Comment by robrenaud
3 days ago
I just wouldn't.
RL is nice in that it is handles messy cases where you don't have per example labels.
How do you build a learned chess playing bot? Essentially the state of the art is to find a clever way of turning the problem of playing chess into a sequence of supervised learning problems.
So IIUC RL is applicable only when the outcome is not immediately available.
Let's say I do have a problem in that setting; say the chess problem, where I have a chess board with the positions of chess pieces and some features like turn number, my color, time left on the clock, etc. are available.
Would I train a DNN with these features? Are there some libraries where I can try out some toy problems?
I guess coming from a classical ML background I am quite clueless about RL but want to learn more. I tried reading the Sutton and Barto book, but got lost in the terminology. I'm a more hands-on person.
OpenAI has an excellent interactive course on Deep RL: https://spinningup.openai.com/en/latest/
The AlphaGo paper might be what you need. It requires some work to understand, but is clearly written. I read it when it came out and was confident enough to give a talk on it. (I don't have the slides any more; I did this when I was at a FAANG and left them behind.)