Jeremy has taught us a lot of different ways to use deep learning to make world-class results in different kind of tasks. I assume that some of the students have heard about reinforcement learning and maybe some of the students even know more about it. I know that Fast.ai library isn’t supporting any reinforcement learning techniques but I still think it is good to have a discussion about it to fully understand the state of machine learning. I also think it is something many current students should study after these lessons if they are more interested in AI.
- Can we even compare supervised learning and reinforcement learning? Or are those like supervised learning and unsupervised learning which doesn’t even solve the same kind of problems.
- Currently, RL is making state of the art results in many areas. People get slightly better results in areas where supervised learning models have been dominating for ages. Is this sign of RL starting to dominate these areas or just RL used to fine tone hyper-parameters? Also, in lesson 4 Jeremy said that he used random tree forest to find the best learning rates but could we get better results with RL?
- RL is great for games. There are also some other areas where there are specific state, action, and reward. In these areas, RL is producing massively better results. Is it plausible that supervised learning could someday be a better approach for these kind of problems?
- Then there is also problems where RL doesn’t do as well as supervised learning or at least it is producing the same kind of results. Is RL something which we will be using same way we use nowadays deep learning almost everytime instead of SVG or other outdated things? So is it plausible that the whole supervised learning area could someday become outdated?
I don’t assume that we should stop watching these amazing videos just because in future there might be something better but I’m rather trying to understand is RL something worth to learn after this course ends.