I know Jeremy isn’t interested in reinforcement learning because “its not practical and doesn’t apply to real world problems.” Well… I beg to differ! For example: AI for wireless spectrum allocation and for autonomous vehicles of all kinds. In fact, any problem that doesn’t have a predefined best outcome is ripe for reinforcement learning. I don’t know what kind of transfer learning we can apply (yet) but I suspect that if we dig in a bit we’ll find something. Perhaps.
I see there is at least one group of fastai users/alumni who are studying reinforcement learning together from Sutton and Barto’s new online graduate-level “introductory” textbook, but I’m wondering if anyone is, or is interested in, creating a fastrl extension to fastai in the excellent best-practices style that Jeremy and Rachel have pioneered? I found one fastrl project on github but it doesn’t seem to be related to fastai at all.
If there is sufficient interest (or maybe even if it’s just me) I’m up for forking fastai as fastrl on github and defining the project goals (for discussion), and keeping it synced as fastai is improved.