug/reinforcement-learning-environments.html#:~:text=The%20agent%20and%20the%20environment%20interact%20at%20each%20of%20a%20sequence%20of%20discrete%20time%20steps%3A
The following MATLAB answer thread discusses a similar problem statement and may provide relevant information on how to address this issue:
Additionally, there are a number of tutorial videos that may provide more details on this implementation:
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