I have seen your script "environment.py" in the below discussion which gives a rough baseline for evaluating the trained policy in the real world setup. I would like to ask whether there is a way to extend this script so that the trained policy can be retrained in the real world setup in order to minimize the existing sim2real gap.
Originally posted by **AntonBock** May 2, 2022
Hello,
We have trained a policy that we would like to test on a real-world setup. Does SKRL have any built-in support for this, or do you have any recommended method of doing this?
-Anton
Hello,
I have seen your script "environment.py" in the below discussion which gives a rough baseline for evaluating the trained policy in the real world setup. I would like to ask whether there is a way to extend this script so that the trained policy can be retrained in the real world setup in order to minimize the existing sim2real gap.
Discussed in https://github.com/Toni-SM/skrl/discussions/10