Ideally, yes, we want to pre-train in a virtual environment using as close to the real model robot as possible. I worked on such a problem as part of my PhD research on mobile robots using the Webots simulator (https://www.cyberbotics.com/overview) as my virtual environment.

In my case, I was working on biologically-inspired models for picking up distant objects. It's impractical to tune hyperparameters in hardware, so you need to be able to create a virtual version that gets you close enough. Once you can demonstrate success there, you then have to move to the physical robot, which introduces several additional challenges: 1) imperfections in your actual hardware behavior vs idealized simulated ones, 2) real-world sensor noise and constraints, 3) dealing with real-world timing and inputs instead of a clean, lock-step simulated environment, 4) having different API to poll sensors/actuate servos between virtual and hardware robots, and 5) ensuring that your trained model can be transferred effectively between your virtual and hardware robot control system.

I was able to solve these issues for my particular constrained research use case, and was pretty happy with the results. You can see a demo reel of the robot here: https://www.youtube.com/watch?v=EoIXFKVGaXw