How to set the reinforcement learning block in Simulink to output 9 actions

I am trying to tune 3 PID controllers in matlab/simulink via reinforcement learning thus the reinforcemt learning toolbox. I am trying to follow "Tune PI Controller using Reinforcement Learning" (https://www.mathworks.com/help/reinforcement-learning/ug/tune-pi-controller-using-td3.html) as best I can and extrapolate it to create three PID controllers controlled by a DDPG agent. i can't really figure out how to make it account for three PID controllers, hence nine gain values.

I can't really share much about the model since I am bound to a NDA by my university, but I feel as though my question is nonspecifc enough to both not get me in trouble, but also allow potential helpers to get what I am trying to do. Please let me know if more info is needed.

[轩]

Hello Aaron, I am trying the same method now. Could you please leave a contact information for your advice?

Answer 1

[Yikai]

the number of the actions is defined at actionInfo of your environment

Answer 2

[Emmanouil Tzorakoleftherakis]

Hello,

the example you are referring to does not output 3 values for the pid gains. The PID gains are "integrated" into the neural network architecture and the policy output is still the same as PID. If you want to follow the same setup, the output of the policy in your case would output 3 values and the neural network weights would match the weights of the PID controllers.