Reinforcement learning stuck on the cluster

I'm working on a reinforcement learning project with a complex environment that requires a significant amount of time to complete, which has led to a slow learning process. When running the code on my local machine using the parallel computing toolbox with three cores, it works perfectly fine. However, when I attempted to utilize my university's cluster to expedite the training process, I encountered issues. With a low number of workers (specifically, three cores), the training process gets stuck, as depicted in the first image below. On the other hand, when using a higher number of workers (e.g., 10 cores or more), the training process fails to initiate altogether, as shown in the second image below. I'm uncertain whether this problem stems from the cluster's configuration, a Matlab-related issue, or a limitation within the reinforcement learning toolbox. Any insights on potential resolutions would be greatly appreciated.

[Emmanouil Tzorakoleftherakis]

Can you please create a technical support case? This may be hard to debug otherwise

[Ahmad Momani]

I just submitted one

[Mike Croucher]

Did you get this resolved?