question

To train the model, you need to run configure the Reinforcement Learning tool. Then you need to run flexsim_training.py. When you run the model yourself, using the reset and run buttons, the model will fire the OnRequestAction trigger, whenever you request an action. But this does not train the AI. When you run flexsim_training.py, the Python script will launch the model and run it. In this case, when you request an action, the OnRequestAction trigger does not fire. Instead, FlexSim sends the observations to the RL algorithm, and gets an action in response. The RL algorithm uses FlexSim as the environment. Running flexsim_training.py is how you train the AI.

Once you have run flexsim_training.py, you'll see that it produces a .zip file. That .zip file contains the AI. You can use the trained brain by running flexsim_inference.py, which becomes a server. You can then configure your OnRequestAction to send the observations to that server, and get an action back.

https://docs.flexsim.com/en/22.1/ModelLogic/ReinforcementLearning/UsingATrainedModel/UsingATrainedModel.html

To increase the number of processors, you would need to make sure the LastItemType parameter matches the last item processed. For example, suppose Processor1 works on a Type 2 item, and Processor 2 works on a Type 5 item. Then, when Processor1 requests an action, the LastItemType value needs to be 2. And when Processor 2 requests an action, the LastItemType value needs to be 5. And yes, then you can listen to the three decision events.