I have done the tutorial. Now I'm trying to extend this example.
This is my model：
This is my SetupTime Table：
If I want to increase the model given in the tutorial from one production line to three, should I change the Observation Space and Action Space in Reinforcement Learning to MultiDiscrete, and set Decision Events to three?
Like this:
This is my model file：
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Since all three processors operate in the same way and are thus equivalent for the Reinforcement Learning agent, you shouldn't need to train a new agent if you already have trained one for one processor.
The easiest way to use more processors would probably be to copy the Reinforcement Learning Tool and only adjust which event triggers them, whose "lastitemtype" value is written to the parameter in the On Observation code and which reward value is returned (if they are different).
If you come up with a good way to know which processor triggered the observation (possibly keep track of who will finish (and thus pull) next), you could also do this with multiple events in a single window.
So what I should do is to use three Reinforcement Learning Tools and a Parameter for each Observation and Action？
Like this：
Thank you for your answer, sorry to trouble you again
If there is a difference (for example the items are not distributed equally or the processors use different tables for their setup times) then you should add a second observation parameter that provides the RL algorithm with the information which processor it is currently making a decision for.
But when I use this way to do my training, the result will be like this：
"ep_len_mean"and "ep_rew_mean" are all smaller than before training. I would like to know which part is wrong.
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