[KMnO4]

Yes. GPUs are extremely good at doing millions of small repetitive calculations, and not great elsewhere.

My capstone project used RL on a Raspberry Pi to train hardware-in-the-loop (essentially when to open and close valves based on sensor input). It was incredibly slow because it couldn’t be parallelized (without buying additional hardware for $500 each). Lots of professors asked why a Raspberry Pi was chosen when we had high end GPUs in the lab, and I had to explain that the Pi was NOT the bottleneck, and in fact stayed idle 95% of the time.

[seg_lol]

Would virtual physical simulation have helped in this case? What was the phase lag between valve actuation and the measured signal?

Create a metalearner than can do one-shot learning when it gets access to physical hardware?