How to improve practical RL
Hello!
We've recently finished the first large iteration of practical_RL course. It would be fair to say that you contributed least just as much work as we did: extensive feedback, contributed bugfixes, recipes for running in the cloud/via docker, compatibility patches, tensorflow versions of the assignments - together we accomplished much more than "official" course staff could ever have done.
And yet there's still much to improve. It is very likely (unless majority votes against that below) the course will be repeated next year with newblood students. We need your opinion on how to improve course for them.
TL;DR please give us feedback to make the course more productive for the next generation of YSDA&HSE students. Your feedback is especially important if you found something unsatisfactory.
Note: the survey is to make the course better, not to make us feel good. In case of doubt, please choose the worse option (idk if "good" or "okay" -> "okay", "okay" or "bad" -> "bad").
Note: please keep your reply anonymous.