topic Re: Error training with flexsim_training.py in FlexSim Forum

Error training with flexsim_training.py

lorenzo_pio_g — Fri, 14 Oct 2022 18:01:48 GMT

[ FlexSim 22.2.2 ]

Hi. I have developed a model in flexsim in which I need to optimize the idle rate of a processor by applying RL to a robot. It has to pick up the boxes from the right queue and decide on which combiner to leave it, based on the number of boxes present on the pallet placed on the top combiner (Combiner1). However, the moment I go to train the agent through the code in flexsim_training.py, this writing remains perpetually and the agent is not trained.

progettoTesi.fsm

Re: Error training with flexsim_training.py

Jacob_White — Thu, 20 Oct 2022 18:57:26 GMT

@Lorenzo_pio G,

Are you able to run the flexsim_env.py code? It seems like visual studio is unable to connect to FlexSim.

Re: Error training with flexsim_training.py

lorenzo_pio_g — Fri, 21 Oct 2022 09:03:30 GMT

The connection to FlexSim is made correctly. In fact, FlexSim opens the moment I run the code. I also tested the flexsim_env.py file on the fsm ChangeoverTime file, which I implemented to follow the RL tutorial, and everything works correctly. There must be an error in initializing the RL parameters in my project, but I can't figure out what it is.

Re: Error training with flexsim_training.py

Jeanette_Fullmer — Tue, 25 Oct 2022 22:39:53 GMT

Can you post your model? It looks like you may have tried to but there is not an attachment.

Re: Error training with flexsim_training.py

Jacob_White — Wed, 26 Oct 2022 18:53:16 GMT

Hi @Lorenzo_pio G,

It looks like the reason your model wasn't able to train is because the reinforcement learning was never able to make a decision with the "On request action" activity. If you run your model you will see that the value in your Action parameter table will never change.

The reason behind this is that reinforcement learning decision events cannot be linked to object flows. You can however use the custom code activity to act as request decision itself. This option can be found by selecting "Control," Reinforcement learning," and then request decision. Select the reinforcement learning tool that you will be using, and then whenever a token enters that activity it will drive the decision making process in the reinforcement learning.

Once that is fixed, your model should be ready to start training.

RL_JW.fsm